Expert System-Based Risk Zoning for Termite Infestation in Reservoirs

Expert System-Based Risk Zoning for Termite Infestation in Reservoirs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Expert System-Based Risk Zoning for Termite Infestation in Reservoirs Fangchan He, Wenkai Zhang, Liqing Li, Ke Bian, Shiwei LI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5008917/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Termite infestations are characterized by their concealment, recurrence, and long-term impact. Research on risk zoning of termite infestations in reservoirs is crucial for prioritizing termite control efforts and rationally allocating funds for treatment. Using termite-infested reservoirs in Nanyang City, Henan Province, as a case study, this research employed disaster risk theory and selected hazard degree indices for evaluation. The indices include surface activity characteristics, internal activity characteristics, and impacts on hydraulic structures. Additionally, hazard sensitivity indices were chosen, incorporating factors such as annual average temperature, annual precipitation, vegetation cover, topography, termite species, dam grade, management status, and treatment status. Based on these factors, an evaluation index system for termite infestation risk in reservoirs was established, along with models for hazard degree, sensitivity, and comprehensive evaluation. Utilizing ArcGIS for geographical processing and spatial analysis, a risk zoning map of termite infestation in the reservoirs of Nanyang City was generated. This map identified nine medium-sized reservoirs requiring urgent termite control, along with one large (Type I) reservoir and five medium-sized reservoirs that need enhanced inspections. Reservoir dams Termite infestation Evaluation models Risk zoning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Henan Province spans four major river basins: the Yangtze, Huaihe, Yellow, and Haihe Rivers. It lies within a transitional zone between the north subtropical and warm temperate climates. The extensive water systems, coupled with the warm and humid climate, create favorable conditions for rapid and prolific termite reproduction. In recent years, driven by global warming, termites have spread from the southern regions to the banks of the Yellow River (Bale JS et al. 2002). Reservoir dams and other hydraulic projects are often located in hilly and mountainous areas. The abundant water sources and dense forests surrounding these dams facilitate termite migration and nest-building. Termite nests function like "bombs" buried within embankments; rising reservoir water levels can trigger incidents such as seepage, piping, and sinkholes in earth-rock dams, significantly threatening the safe operation of reservoirs (Saghaee G et al. 2017). Consequently, the Ministry of Water Resources issued the "Guidelines for Termite Control in Hydraulic Engineering," which call for the establishment of a comprehensive termite control system, the improvement of funding mechanisms for termite control, and the acceleration of foundational research on grading standards for termite damage (Ministry of Water Resources. 2023). However, due to limited funding for termite control in hydraulic engineering and a shortage of technical personnel in Henan Province, it is currently impossible to completely eradicate termite infestations in reservoir dams all at once (Zhang WJ. 2023). Therefore, prioritizing termite control efforts and determining key inspection priorities are urgent technical challenges. Currently, research in disaster and risk sciences for hydraulic engineering is centered on developing risk evaluation systems and models, as well as on disaster and risk zoning (Amendola A. 2008). With the advancement of Geographic Information System (GIS) technology, regional risk zoning using GIS has become a major focus of research for water resource professionals(Zargar M et al. 2016; Terêncio DPS et al. 2020; Owusu PA et al. 2013). Tawatchai Tingsanchali et al (2010).combined GIS with risk assessment techniques to evaluate flood disasters and risks in the Yom River, with the goal of minimizing local economic and environmental losses. Raffaele De Risi et al (2020). conducted a flood risk assessment in Addis Ababa to provide technical support for urban strategic planning and decision-making. Chen Cihao et al (2023). employed GIS technology to predict the distribution of risk areas for Aedes albopictus invasion in Guangdong Province, providing a scientific basis for developing epidemic prevention strategies. Emre Ozsahin et al (2022). employed GIS to analyze the impact of climate and topography on the range and spatial distribution of termite habitats. Jacob Mayowa Owoyemi et al (2021). relied on GIS technology to map the probability of severe termite infestations on the FUTA campus, aiding in termite eradication efforts. However, due to the shortage of termite control technicians in hydraulic engineering, there is relatively little research on using GIS for risk evaluation and zoning of termite infestations in reservoir dams. Therefore, this study focused on Nanyang City, Henan Province, where termite infestations in hydraulic engineering are severe, to establish an evaluation index system and models for assessing termite infestation risks. Based on GIS technology, risk zoning was performed for termite infestations in reservoirs within Nanyang City. This allowed for the identification of reservoirs requiring focused treatment and key inspections according to their risk levels. The research results are expected to provide technical support and a theoretical foundation for the systematic control and targeted inspections of termite infestations in reservoirs across Henan Province. 2. Overview of the Study Area Nanyang City features a diverse landscape, with plains covering 21% of the area, hills 30.6%, and mountains 48.4%. It serves as a key water source and canal head for the middle route of the South-to-North Water Diversion Project. The city is crisscrossed numerous rivers that are part of the Yangtze, Huaihe, and Yellow River systems. Nanyang experiences a north subtropical monsoon-type continental climate, with annual precipitation ranging from 801 to 1,174 mm. More than 95% of the reservoir dams in Nanyang are earth-rock dams. The ample water sources, suitable soil for nesting, and high vegetation cover create an excellent habitat for termites. Existing data indicate that termite infestations have been identified in reservoirs across 11 out of 13 counties and districts in Nanyang. Of the 561 reservoirs, 335 have been infested by termites, representing 59.7%, with 76 of these reservoirs classified as severely infested (Lei CW et al. 2022). The specific distribution is illustrated in Fig. 1 . Given the challenging and urgent need to manage termite-infested reservoirs in Nanyang City, it is crucial to develop a risk evaluation system, evaluation models, and risk zoning for these reservoirs. This research aims to provide both technical support and theoretical foundations for the systematic control, targeted inspections, and termite management efforts of reservoir dams in Nanyang City. 3. Evaluation Index System for Termite Infestation in Reservoirs 3.1. Selection and Weighting of Evaluation Indices 3.1.1. Selection of Evaluation Indices The "Technical Guidelines for Termite Control in Hydraulic Engineering (Trial)" (Ministry of Water Resources. 