Geospatial based assessment of Groundwater Potential Zones using MIF technique in a watershed of Malwa region, Madhya Pradesh, India

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K. Awasthi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5750566/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 Water is a major natural resource and a key constituent of all living beings. Water is available in two introductory forms i.e. surface water and groundwater. One of the most important accumulated resources in the world is groundwater, which is inversely distributed around the world. To boost the groundwater-recharge is one of the primary hydrological parameters for assessment, budgeting, management, and modelling of ground water resources. Although information and data regarding recharge rate is vital for recharge assessment of any region, determination of this parameter is not easy and straigntforward. The current study has been conducted in northwest part of Madhya Pradesh. The Neemuch and Mandsaur district comes under semi-arid zones and faces the problem of scarcity of water annually. Thus study aims to assess the groundwater recharge zones using an integrated approach of remote sensing and geographical information system (GIS). The parameters considered for identifying the groundwater potential zone of geology, slope, drainage density, soil, geomorphology and lineament density. These thematic maps were generated using the SRTM DEM, Sentinel-2 Imaginary and Survey of India (SOI) toposheets of scale 1:50000 and integrated them to identify the groundwater potential of the study area. Appropriate weightage factors were assigned for each class of these parameters. For the various geomorphic units, weightage factors were assigned based on their capability to store ground-water using multi-influencing factor (MIF). This procedure was repeated for all the other layers and resultant layers were re-classified. After reclassification, the layers were then united to demarcate zones as very good, good, moderate, poor and very poor. The assessment of groundwater potentiality information using RS & GIS could be used for effective identification of suitable locals for birth of potable water for rural populations. Agricultural Engineering Remote sensing GIS Multi-Influencing Factor Groundwater recharge Groundwater potential zones Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction Water is the main constituent of the Earths hydrosphere. It is very important ingredient forms of life, despite providing neither food, energy nor organic micronutrients. All the living organisms are dependent on water. One of the most vital resources in the world is potable water. Potable water is available in different forms like surface water, groundwater, river, lakes etc. in all areas. Although there is a plenty of water on Earth, water has not been available when and where it is in need. India is fast moving away from being a predominantly agricultural society. The urban industrial sector and ecological needs are silently acquiring. Exploitation of the groundwater has increased greatly in the last two to three decades in India. (Verghese 1990 ). The groundwater scenario in India, which receives a substantial amount of rainfall, is not very encouraging primarily due to the imbalance between recharge and groundwater exploitation. Although transportation of fresh water may be expensive and the supply may not always be sustainable. The rainfall occurrence is highly seasonal in many parts of India. In arid and semi-arid regions of India the only source of freshwater is rainfall. Therefore, such regions experience the droughtful condition due to insufficiency of water availability before monsoon season. In such conditions, groundwater plays and dominant role in consumption of water for agriculture, drinking and other socio-economic usage. Development of economical agriculture highly depends on groundwater in many semi-arid climatic regions (Senapati and Das, 2022). Hence new research has been carried out on groundwater recharge zones in the countries like China, Iran, Saudi Arabia, Tiwan etc. and found that the groundwater resource is reducing (Shao et al. 2020; Luo et al. 2020; Arabameri et al. 2020; Rahmati et al. 2016; Mallick et al. 2019; Zabihi et al. 2020; Manap et al. 2011; Serele et al. 2019; Yeh et al. 2016; Owolabi et al. 2020). Geophysical data with geospatial data have been integrated by many researchers (Srivastava et al. 2006 and Rashid et al. 2011 ). The surface hydrological features like soil, slope, drainage, geology etc., plays an important role in recharging the groundwater. Groundwater recharge is one practice option to utilize unused water in the replenishment of aquifers. Surface waterbodies like river, lakes, ponds etc. can act as recharge zone in enhancing the groundwater potentials. Therefore, identification and development of groundwater potential