2023) assess the severity of termite infestation in hydraulic structures based on surface activity characteristics, internal activity characteristics, and impacts on hydraulic structures. However, due to the concealed, recurrent, and long-term nature of termite infestations, there is a need to further refine the grading technology for termite damage by incorporating additional factors such as regional topography, dam grade, vegetation cover, and treatment status. Based on literature [15] and expert consultation, 11 evaluation indices were selected from two main aspects: factors related to the degree of termite infestation (termite infestation status in reservoirs) and factors related to termite infestation sensitivity factors (natural and environmental conditions). This selection led to the construction of an evaluation index system for termite infestation in reservoirs. Details are presented in Table 1 . Table 1 Evaluation index system for termite infestation in reservoirs Goal layer Comprehensive index of termite infestation Criterion layer Hazard degree indices Hazard sensitivity indices Alternative layer Surface activity characteristics Internal activity characteristics Impacts on hydraulic structures Annual average temperature Annual precipitation Topography Dam grade Vegetation cover Termite species Management status Treatment status 3.1.2. Weighting of Evaluation Indices Information was collected through questionnaires distributed to experts and technical personnel in termite control to obtain scores and rankings for each index. A total of 66 questionnaires were distributed, of which 3 were invalid, resulting in 63 valid responses. Referring to the structural entropy method employed by B. Srdjevic et al (2004). both subjective and objective weighting methods were combined to determine the weights of hazard degree indices, hazard sensitivity indices, and the comprehensive index for termite infestations in reservoirs. The weights for these three types of indices are presented in Tables 2 , 3 , and 4 , respectively. Table 2 Weights of hazard degree indices Evaluation index Surface activity characteristics Internal activity characteristics Impacts on hydraulic structures Weight coefficient 0.3199 0.3631 0.3171 Table 3 Weights of hazard sensitivity indices Evaluation index Annual average temperature Annual precipitation Topography Dam grade Vegetation cover Termite species Management status Treatment status Weight coefficient 0.1259 0.1140 0.1245 0.1267 0.1263 0.1255 0.1276 0.1277 Table 4 Weight of comprehensive index Evaluation index Hazard degree indices Hazard sensitivity indices Weight coefficient 0.4615 0.5385 3.2. Quantification and Classification of Evaluation Indices Based on the literature [15], hazard degree indices were classified into three risk levels. The classification of hazard sensitivity indices was quantified according to the relationship between index values and the risk level of termite infestation, using the natural breaks classification method in ArcGIS. 3.2.1. Quantification of Hazard Degree Indices The surface activity characteristics of hazard degree indices were classified based on the number of exposed features such as mud tubes and mud trails. Internal activity characteristics were classified based on the size of termite nests and the length of termite queens. Impacts on hydraulic structures were classified based on whether phenomena such as seepage and wet slopes occur in hydraulic engineering. Details are shown in Table 5 . Table 5 Values and risk levels of hazard degree indices Evaluation index Index value Level Score Surface activity characteristics Infested area 0–4 spots, Source area 3–14 spots 1 1 Infested area 5–9 spots, Source area 15 spots and above, Source area with flight holes 2 2 Infested area 10 spots and above, Infested area with flight holes 3 3 Internal activity characteristics Main nest diameter ≤ 250 mm or Queen length ≤ 30 mm, Nest in juvenile stage 1 1 250 mm < Main nest diameter ≤ 350 mm or 30 mm 350 mm or Queen length > 50 mm, More than 3 mature nests found, Through tunnels in main structure 3 3 Impacts on hydraulic structures Signs of termite activity, No seepage or wet slope damage 1 1 Seepage or wet slope damage due to termite activity 2 2 Leaks, sinkholes, or slope failure due to termite activity 3 3 3.2.2. Quantification of Hazard Sensitivity Indices Considering the natural environment and reservoir conditions in Nanyang City, annual average temperature, annual precipitation, vegetation cover, topography, termite species, dam grade, management status, and treatment status were selected as hazard sensitivity indices. The classification was quantified based on the relationship between index values and the risk level of termite infestation, using the natural breaks classification method in ArcGIS. Details are shown in Table 6 . Table 6 Values and risk levels of hazard sensitivity indices Evaluation index Index value Level Score Annual average temperature (Peng S 2019) / ℃ ≤ 14.17 1 1 14.17 ~ 16.74 2 2 ≥ 16.74 3 3 Annual precipitation (Peng S 2019) /mm ≤ 907.21 1 1 907.21 ~ 975.92 2 2 ≥ 975.92 3 3 Vegetation cover (Xu XL 2018) ≤ 60.07 % 1 1 60.07%~78.35 % 2 2 ≥ 78.35 % 3 3 Topography (Cheng W and Zhou C 2014; Li BY et al 2008) Plains and terraces 1 1 Hills 2 2 Mountain areas 3 3 Termite species (Wang ZG 1984) Areas with a single species and minor infestations or no termite infestations 1 1 Areas with multiple species and severe infestations 2 2 Areas with the most species and the most severe infestations 3 3 Dam grade Small (Type I) reservoirs and small (Type II) reservoirs 1 1 Medium-sized reservoirs 2 2 Large (Type I) reservoirs and large (Type II) reservoirs 3 3 Management status Reservoirs managed by water conservancy departments 1 1 Reservoirs managed by agricultural service centers 2 2 Reservoirs managed by non-water conservancy departments 3 3 Treatment status Termite infestations have been treated/no termite infestations 1 1 Termite infestations are being treated 2 2 Termite infestations have not been treated 3 3 3.3. Termite Infestation Evaluation Models 3.3.1. Hazard Degree Evaluation Model Based on Table 2 and Table 5 , the hazard degree evaluation model for termite infestations is derived, as shown in Formula ( 1 ). where, a, b and c are the scores for surface activity characteristics, internal activity characteristics, and impacts on hydraulic structures, respectively; γ1, γ2 and γ3 are the corresponding weight coefficients for these indices, as shown in Table 2 . RIm is the hazard degree index, obtained by summing the products of the scores and their corresponding weights. 3.3.2. Hazard Sensitivity Evaluation Model Based on Table 3 and Table 6 , the hazard sensitivity evaluation model for termite infestations is derived, as shown in Formula ( 2 ). where, d, e, f, g, h, i, j and k are the scores for annual average temperature, annual precipitation, vegetation cover, topography, termite species, dam grade, management status, and treatment status, respectively; γ4, γ5, γ6, γ7, γ8, γ9, γ10 and γ11 are the corresponding weight coefficients for these indices, as shown in Table 3 . RIn is the hazard sensitivity index, obtained by summing the products of the scores and their corresponding weights. 