zones is a main aim to increase the groundwater aquifers. Geospatial techniques play a vital role in exploration of groundwater. To establish a good harmony between groundwater and runoff, rainfall preservation strategies for semi-arid region (Biswas et al 2012), groundwater potential mapping is required (Asadi et al 2007 ; Sunitha et al 2016; Duan et al 2016; nag and Ray 2013). Easy understanding, data handling and processing made researchers comfortable to use Remote sensing and GIS tools. Remote Sensing data are processed in GIS software (Ramasamy & Anbazhagan 1997 ). This study deals with the multi-Influencing factor used to generate groundwater potential map in semi-arid regions of Malwa region in Madhya Pradesh. From these results a soil and water conservation techniques are suggested from the future point of view. Materials and Methods Study area Madhya Pradesh is an agricultural state. It is situated in the central part of India. It has almost all the climatic conditions. About 70.31% of the population lives in rural area while 29.69% lives in urban area. The main occupation of the people in this malwa region is agriculture. The study area is located in the semi-arid zones of Madhya Pradesh which lies in north west part of the state. The geographic area is about 4516.40 Km 2 and lies between north latitude 24 o 44’59”- 23 0 51’27” and east longitude 75 0 00’31”-74 0 46’40”. The topographic elevation ranges from 90–239 meters. Geologically, major parts of the study area is occupied by Deccan Trap, basalts except narrow patch of alluvium and sedimentary rocks of Vindhyans super group in isolated patches. Major parts of the district having gently slope ranging from 317 m and 579 m above mean sea level. The normal maximum temperature receives during may is approximately 40 0 C and minimum during month of January is approximately 9.5 0 C. The normal mean monthly maximum temperature is 31.6 0 C and daily mean minimum temperature exceeds 19 0 C. The summer is the driest period of the year. The study area receives rainfall during June to September. About 90% of the annual rainfall receive during monsoon season only. The relative humidity exceeds 87% in monsoon season. The average annual wind velocity is about 9.2km/hr. Most of the streams in the study area remain dry throughout the year except the monsoon season (June to Sept). Throughout the year the air is quite dry and hot. The physiography is dominated by deccan trap and hence the soil is loamy and clayey. The depth of water level varies from 13.82 in pre monsoon and 3.23 in post-monsoon. Therefore, maximum population is working in agriculture activities. The main crop of this area is wheat, gram, mustered in rabi season and jawar, rice, urad and moong in kharif season. The drainage streams meet to the Gandhi Sagar dam in the east of the study area. Location map of study area is shown in Fig. 1 . Data Collected and software used The study deals with the use of GIS for the preparation, handling and processing of different thematic layers. Shuttle Radar Topography Mission (SRTM) 30-m resolution data were used for identification of slope, drainage and drainage density of the study area. Using the data from SRTM-DEM, the flow direction map was created first, and then the flow accumulation map and, lastly, the streams were created with the help of spatial analysis tool in Arc GIS 10.5. The drainage density map was created using line density tool. To demarcate the study area of Malwa region the Arc SWAT 2012.10.21 version of software is used. The lineament map was digitised from open software Bhuvan at 1:50,000 scale. The Soil map was prepared from scanned map provided by National Bureau of Soil Survey & Land Use Planning (NBSS&LUP), Nagpur. Land Use Land Cover (LULC) image was collected from Sentinel-2B, Google Earth and Bhuvan (LISS_IV and Resource SAT- II). The 2-,3-,4- and 8-layer stacking was processed in ERDAS IMAGINE 9.3 software and supervised classification was done to generate Land Use Land Cover map. The geology and geomorphology map were also digitized from Bhukhosh. Net groundwater availability data was collected from Water resource department, Madhya Pradesh for the validation of groundwater recharge sites. Groundwater data and the rainfall data were collected from Madhya Pradesh Water Resource Department (MPWRD, Central Ground Water Board (CGWB)) and Indian Meteorological Department (IMD) Pune. The (World Geodetic System) WGS 84 datum and (Universal Transverse Mercator) UTM zone 43N, WGS_1984_UTM_43N projection has been used to georeferenced these maps. Thematic layer The thematic layers of slope, soil, drainage density, geology, geomorphology, lineament density and land use/land cover were prepared by using the data. The detailed procedure to identify the groundwater recharge sites are shown in Fig. 2 . The thematic layers that are affecting the groundwater recharge are slope, drainage density, geology, geomorphology, lineament density, land use/land cover, soil texture and rainfall. These agents were considered to assess the groundwater potential zone and then the groundwater recharge sites. The slope and elevation were produces by SRTM-DEM data. The drainage density and lineament density were produced by using drainage and lineament layers respectively. 