3.3.3. Comprehensive Evaluation Model Based on Formula ( 1 ) and Formula ( 2 ), the comprehensive evaluation model for termite infestations is derived as shown in Formula ( 3 ). where, RIm and RIn are the hazard degree index and hazard sensitivity index, respectively; α and β are the corresponding weight coefficients for these indices, as shown in Table 4 . RI is the comprehensive index of termite infestation, obtained by summing the products of the indices and their corresponding weights. 4. Spatial Analysis of Termite Infestations 4.1. Hazard Degree Index Analysis The spatial distribution results of surface activity characteristics, internal activity characteristics, and impacts on hydraulic structures were integrated with weight coefficients into the hazard degree evaluation model. The natural breaks classification method in the ArcGIS system was then applied to categorize hazard degrees into three levels, ranging from light to severe (Level 1 to Level 3). The risk distribution of the comprehensive index of termite infestation for the reservoir dams in Nanyang City is illustrated in Fig. 2 . As shown in Fig. 2 , Level 3 risk was predominantly found in Nanzhao County, due to the high number of existing earth-rock dams and historically severe termite infestations. This is consistent with findings from literature [14]. 4.2. Hazard Sensitivity Index Analysis The hazard sensitivity indices for termite infestation were quantitatively graded based on the levels of each evaluation index. ArcGIS was used to generate risk distribution maps for these indices. Due to space limitations, this paper focuses on four indices: annual average temperature, annual precipitation, topography, and vegetation cover. Figure 3 illustrates the risk distribution of the annual average temperature index in Nanyang City. According to Fig. 3 , Level 3 risk areas are primarily located in the central and southern parts of city. Previous studies (Cornelius LM and Osbrink ALW 2021) indicate that termite activity increases with rising temperatures. In Nanyang City, the annual average temperature ranges from 8.80°C to 18.74°C with temperatures between 16.74°C to 18.74°C classified as Level 3 risk. Therefore, higher-risk areas were concentrated in the warmer central and southern regions. Figure 4 showcases the risk distribution of the annual precipitation index in Nanyang City. It can be observed that annual precipitation in Nanyang City was concentrated in Dengzhou City, Xinye County, Tanghe County, Tongbai County, and the northeastern part of Xichuan County, with amounts exceeding 975.72 mm. Increased moisture from precipitation enhances soil and air humidity, promoting termite activity. Consequently, Level 3 risk areas for annual precipitation are primarily located in these southern regions, which experience relatively high rainfall. Figure 5 displays the risk distribution of the topography index in Nanyang City. According to Fig. 5 , the hilly and mountainous areas of Nanyang City are concentrated in Xixia County, Xichuan County, Neixiang County, Nanzhao County, and Tongbai County. Compared to plains, hilly and mountainous terrains provide easier access to food for termites, enhancing their survival. Therefore, Level 3 risk areas for topography are primarily located in the complex terrain of the northwest and southeast regions. Figure 6 provides the risk distribution of vegetation cover index in Nanyang City. It can be observed that areas with high vegetation cover are primarly concentrated in Xixia County, Neixiang County, Nanzhao County, Sheqi County, Tanghe County, and Tongbai County, with vegetation cover exceeding 78.35%. Since termites primarily feed on cellulose and hemicellulose from plants and wood, Level 3 risk areas for vegetation cover are concentrated in the northwest and southeast regions, which have higher vegetation cover. Based on the hazard sensitivity evaluation model and considering the risk distribution of annual average temperature, annual precipitation, vegetation cover, topography, termite species, dam grade, management status, and treatment status, the natural breaks classification method in the ArcGIS was used to obtain the risk distribution of the hazard sensitivity index for termite infestation in Nanyang City, as shown in Fig. 7 . This figure reflects the potential risk distribution of termite infestation in reservoir dams within Nanyang City. According to Fig. 7 , Level 3 risk areas are mainly located in the southeastern parts of Tongbai County and Tanghe County, while Level 2 risk areas are primarily found in Xichuan County, Nanzhao County, Zhenping County, Tanghe County, Xixia County, and Sheqi County. These regions experience higher annual temperatures, significant rainfall, widespread rivers and lakes, abundant water resources, hilly and mountainous terrain, and high vegetation cover. These multiple factors induce a high sensitivity risk of termite infestation. Therefore, these areas should be prioritized in future termite infestation surveys and control efforts in hydraulic engineering. 4.3. Termite Infestation Risk Zoning in Reservoirs Based on the comprehensive evaluation model of termite infestation and utilizing the natural breaks classification method in the ArcGIS system, along with the "Technical Guidelines for Termite Control in Hydraulic Engineering (Trial)" for grading termite infestation, the risk zoning for termite infestation in reservoirs in Nanyang City was categorized into three levels: [0, 30) as low-risk (Level I), requiring enhanced routine inspections and termite control; [30, 50) as moderate-risk (Level II), necessitating focused inspections and extermination; and [50, 100] as high-risk (Level III), requiring targeted treatment projects. Figure 8 shows the comprehensive risk zoning map of termite infestation in reservoirs in Nanyang City. This map was derived from the overlay analysis of Figs. 2 and 7 , utilizing the spatial analysis function in the ArcGIS system. According to Fig. 8 , Level III risk areas are primarly concentrated in Nanzhao County and Tongbai County, while Level II risk areas were mainly in Xichuan County, Zhenping County, Sheqi County, Wancheng District, and Tanghe County. Based on the risk distribution, nine medium-sized reservoirs in Level III risk areas - Liushan Reservoir, Penglikeng Reservoir, Damoshiyan Reservoir, Liaozhuang Reservoir, Zhaozhuang Reservoir, Erlangshan Reservoir, Jinzhuang Reservoir, Shantou Reservoir, and Doupo Reservoir - require urgent termite control efforts. One large (Type I) reservoir, Yahekou Reservoir, along with five medium-sized reservoirs - Qiyu Reservoir, Lanying Reservoir, Shibuhe Reservoir, Longtanhe Reservoir, and Xinzhuang Reservoir- in Level II risk areas, need enhanced inspections and routine extermination. Due to the severe termite infestation and high risk index in the nine Level III risk reservoirs, which threaten the safe operation of reservoirs, it is recommended to prioritize these reservoirs for termite control efforts. Reservoirs in Level II risk areas should focus on enhanced inspections and routine extermination to prevent further development of termite infestations, particularly in the event of limiting funding far termite treatment. 