30 years (1991–2021) rainfall data were taken to generate thematic maps for the study area. After the thematic maps are reclassified, the weightage was assigned to all the layers using multi-influencing factors (MIF) technique. This technique estimates the individual weight that has been given to each thematic layers which is considered like soil, geology, drainage density etc. (Poddar et al. 2020). The effects can be closely observed from the inter-relationship between the layers. After observing the strong and sturdy correlation between the factors, score 1 is assigned. If the impact is minimum and showing the poor relationship between the factors, score assigned is 0.5. Summation of all the weights will give the independent weights of each impacting agent (Pande et. al, 2021). All the relations are weighted according to the direct and indirect impact on groundwater recharge. The sub-classes are classified based on relative relationships (Pande et al., 2021; Poddar et. al., 2020). With the help of this relation the proposed score or the individua weightage is calculated by the following equation. Proposed score = \(\:\frac{(X+{X}^{{\prime\:}})}{(X+{X}^{{\prime\:}})}\times\:100\) ………..(Eq. 1.) Where, X is a major impact and X' is a minor impact. Inter-relationship between the layers impacting on groundwater recharge zones Results and discussion The groundwater potential zones were delineated by using various thematic layers. In order to get the results of semi-arid region in Malwa, the thematic maps are made and weighted overly is carried out. The multi-influencing factor technique is used to generate the Groundwater potential zones. The results pertaining to prepare drainage density, lineament density, elevation map, slope map, soil map, geology map, geomorphology map, rainfall map and land use land cover map. Preparation of thematic maps Geology Groundwater depends on the occurrence of rock and movement of water. Hence geology is key feature to understand. (Thapa et al. 2017 , Shaban et al. 2006 , raju et al. 2019 , Balaji et al. 2019b , Ghasemizadeh et al., 2012 ). In the study area, the geologic formation of Alluvium, Basalt, Laterite, sandstone and shale was found. Basalt is dominating geology in this study area, as it covers 83% of the total area. Geology map is shown in the Fig. 3 . Geomorphology The underground water moves according to the geomorphic units which is under the surface (Poddar et al., 2020). Thus, geomorphology map plays an important role in finding out the groundwater potential zones (Shinde et al., 2022). The geomorphology landform was detected and generated from the satellite data using visual interpretations and the survey of India toposheet maps. It was found that 92% (4246.81 Sq Km) is covered with Pediment pediplain complex followed by moderately desiccated plateau (2%) and other formations which is shown in Fig. 4 . Soil map Soil type has main control of water percolation and infiltration process to the aquifer water is due to soil texture (Raju et al., 2019 ). Soil having high infiltration rate will be best suited to find the artificial recharge sites. It is found that the study area is covered with mainly three types of soil texture i.e., fine clay, loamy and calcareous loamy. It was found that the most dominating soil texture is fine clay (57%) covers area of 2582.16 Sq Km followed by Calcareous loamy (39%) covers area of about 1792.1 Sq. Km. and loamy (4%) covers area of 181.2 Sq. Km. The soil map is shown in the Fig. 5 . Slope map To get the groundwater potential zones, slope is very important key parameter. Surface water is directly affected by the process of infiltration in the study area. (Rajaveni et al., 2015). The slope map was generated using SMRT-30 m DEM data in Arc GIS. The slope varies from 341–651 meters above mean sea level. The present study of slope is classified in five classes viz., very low, low, moderate, high and very high. It was found that the maximum area comes under very low slope of terrain. About 63% (2865.29 Sq Km) of the total study area comes under very low class. Figure 6 shows the details of the slope map. Land Use / Land Cover Land use changes depends upon the manmade growth with respect to industrialization. In this malwa region it is discovered that the main occupation of the people is agriculture. Therefore, maximum land is under the agricultural land, i.e, crop land and fallow land. The classes are shown in Fig. 7 . About 34% and 29% of the total study area is covered with fallow land and agriculture land