5. Conclusions ( 1 ) This study focused on termite-infested reservoirs in Nanyang City, Henan Province, establishing evaluation index system for termite infestation risk in reservoirs. Hazard degree, sensitivity, and comprehensive evaluation models, were developed based on the theories on index systems and disaster risks. Using an expert system, the study effectively mapped the risk zoning of termite infestations in Nanyang City. The findings provide an accurate reflection the risk distribution of termite infestations in the city. ( 2 ) Based on the risk zoning results of termite infestations in Nanyang City, nine medium-sized reservoirs - Liushan Reservoir, Penglikeng Reservoir, Damoshiyan Reservoir, Liaozhuang Reservoir, Zhaozhuang Reservoir, Erlangshan Reservoir, Jinzhuang Reservoir, Shantou Reservoir, and Doupo Reservoir - are classified as Level III risk areas and require urgent termite control efforts. One large (Type I) reservoir, Yahekou Reservoir, and five medium-sized reservoirs - Qiyu Reservoir, Lanying Reservoir, Shibuhe Reservoir, Longtanhe Reservoir, and Xinzhuang Reservoir - are classified as Level II risk areas and require enhanced inspections and routine extermination. ( 3 ) Termite infestations in hydraulic engineering are marked by their concealment, complexity, and long-term nature. The theoretical research and practical application of comprehensive regional management of termite infestations in reservoir dams are still developing. 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Technology","correspondingAuthor":false,"prefix":"","firstName":"Wenkai","middleName":"","lastName":"Zhang","suffix":""},{"id":358506041,"identity":"16db9a42-f09c-4c6d-872f-a82f7c2bf706","order_by":2,"name":"Liqing Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAp0lEQVRIiWNgGAWjYBACPnYGxsdQtgFxWtiYGZiNgbQESVrYpEnVwrytuqDmcB0De/M2CYaaO8RoYSu7PePYYQkGnmNlEgzHnhGjhcfsNg8bUItEjpkEY8Nh4rQU8/wDapF/Q4IWZt42kC08RGthK5bm7UuXbONJK7ZIOEaEFn725o2feb5Z8/OzH95440MNEVoYYNHBBiISiNJAdAyOglEwCkbByAUA9isp4SKYc1MAAAAASUVORK5CYII=","orcid":"","institution":"Henan University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Liqing","middleName":"","lastName":"Li","suffix":""},{"id":358506042,"identity":"a63c1e3e-b7d9-4475-afaa-05f381bd09d9","order_by":3,"name":"Ke Bian","email":"","orcid":"","institution":"Henan Water Conservancy Science and Technology Application Center","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Bian","suffix":""},{"id":358506044,"identity":"3867e849-d554-4fbf-a6b3-00c610dc51f3","order_by":4,"name":"Shiwei LI","email":"","orcid":"","institution":"Henan Water Conservancy Science and Technology Application Center","correspondingAuthor":false,"prefix":"","firstName":"Shiwei","middleName":"","lastName":"LI","suffix":""}],"badges":[],"createdAt":"2024-08-31 12:22:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5008917/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5008917/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66944610,"identity":"1e2bf73e-f1fe-4ef1-92fa-aa0d4dc6f5d8","added_by":"auto","created_at":"2024-10-18 09:37:30","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75070,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of termite-infested reservoirs in Nanyang City\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5008917/v1/0a728ceef09964bfa831b38a.jpeg"},{"id":66944616,"identity":"94bbb4f6-b4b0-4443-be35-c72a1ba42ed1","added_by":"auto","created_at":"2024-10-18 09:37:30","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":60768,"visible":true,"origin":"","legend":"\u003cp\u003eRisk distribution of hazard degree index for termite infestation in Nanyang City\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5008917/v1/bd1a80e7ec455a61e1d12f83.jpeg"},{"id":66944612,"identity":"f93a8c43-db2b-4f51-aab0-3d189ed3c09c","added_by":"auto","created_at":"2024-10-18 09:37:30","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":43432,"visible":true,"origin":"","legend":"\u003cp\u003eRisk distribution of annual average temperature index in Nanyang City\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5008917/v1/a16f6356f78522a2bf0ed79d.jpeg"},{"id":66946412,"identity":"5e47d756-31d0-4e40-8ea7-febcf58d5dde","added_by":"auto","created_at":"2024-10-18 09:45:30","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":44435,"visible":true,"origin":"","legend":"\u003cp\u003eRisk distribution of annual precipitation index in Nanyang City\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5008917/v1/2a56740be565f7759ea64bcf.jpeg"},{"id":66946411,"identity":"430dc202-5afa-4c44-82df-74be59aac999","added_by":"auto","created_at":"2024-10-18 09:45:30","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":44984,"visible":true,"origin":"","legend":"\u003cp\u003eRisk distribution of topography index in Nanyang City\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5008917/v1/a0d7f86765a92df15e468caf.jpeg"},{"id":66946409,"identity":"2f70ccb2-5e0c-4526-ae5e-ea4af8dafe83","added_by":"auto","created_at":"2024-10-18 09:45:30","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":53314,"visible":true,"origin":"","legend":"\u003cp\u003eRisk distribution of vegetation cover index in Nanyang City\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5008917/v1/3e65758e332865ea0b19e6b3.jpeg"},{"id":66944614,"identity":"72921e31-3b43-4803-b339-8f784760d10b","added_by":"auto","created_at":"2024-10-18 09:37:30","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":64181,"visible":true,"origin":"","legend":"\u003cp\u003eRisk distribution of hazard sensitivity indices for termite infestation in Nanyang City\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5008917/v1/1bce2ae2ea915e5b59437304.jpeg"},{"id":66944617,"identity":"7af47000-9464-417f-b706-0ec1d62187b2","added_by":"auto","created_at":"2024-10-18 09:37:30","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":55487,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive risk zoning map of termite infestation in reservoirs in Nanyang City\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5008917/v1/83e2e341381da5df39420e08.jpeg"},{"id":69976480,"identity":"07030e6d-0070-49a1-aa7d-fc3cabd91a3c","added_by":"auto","created_at":"2024-11-27 07:09:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1073118,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5008917/v1/6110f515-7bc3-476a-8501-d512c94782da.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Expert System-Based Risk Zoning for Termite Infestation in Reservoirs","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHenan Province spans four major river basins: the Yangtze, Huaihe, Yellow, and Haihe Rivers. It lies within a transitional zone between the north subtropical and warm temperate climates. The extensive water systems, coupled with the warm and humid climate, create favorable conditions for rapid and prolific termite reproduction. In recent years, driven by global warming, termites have spread from the southern regions to the banks of the Yellow River (Bale JS et al. 2002). Reservoir dams and other hydraulic projects are often located in hilly and mountainous areas. The abundant water sources and dense forests surrounding these dams facilitate termite migration and nest-building. Termite nests function like \"bombs\" buried within embankments; rising reservoir water levels can trigger incidents such as seepage, piping, and sinkholes in earth-rock dams, significantly threatening the safe operation of reservoirs (Saghaee G et al. 2017). Consequently, the Ministry of Water Resources issued the \"Guidelines for Termite Control in Hydraulic Engineering,\" which call for the establishment of a comprehensive termite control system, the improvement of funding mechanisms for termite control, and the acceleration of foundational research on grading standards for termite damage (Ministry of Water Resources. 2023). However, due to limited funding for termite control in hydraulic engineering and a shortage of technical personnel in Henan Province, it is currently impossible to completely eradicate termite infestations in reservoir dams all at once (Zhang WJ. 2023). Therefore, prioritizing termite control efforts and determining key inspection priorities are urgent technical challenges. Currently, research in disaster and risk sciences for hydraulic engineering is centered on developing risk evaluation systems and models, as well as on disaster and risk zoning (Amendola A. 2008).\u003c/p\u003e \u003cp\u003eWith the advancement of Geographic Information System (GIS) technology, regional risk zoning using GIS has become a major focus of research for water resource professionals(Zargar M et al. 2016; Ter\u0026ecirc;ncio DPS et al. 2020; Owusu PA et al. 2013). Tawatchai Tingsanchali et al (2010).combined GIS with risk assessment techniques to evaluate flood disasters and risks in the Yom River, with the goal of minimizing local economic and environmental losses. Raffaele De Risi et al (2020). conducted a flood risk assessment in Addis Ababa to provide technical support for urban strategic planning and decision-making. Chen Cihao et al (2023). employed GIS technology to predict the distribution of risk areas for Aedes albopictus invasion in Guangdong Province, providing a scientific basis for developing epidemic prevention strategies. Emre Ozsahin et al (2022). employed GIS to analyze the impact of climate and topography on the range and spatial distribution of termite habitats. Jacob Mayowa Owoyemi et al (2021). relied on GIS technology to map the probability of severe termite infestations on the FUTA campus, aiding in termite eradication efforts. However, due to the shortage of termite control technicians in hydraulic engineering, there is relatively little research on using GIS for risk evaluation and zoning of termite infestations in reservoir dams. Therefore, this study focused on Nanyang City, Henan Province, where termite infestations in hydraulic engineering are severe, to establish an evaluation index system and models for assessing termite infestation risks. Based on GIS technology, risk zoning was performed for termite infestations in reservoirs within Nanyang City. This allowed for the identification of reservoirs requiring focused treatment and key inspections according to their risk levels. The research results are expected to provide technical support and a theoretical foundation for the systematic control and targeted inspections of termite infestations in reservoirs across Henan Province.\u003c/p\u003e"},{"header":"2. Overview of the Study Area","content":"\u003cp\u003eNanyang City features a diverse landscape, with plains covering 21% of the area, hills 30.6%, and mountains 48.4%. It serves as a key water source and canal head for the middle route of the South-to-North Water Diversion Project. The city is crisscrossed numerous rivers that are part of the Yangtze, Huaihe, and Yellow River systems. Nanyang experiences a north subtropical monsoon-type continental climate, with annual precipitation ranging from 801 to 1,174 mm. More than 95% of the reservoir dams in Nanyang are earth-rock dams. The ample water sources, suitable soil for nesting, and high vegetation cover create an excellent habitat for termites. Existing data indicate that termite infestations have been identified in reservoirs across 11 out of 13 counties and districts in Nanyang. Of the 561 reservoirs, 335 have been infested by termites, representing 59.7%, with 76 of these reservoirs classified as severely infested (Lei CW et al. 2022). The specific distribution is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Given the challenging and urgent need to manage termite-infested reservoirs in Nanyang City, it is crucial to develop a risk evaluation system, evaluation models, and risk zoning for these reservoirs. This research aims to provide both technical support and theoretical foundations for the systematic control, targeted inspections, and termite management efforts of reservoir dams in Nanyang City.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3. Evaluation Index System for Termite Infestation in Reservoirs","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Selection and Weighting of Evaluation Indices\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. Selection of Evaluation Indices\u003c/h2\u003e \u003cp\u003eThe \"Technical Guidelines for Termite Control in Hydraulic Engineering (Trial)\" (Ministry of Water Resources. 2023) assess the severity of termite infestation in hydraulic structures based on surface activity characteristics, internal activity characteristics, and impacts on hydraulic structures. However, due to the concealed, recurrent, and long-term nature of termite infestations, there is a need to further refine the grading technology for termite damage by incorporating additional factors such as regional topography, dam grade, vegetation cover, and treatment status.\u003c/p\u003e \u003cp\u003eBased on literature [15] and expert consultation, 11 evaluation indices were selected from two main aspects: factors related to the degree of termite infestation (termite infestation status in reservoirs) and factors related to termite infestation sensitivity factors (natural and environmental conditions). This selection led to the construction of an evaluation index system for termite infestation in reservoirs. Details are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEvaluation index system for termite infestation in reservoirs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoal layer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"11\" nameend=\"c12\" namest=\"c2\"\u003e \u003cp\u003eComprehensive index of termite infestation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriterion layer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eHazard degree indices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c12\" namest=\"c5\"\u003e \u003cp\u003eHazard sensitivity indices\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlternative layer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurface activity characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInternal activity characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImpacts on hydraulic structures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnnual average temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAnnual precipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTopography\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDam grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eVegetation cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTermite species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eManagement status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eTreatment status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. Weighting of Evaluation Indices\u003c/h2\u003e \u003cp\u003eInformation was collected through questionnaires distributed to experts and technical personnel in termite control to obtain scores and rankings for each index. A total of 66 questionnaires were distributed, of which 3 were invalid, resulting in 63 valid responses. Referring