respectively. In total 63% of the total study area is covered with crop land. Lineament density Linear feature like fracture and joints are present below the earth surface of any area. Water holding and water transmission capacity of these lineaments is very good as they increases the porosity, hydraulic conductivity and permeability of the land surface. Thus, presence of lineaments in particular area is considered helpful for the groundwater recharge. The lineament density of the malwa region is generated through lineament map. The lineament map is shown in the Fig. 8 . Drainage density For and watershed area, the ratio of total distance travelled by all the major and minor rivers to the total surface area is called drainage density. It is mainly expressed as km/km 2 . To evaluate the groundwater potential zones drainage density is an important factor. Higher is the drainage density value, higher is the runoff and vice versa, this ration directly affects the recharge capability of the particular area. The highest stream order in the study area was found as 4. The detailed stream order and the drainage density is shown in the map. (Figs. 9 and 10 respectively). Elevation The elevation is developed using SRTM DEM- 30m using spatial analysis tools. It was observed that the elevation lies between 371 to 579 meters (MSL) in the study area. The elevation map is shown in Fig. 11 . Weightage calculation Multi-influencing factor technique is used in this study. The major and the minor effect of the thematic maps are observed carefully and proposed score is calculated. Every thematic layer has weightage classification. Weighted value of each class is referred from different literature review (Masitoh et al, (2022); Senanayake et. al, ( 2016 ); Maity and Mandal ( 2019 ); Mangesh et. al, (2012); Acharya et. al., ( 2019 ); Raju et. al., ( 2019 ); Etikala et al., ( 2019 ). Weightage given to each major impact is 1 and that of minor impact is 0.5. The major and the minor effect is shown in the Table 1 . From the Eq. 1 the proposed score is calculated and weightage is assigned to every thematic map. Thus, forming the groundwater potential zone map. Table 1 Major and minor effect of thematic layer and the proposed score Factor Major (X) Minor ( \(\:{\varvec{X}}^{\varvec{{\prime\:}}}\) ) ( \(\:\varvec{X}+{\varvec{X}}^{\varvec{{\prime\:}}}\) ) Proposed score Geology 1 + 1 + 1 0.5 3.5 17 Geomorphology 1 + 1 + 1 - 3 14 Land Use Land Cover 1 05 1.5 7 Rainfall 1 0.5 1.5 7 Drainage Density 0.5 + 0.5 1 5 Lineament Density 1 + 1 0.5 2.5 12 Soil 1 + 1 + 1 0.5 3.5 17 Slope 1 0.5 + 0.5 + 0.5 2.5 12 Elevation 1 + 1 - 2 9 Total Σ (X + X') 21 100 Table 2 Weightage and rating of the sub classes influencing the groundwater potential zones. Factors Sub-classes Rating Weightage Geology Alluvium 17 17 Basalt 10 Laterite 5 Sandstone 7 Shale 4 Geomorphology Alluvium Plan 5 14 Flood Plan 14 Low Dissected Hill and Valley 1 Low Dissected Platue 2 Moderately Dissected Hill and Valley 2 Pedi plane complex 7 Land Use Land Cover Agriculture 2 7 Built-up area 1 Forest 5 Pasture Land 2 Waste Land 1 Waterbodies 7 Elevation 0–1 9 9 1–2 7 2–3 5 3–5 2 > 5 1 Drainage Density Very High 5 5 High 4 Medium 3 Low 1 Very Low 1 Rainfall Very Low 1 7 Low 3 Medium 5 High 6 Very High 7 Soil Calcareous loamy 17 17 Fine clay 5 Loamy 10 Lineament Density Very High 12 12 High 10 Medium 9 Low 3 Very Low 1 Slope Very High 1 12 High 2 Medium 9 Low 10 Very Low 12 Groundwater Potential Zone Map Remote sensing technique provides a complete realistic database on resource, while the GIS technique helps in storage and analysis of spatial database in a computer system Therefore, the application of remote sensing and GIS tools and techniques carried out for the identification of groundwater potential zones and recharge zone. Based on the results of the study, the groundwater potential zone map is generated in Arc GIS 10.5 and ERDAS. Multi-influencing factor technique (MIF) is deliberately hired to assess the influence of all the thematic layers for the identification of different groundwater potential zones. Total nine thematic layers viz., soil, slope, lineament, geology, geomorphology rainfall, drainage density LULC and topographic elevation are considered for the study of groundwater potential zoning. The groundwater potential zones are very high, high, moderate, poor, and very poor. The very high area covers about 442.58 sq km, high area covers about 1266.45 sq km, moderate area covers 1736.51 sq km, poor 755.04 sq km and very poor covers 430.66 sq km. Conclusion The proper understanding of groundwater resources is essential for the recharge and management of groundwater. This study provides an updated summary of groundwater potential approaches based on a comprehensive literature assessment. Various researchers' findings indicate that the identification of groundwater potential zones necessitates the construction of distinct thematic layers. The potential zones are obtained by using Remote Sensing and GIS which requires a thorough evaluation of the weightage. This study utilized geospatial techniques, including Multi-Influencing Factors (MIF), remote sensing, and GIS, to assess groundwater potential zones in Madhya Pradesh's semi-arid region. Key factors like geology, geomorphology, drainage density, slope, land use, and rainfall were integrated with groundwater level data. The results categorized areas into five classes: very high, high, moderate, poor, and very poor. The dominant category was moderate, covering approximately 37.50% (1736.51 sq km) of the total area, followed by good (27.35%), poor (16.30%), very good (9.56%), and very poor (9.30%) groundwater potential zones. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5750566","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":396681649,"identity":"7df6759f-38dc-4787-9d5b-ae8b7ede31d1","order_by":0,"name":"Dr. Priyamvada M.","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYFACxgYIfYD9wOEfFUAGM3MDsVp4Eh8znAFpgYkQBAcYjI0Z25ANwQHMpQ+3SfzMOSzPd/xAmnThvNpo/naglh8V23BqsexLbJPs3XbYcOaZxGPSM7cdz51xmLGBsefMbZxaDM4wtknwbktj3HAgIQ3IOJbbANTCzNiGX4vk321p9hvOPzCT4J1zLHc+MVqkebfZJG64kWBszNtQk7uBkBbLHsZma9ltNskzb7xJfDjj2IHcjUAtB/H5xZyH/eHNt9skbPvOpx848KGmLnfe+cMHH/yowOMwBgYWCST+YTB5AKd6iBbmD0j8OnyKR8EoGAWjYIQCAPkqZHs7fh0CAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0005-5694-4873","institution":"","correspondingAuthor":true,"prefix":"Dr.","firstName":"Priyamvada","middleName":"","lastName":"M.","suffix":""},{"id":396681650,"identity":"aeb80f00-1852-42ef-b26b-7ef29ef34952","order_by":1,"name":"Dr. M. K. Awasthi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"Dr.","firstName":"M.","middleName":"K.","lastName":"Awasthi","suffix":""}],"badges":[],"createdAt":"2025-01-02 09:27:15","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-5750566/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5750566/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73091187,"identity":"e537f5be-222d-4ffc-86b8-c6a2c671f6c4","added_by":"auto","created_at":"2025-01-06 15:34:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":204397,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of the study area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5750566/v1/67148ef5cc1954e95ff907fb.png"},{"id":73091388,"identity":"2ee1e527-5860-495e-9f9d-78e3f33af474","added_by":"auto","created_at":"2025-01-06 15:42:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":16837,"visible":true,"origin":"","legend":"\u003cp\u003eProcedure for identification of groundwater recharge sites\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5750566/v1/07eda6e9c0bdc55c5e9a55a8.png"},{"id":73091173,"identity":"42fecb3d-f004-4942-b3f2-edabfb176d4e","added_by":"auto","created_at":"2025-01-06 15:34:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":198324,"visible":true,"origin":"","legend":"\u003cp\u003eGeology map of malwa region\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5750566/v1/f22778c7ab3ac69843f0af7d.png"},{"id":73091628,"identity":"4a1a7e27-6afc-4213-8c2f-9c90e46f6bb0","added_by":"auto","created_at":"2025-01-06 15:50:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":251053,"visible":true,"origin":"","legend":"\u003cp\u003eGeomorphology map of malwa region\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5750566/v1/3a7bde54ee356b8621379377.png"},{"id":73091174,"identity":"b9d93ccd-df76-491e-a324-fcdeaea2de85","added_by":"auto","created_at":"2025-01-06 15:34:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":216929,"visible":true,"origin":"","legend":"\u003cp\u003eSoil map of Malwa region\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5750566/v1/f8d8f24079b573b3cad5564b.png"},{"id":73091176,"identity":"240aabb9-983f-480e-ad1b-2410d1241a7b","added_by":"auto","created_at":"2025-01-06 15:34:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":233299,"visible":true,"origin":"","legend":"\u003cp\u003eSlope map of Malwa region\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5750566/v1/68b5a12adb23d74c438da3ee.png"},{"id":73091192,"identity":"089db5a6-641e-4aeb-9f66-54b541ce7e91","added_by":"auto","created_at":"2025-01-06 15:34:30","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":300372,"visible":true,"origin":"","legend":"\u003cp\u003eLand Use/ Land Cover map of Malwa region\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5750566/v1/dccf8e1b6aabc58743f06a8b.png"},{"id":73091208,"identity":"5aee23ba-eeb1-4840-b3da-f0f7a42a9378","added_by":"auto","created_at":"2025-01-06 15:34:31","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":190217,"visible":true,"origin":"","legend":"\u003cp\u003eLineament map of Malwa region\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5750566/v1/3f38b93d99d5080e6ae4bff7.png"},{"id":73091197,"identity":"04afbc29-d43d-4a19-a7b8-ce048bc04e0e","added_by":"auto","created_at":"2025-01-06 