to the structural entropy method employed by B. Srdjevic et al (2004). both subjective and objective weighting methods were combined to determine the weights of hazard degree indices, hazard sensitivity indices, and the comprehensive index for termite infestations in reservoirs. The weights for these three types of indices are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWeights of hazard degree indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvaluation index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurface activity characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInternal activity characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImpacts on hydraulic structures\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWeights of hazard sensitivity indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvaluation index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual average temperature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual precipitation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTopography\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDam grade\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVegetation cover\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTermite species\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eManagement status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTreatment status\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWeight of comprehensive index\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvaluation index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard degree indices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHazard sensitivity indices\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Quantification and Classification of Evaluation Indices\u003c/h2\u003e \u003cp\u003eBased on the literature [15], hazard degree indices were classified into three risk levels. The classification of hazard sensitivity indices was quantified according to the relationship between index values and the risk level of termite infestation, using the natural breaks classification method in ArcGIS.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Quantification of Hazard Degree Indices\u003c/h2\u003e \u003cp\u003eThe surface activity characteristics of hazard degree indices were classified based on the number of exposed features such as mud tubes and mud trails. Internal activity characteristics were classified based on the size of termite nests and the length of termite queens. Impacts on hydraulic structures were classified based on whether phenomena such as seepage and wet slopes occur in hydraulic engineering. Details are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValues and risk levels of hazard degree indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvaluation index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndex value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSurface activity characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfested area 0\u0026ndash;4 spots, Source area 3\u0026ndash;14 spots\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfested area 5\u0026ndash;9 spots, Source area 15 spots and above, Source area with flight holes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfested area 10 spots and above, Infested area with flight holes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eInternal activity characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMain nest diameter\u0026thinsp;\u0026le;\u0026thinsp;250 mm or Queen length\u0026thinsp;\u0026le;\u0026thinsp;30 mm, Nest in juvenile stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250 mm\u0026thinsp;\u0026lt;\u0026thinsp;Main nest diameter\u0026thinsp;\u0026le;\u0026thinsp;350 mm or 30 mm\u0026thinsp;\u0026lt;\u0026thinsp;Queen Length\u0026thinsp;\u0026le;\u0026thinsp;50 mm, Mature nest found\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMain nest diameter\u0026thinsp;\u0026gt;\u0026thinsp;350 mm or Queen length\u0026thinsp;\u0026gt;\u0026thinsp;50 mm, More than 3 mature nests found, Through tunnels in main structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eImpacts on hydraulic structures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSigns of termite activity, No seepage or wet slope damage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeepage or wet slope damage due to termite activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeaks, sinkholes, or slope failure due to termite activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Quantification of Hazard Sensitivity Indices\u003c/h2\u003e \u003cp\u003eConsidering the natural environment and reservoir conditions in Nanyang City, annual average temperature, annual precipitation, vegetation cover, topography, termite species, dam grade, management status, and treatment status were selected as hazard sensitivity indices. The classification was quantified based on the relationship between index values and the risk level of termite infestation, using the natural breaks classification method in ArcGIS. Details are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValues and risk levels of hazard sensitivity indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvaluation index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndex value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAnnual average temperature\u003c/p\u003e \u003cp\u003e(Peng S 2019)\u003c/p\u003e \u003cp\u003e/\u003cem\u003e℃\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;14.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.17\u0026thinsp;~\u0026thinsp;16.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;16.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAnnual precipitation (Peng S 2019)\u003c/p\u003e \u003cp\u003e/mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;907.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e907.21\u0026thinsp;~\u0026thinsp;975.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;975.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVegetation cover (Xu XL 2018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60.07\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.07%~78.35\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;78.35\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTopography (Cheng W and Zhou C 2014; Li BY et al 2008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlains and terraces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMountain areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTermite species\u003c/p\u003e \u003cp\u003e(Wang ZG 1984)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAreas with a single species and minor infestations or no termite infestations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAreas with multiple species and severe infestations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAreas with the most species and the most severe infestations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDam grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmall (Type I) reservoirs and small (Type II) reservoirs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium-sized reservoirs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge (Type I) reservoirs and large (Type II) reservoirs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eManagement