15:34:31","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":163523,"visible":true,"origin":"","legend":"\u003cp\u003eStream order map of Malwa region\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5750566/v1/0fed6aa66094512e0fa3d490.png"},{"id":73091178,"identity":"27384859-4104-4178-8fd7-3c812aaccd07","added_by":"auto","created_at":"2025-01-06 15:34:29","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":254806,"visible":true,"origin":"","legend":"\u003cp\u003eDrainage density map of Malwa region\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-5750566/v1/312fd465144b32a4b4339de5.png"},{"id":73091188,"identity":"92ea9698-e992-43ad-988a-abe6382ac3ea","added_by":"auto","created_at":"2025-01-06 15:34:30","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":224983,"visible":true,"origin":"","legend":"\u003cp\u003eDrainage density map of Malwa region\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-5750566/v1/446b25c1da728f2447fa7bdc.png"},{"id":73091380,"identity":"d529df5c-ee21-4e73-aeb1-091a7a943a35","added_by":"auto","created_at":"2025-01-06 15:42:30","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":266379,"visible":true,"origin":"","legend":"\u003cp\u003eGroundwater potential map of malva region\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-5750566/v1/b588d3b7174a1e4d36299806.png"},{"id":73093096,"identity":"8573d7f3-2b48-448d-b70f-8829a63ebc56","added_by":"auto","created_at":"2025-01-06 16:00:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2723897,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5750566/v1/ba50404f-9c77-4570-9e26-3b996dc285b0.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGeospatial based assessment of Groundwater Potential Zones using MIF technique in a watershed of Malwa region, Madhya Pradesh, India\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWater is the main constituent of the Earths hydrosphere. It is very important ingredient forms of life, despite providing neither food, energy nor organic micronutrients. All the living organisms are dependent on water. One of the most vital resources in the world is potable water. Potable water is available in different forms like surface water, groundwater, river, lakes etc. in all areas. Although there is a plenty of water on Earth, water has not been available when and where it is in need. India is fast moving away from being a predominantly agricultural society. The urban industrial sector and ecological needs are silently acquiring. Exploitation of the groundwater has increased greatly in the last two to three decades in India. (Verghese \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). The groundwater scenario in India, which receives a substantial amount of rainfall, is not very encouraging primarily due to the imbalance between recharge and groundwater exploitation. Although transportation of fresh water may be expensive and the supply may not always be sustainable. The rainfall occurrence is highly seasonal in many parts of India. In arid and semi-arid regions of India the only source of freshwater is rainfall. Therefore, such regions experience the droughtful condition due to insufficiency of water availability before monsoon season. In such conditions, groundwater plays and dominant role in consumption of water for agriculture, drinking and other socio-economic usage. Development of economical agriculture highly depends on groundwater in many semi-arid climatic regions (Senapati and Das, 2022). Hence new research has been carried out on groundwater recharge zones in the countries like China, Iran, Saudi Arabia, Tiwan etc. and found that the groundwater resource is reducing (Shao et al. 2020; Luo et al. 2020; Arabameri et al. 2020; Rahmati et al. 2016; Mallick et al. 2019; Zabihi et al. 2020; Manap et al. 2011; Serele et al. 2019; Yeh et al. 2016; Owolabi et al. 2020). Geophysical data with geospatial data have been integrated by many researchers (Srivastava et al. 2006 and Rashid et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The surface hydrological features like soil, slope, drainage, geology etc., plays an important role in recharging the groundwater. Groundwater recharge is one practice option to utilize unused water in the replenishment of aquifers. Surface waterbodies like river, lakes, ponds etc. can act as recharge zone in enhancing the groundwater potentials. Therefore, identification and development of groundwater potential zones is a main aim to increase the groundwater aquifers. Geospatial techniques play a vital role in exploration of groundwater. To establish a good harmony between groundwater and runoff, rainfall preservation strategies for semi-arid region (Biswas et al 2012), groundwater potential mapping is required (Asadi et al \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Sunitha et al 2016; Duan et al 2016; nag and Ray 2013). Easy understanding, data handling and processing made researchers comfortable to use Remote sensing and GIS tools. Remote Sensing data are processed in GIS software (Ramasamy \u0026amp; Anbazhagan \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). This study deals with the multi-Influencing factor used to generate groundwater potential map in semi-arid regions of Malwa region in Madhya Pradesh. From these results a soil and water conservation techniques are suggested from the future point of view.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eMadhya Pradesh is an agricultural state. It is situated in the central part of India. It has almost all the climatic conditions. About 70.31% of the population lives in rural area while 29.69% lives in urban area. The main occupation of the people in this malwa region is agriculture. The study area is located in the semi-arid zones of Madhya Pradesh which lies in north west part of the state. The geographic area is about 4516.40 Km\u003csup\u003e2\u003c/sup\u003e and lies between north latitude 24\u003csup\u003eo\u003c/sup\u003e44\u0026rsquo;59\u0026rdquo;- 23\u003csup\u003e0\u003c/sup\u003e51\u0026rsquo;27\u0026rdquo; and east longitude 75\u003csup\u003e0\u003c/sup\u003e00\u0026rsquo;31\u0026rdquo;-74\u003csup\u003e0\u003c/sup\u003e46\u0026rsquo;40\u0026rdquo;. The topographic elevation ranges from 90\u0026ndash;239 meters. Geologically, major parts of the study area is occupied by Deccan Trap, basalts except narrow patch of alluvium and sedimentary rocks of Vindhyans super group in isolated patches. Major parts of the district having gently slope ranging from 317 m and 579 m above mean sea level. The normal maximum temperature receives during may is approximately 40\u003csup\u003e0\u003c/sup\u003eC and minimum during month of January is approximately 9.5\u003csup\u003e0\u003c/sup\u003eC. The normal mean monthly maximum temperature is 31.6\u003csup\u003e0\u003c/sup\u003eC and daily mean minimum temperature exceeds 19\u003csup\u003e0\u003c/sup\u003eC. The summer is the driest period of the year. The study area receives rainfall during June to September. About 90% of the annual rainfall receive during monsoon season only. The relative humidity exceeds 87% in monsoon season. The average annual wind velocity is about 9.2km/hr. Most of the streams in the study area remain dry throughout the year except the monsoon season (June to Sept). Throughout the year the air is quite dry and hot. The physiography is dominated by deccan trap and hence the soil is loamy and clayey. The depth of water level varies from 13.82 in pre monsoon and 3.23 in post-monsoon. Therefore, maximum population is working in agriculture activities. The main crop of this area is wheat, gram, mustered in rabi season and jawar, rice, urad and moong in kharif season. The drainage streams meet to the Gandhi Sagar dam in the east of the study area. Location map of study area is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collected and software used\u003c/h3\u003e\n\u003cp\u003eThe study deals with the use of GIS for the preparation, handling and processing of different thematic layers. Shuttle Radar Topography Mission (SRTM) 30-m resolution data were used for identification of slope, drainage and drainage density of the study area. Using the data from SRTM-DEM, the flow direction map was created first, and then the flow accumulation map and, lastly, the streams were created with the help of spatial analysis tool in Arc GIS 10.5. The drainage density map was created using line density tool. To demarcate the study area of Malwa region the Arc SWAT 2012.10.21 version of software is used. The lineament map was digitised from open software Bhuvan at 1:50,000 scale. The Soil map was prepared from scanned map provided by National Bureau of Soil Survey \u0026amp; Land Use Planning (NBSS\u0026amp;LUP), Nagpur. Land Use Land Cover (LULC) image was collected from Sentinel-2B, Google Earth and Bhuvan (LISS_IV and Resource SAT- II). The 2-,3-,4- and 8-layer stacking was processed in ERDAS IMAGINE 9.3 software and supervised classification was done to generate Land Use Land Cover map. The geology and geomorphology map were also digitized from Bhukhosh. Net groundwater availability data was collected from Water resource department, Madhya Pradesh for the validation of groundwater recharge sites. Groundwater data and the rainfall data were collected from Madhya Pradesh Water Resource Department (MPWRD, Central Ground Water Board (CGWB)) and Indian Meteorological Department (IMD) Pune. The (World Geodetic System) WGS 84 datum and (Universal Transverse Mercator) UTM zone 43N, WGS_1984_UTM_43N projection has been used to georeferenced these maps.