status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReservoirs managed by water conservancy departments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReservoirs managed by agricultural service centers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReservoirs managed by non-water conservancy departments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTreatment status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTermite infestations have been treated/no termite infestations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTermite infestations are being treated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTermite infestations have not been treated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Termite Infestation Evaluation Models\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Hazard Degree Evaluation Model\u003c/h2\u003e \u003cp\u003eBased on Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the hazard degree evaluation model for termite infestations is derived, as shown in Formula (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"361\" height=\"31\"\u003e\u003c/p\u003e \u003cp\u003ewhere, a, b and c are the scores for surface activity characteristics, internal activity characteristics, and impacts on hydraulic structures, respectively; γ1, γ2 and γ3 are the corresponding weight coefficients for these indices, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. RIm is the hazard degree index, obtained by summing the products of the scores and their corresponding weights.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Hazard Sensitivity Evaluation Model\u003c/h2\u003e \u003cp\u003eBased on Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the hazard sensitivity evaluation model for termite infestations is derived, as shown in Formula (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"480\" height=\"34\"\u003e\u003c/p\u003e\u003cp\u003ewhere, d, e, f, g, h, i, j and k are the scores for annual average temperature, annual precipitation, vegetation cover, topography, termite species, dam grade, management status, and treatment status, respectively; γ4, γ5, γ6, γ7, γ8, γ9, γ10 and γ11 are the corresponding weight coefficients for these indices, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. RIn is the hazard sensitivity index, obtained by summing the products of the scores and their corresponding weights.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3. Comprehensive Evaluation Model\u003c/h2\u003e \u003cp\u003eBased on Formula (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) and Formula (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), the comprehensive evaluation model for termite infestations is derived as shown in Formula (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ewhere, RIm and RIn are the hazard degree index and hazard sensitivity index, respectively; α and β are the corresponding weight coefficients for these indices, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. RI is the comprehensive index of termite infestation, obtained by summing the products of the indices and their corresponding weights.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Spatial Analysis of Termite Infestations","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Hazard Degree Index Analysis\u003c/h2\u003e \u003cp\u003eThe spatial distribution results of surface activity characteristics, internal activity characteristics, and impacts on hydraulic structures were integrated with weight coefficients into the hazard degree evaluation model. The natural breaks classification method in the ArcGIS system was then applied to categorize hazard degrees into three levels, ranging from light to severe (Level 1 to Level 3). The risk distribution of the comprehensive index of termite infestation for the reservoir dams in Nanyang City is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Level 3 risk was predominantly found in Nanzhao County, due to the high number of existing earth-rock dams and historically severe termite infestations. This is consistent with findings from literature [14].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Hazard Sensitivity Index Analysis\u003c/h2\u003e \u003cp\u003eThe hazard sensitivity indices for termite infestation were quantitatively graded based on the levels of each evaluation index. ArcGIS was used to generate risk distribution maps for these indices. Due to space limitations, this paper focuses on four indices: annual average temperature, annual precipitation, topography, and vegetation cover.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the risk distribution of the annual average temperature index in Nanyang City. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Level 3 risk areas are primarily located in the central and southern parts of city. Previous studies (Cornelius LM and Osbrink ALW 2021) indicate that termite activity increases with rising temperatures. In Nanyang City, the annual average temperature ranges from 8.80\u0026deg;C to 18.74\u0026deg;C with temperatures between 16.74\u0026deg;C to 18.74\u0026deg;C classified as Level 3 risk. Therefore, higher-risk areas were concentrated in the warmer central and southern regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e showcases the risk distribution of the annual precipitation index in Nanyang City. It can be observed that annual precipitation in Nanyang City was concentrated in Dengzhou City, Xinye County, Tanghe County, Tongbai County, and the northeastern part of Xichuan County, with amounts exceeding 975.72 mm. Increased moisture from precipitation enhances soil and air humidity, promoting termite activity. Consequently, Level 3 risk areas for annual precipitation are primarily located in these southern regions, which experience relatively high rainfall.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e displays the risk distribution of the topography index in Nanyang City. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the hilly and mountainous areas of Nanyang City are concentrated in Xixia County, Xichuan County, Neixiang County, Nanzhao County, and Tongbai County. Compared to plains, hilly and mountainous terrains provide easier access to food for termites, enhancing their survival. Therefore, Level 3 risk areas for topography are primarily located in the complex terrain of the northwest and southeast regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e provides the risk distribution of vegetation cover index in Nanyang City. It can be observed that areas with high vegetation cover are primarly concentrated in Xixia County, Neixiang County, Nanzhao County, Sheqi County, Tanghe County, and Tongbai County, with vegetation cover exceeding 78.35%. Since termites primarily feed on cellulose and hemicellulose from plants and wood, Level 3 risk areas for vegetation cover are concentrated in the northwest and southeast regions, which have higher vegetation cover.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the hazard sensitivity evaluation model and considering the risk distribution of annual average temperature, annual precipitation, vegetation cover, topography, termite species, dam grade, management status, and treatment status, the natural breaks classification method in the ArcGIS was used to obtain the risk distribution of the hazard sensitivity index for termite infestation in Nanyang City, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. This figure reflects the potential risk distribution of termite infestation in reservoir dams within Nanyang City. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Level 3 risk areas are mainly located in the southeastern parts of Tongbai County and Tanghe County, while Level 2 risk areas are primarily found in Xichuan County, Nanzhao County, Zhenping County, Tanghe County, Xixia County, and Sheqi County. These regions experience higher annual temperatures, significant rainfall, widespread rivers and lakes, abundant water resources, hilly and mountainous terrain, and high vegetation cover. These multiple factors induce a high sensitivity risk of termite infestation. Therefore, these areas should be prioritized in future termite infestation surveys and control efforts in hydraulic engineering.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Termite Infestation Risk Zoning in Reservoirs\u003c/h2\u003e \u003cp\u003eBased on the comprehensive evaluation model of termite infestation and utilizing the natural breaks classification method in the ArcGIS system, along with the \"Technical Guidelines for Termite Control in Hydraulic Engineering (Trial)\" for grading termite infestation, the risk zoning for termite infestation in reservoirs in Nanyang City was categorized into three levels: [0, 30) as low-risk (Level I), requiring enhanced routine inspections and termite control; [30, 50) as moderate-risk (Level II), necessitating focused inspections and extermination; and [50, 100] as high-risk (Level III), requiring targeted treatment projects. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the comprehensive risk zoning map of termite infestation in reservoirs in Nanyang City. This map was derived from the overlay analysis of Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, utilizing the spatial analysis function in the ArcGIS system. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, Level III risk areas are primarly concentrated in Nanzhao County and Tongbai County, while Level II risk areas were mainly in Xichuan County, Zhenping County, Sheqi County, Wancheng District, and Tanghe County. Based on the risk distribution, nine medium-sized reservoirs in Level III risk areas - Liushan Reservoir, Penglikeng Reservoir, Damoshiyan Reservoir, Liaozhuang Reservoir, Zhaozhuang Reservoir, Erlangshan Reservoir, Jinzhuang Reservoir, Shantou Reservoir, and Doupo Reservoir - require urgent termite control efforts. One large (Type I) reservoir, Yahekou Reservoir, along with five medium-sized reservoirs - Qiyu Reservoir, Lanying Reservoir, Shibuhe Reservoir, Longtanhe Reservoir, and Xinzhuang Reservoir- in Level II risk areas, need enhanced inspections and routine extermination. Due to the severe termite infestation and high risk index in the nine Level III risk reservoirs, which threaten the safe operation of reservoirs, it is recommended to prioritize these reservoirs for termite control efforts. Reservoirs in Level II risk areas should focus on enhanced inspections and routine extermination to prevent further development of termite infestations, particularly in the event of limiting funding far termite treatment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) This study focused on termite-infested reservoirs in Nanyang City, Henan Province, establishing evaluation index system for termite infestation risk in reservoirs. Hazard degree, sensitivity, and comprehensive evaluation models, were developed based on the theories on index systems and disaster risks. Using an expert system, the study effectively mapped the risk zoning of termite infestations in Nanyang City. The findings provide an accurate reflection the risk distribution of termite infestations in the city.\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Based on the risk zoning results of termite infestations in Nanyang City, nine medium-sized reservoirs - Liushan Reservoir, Penglikeng Reservoir, Damoshiyan Reservoir, Liaozhuang Reservoir, Zhaozhuang Reservoir, Erlangshan Reservoir, Jinzhuang Reservoir, Shantou Reservoir, and Doupo Reservoir - are classified as Level III risk areas and require urgent termite control efforts. One large (Type I) reservoir, Yahekou Reservoir, and five medium-sized reservoirs - Qiyu Reservoir, Lanying Reservoir, Shibuhe Reservoir, Longtanhe Reservoir, and Xinzhuang Reservoir - are classified as Level II risk areas and require enhanced inspections and routine extermination.\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Termite infestations in hydraulic engineering are marked by their concealment, complexity, and long-term nature. The theoretical research and practical application of comprehensive regional management of termite infestations in reservoir dams are still developing. Further optimization and enhancement of the index system and evaluation models are needed to ensure that the evaluation results are more targeted and valuable for reference.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL.H. and Z. wrote the main manuscript text . All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAcknowledgements：The authors would like to express their gratitude to EditSprings (https://www.editsprings.cn ) for the expert linguistic services provided.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBale JS, Masters GJ, Hodkinson ID et al (2002) Herbivory in global climate change research:direct effects of rising temperature on insect herbivores. 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Research on risk zoning of termite infestations in reservoirs is crucial for prioritizing termite control efforts and rationally allocating funds for treatment. Using termite-infested reservoirs in Nanyang City, Henan Province, as a case study, this research employed disaster risk theory and selected hazard degree indices for evaluation. The indices include surface activity characteristics, internal activity characteristics, and impacts on hydraulic structures. Additionally, hazard sensitivity indices were chosen, incorporating factors such as annual average temperature, annual precipitation, vegetation cover, topography, termite species, dam grade, management status, and treatment status. Based on these factors, an evaluation index system for termite infestation risk in reservoirs was established, along with models for hazard degree, sensitivity, and comprehensive evaluation. Utilizing ArcGIS for geographical processing and spatial analysis, a risk zoning map of termite infestation in the reservoirs of Nanyang City was generated. This map identified nine medium-sized reservoirs requiring urgent termite control, along with one large (Type I) reservoir and five medium-sized reservoirs that need enhanced inspections.\u003c/p\u003e","manuscriptTitle":"Expert System-Based Risk Zoning for Termite Infestation in Reservoirs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-18 09:37:25","doi":"10.21203/rs.3.rs-5008917/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"289c9a06-73c6-4088-ab43-19c6aee0860e","owner":[],"postedDate":"October 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-27T07:09:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-18 09:37:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5008917","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5008917","identity":"rs-5008917","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}