\u003c/p\u003e\n\u003ch3\u003eThematic layer\u003c/h3\u003e\n\u003cp\u003eThe thematic layers of slope, soil, drainage density, geology, geomorphology, lineament density and land use/land cover were prepared by using the data. The detailed procedure to identify the groundwater recharge sites are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe thematic layers that are affecting the groundwater recharge are slope, drainage density, geology, geomorphology, lineament density, land use/land cover, soil texture and rainfall. These agents were considered to assess the groundwater potential zone and then the groundwater recharge sites. The slope and elevation were produces by SRTM-DEM data. The drainage density and lineament density were produced by using drainage and lineament layers respectively. 30 years (1991\u0026ndash;2021) rainfall data were taken to generate thematic maps for the study area. After the thematic maps are reclassified, the weightage was assigned to all the layers using multi-influencing factors (MIF) technique. This technique estimates the individual weight that has been given to each thematic layers which is considered like soil, geology, drainage density etc. (Poddar et al. 2020). The effects can be closely observed from the inter-relationship between the layers. After observing the strong and sturdy correlation between the factors, score 1 is assigned. If the impact is minimum and showing the poor relationship between the factors, score assigned is 0.5. Summation of all the weights will give the independent weights of each impacting agent (Pande et. al, 2021). All the relations are weighted according to the direct and indirect impact on groundwater recharge. The sub-classes are classified based on relative relationships (Pande et al., 2021; Poddar et. al., 2020). With the help of this relation the proposed score or the individua weightage is calculated by the following equation.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eProposed score = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{(X+{X}^{{\\prime\\:}})}{(X+{X}^{{\\prime\\:}})}\\times\\:100\\)\u003c/span\u003e\u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;..(Eq.\u0026nbsp;1.)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cem\u003eX\u003c/em\u003e is a major impact and \u003cem\u003eX'\u003c/em\u003e is a minor impact.\u003c/p\u003e \u003cp\u003eInter-relationship between the layers impacting on groundwater recharge zones\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eThe groundwater potential zones were delineated by using various thematic layers. In order to get the results of semi-arid region in Malwa, the thematic maps are made and weighted overly is carried out. The multi-influencing factor technique is used to generate the Groundwater potential zones. The results pertaining to prepare drainage density, lineament density, elevation map, slope map, soil map, geology map, geomorphology map, rainfall map and land use land cover map.\u003c/p\u003e\n\u003ch3\u003ePreparation of thematic maps\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eGeology\u003c/h2\u003e\n \u003cp\u003eGroundwater depends on the occurrence of rock and movement of water. Hence geology is key feature to understand. (Thapa et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e, Shaban et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e, raju et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e, Balaji et al. \u003cspan class=\"CitationRef\"\u003e2019b\u003c/span\u003e, Ghasemizadeh et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). In the study area, the geologic formation of Alluvium, Basalt, Laterite, sandstone and shale was found. Basalt is dominating geology in this study area, as it covers 83% of the total area. Geology map is shown in the Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGeomorphology\u003c/strong\u003e The underground water moves according to the geomorphic units which is under the surface (Poddar et al., 2020). Thus, geomorphology map plays an important role in finding out the groundwater potential zones (Shinde et al., 2022). The geomorphology landform was detected and generated from the satellite data using visual interpretations and the survey of India toposheet maps. It was found that 92% (4246.81 Sq Km) is covered with Pediment pediplain complex followed by moderately desiccated plateau (2%) and other formations which is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eSoil map\u003c/h3\u003e\n\u003cp\u003eSoil type has main control of water percolation and infiltration process to the aquifer water is due to soil texture (Raju et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Soil having high infiltration rate will be best suited to find the artificial recharge sites. It is found that the study area is covered with mainly three types of soil texture i.e., fine clay, loamy and calcareous loamy. It was found that the most dominating soil texture is fine clay (57%) covers area of 2582.16 Sq Km followed by Calcareous loamy (39%) covers area of about 1792.1 Sq. Km. and loamy (4%) covers area of 181.2 Sq. Km. The soil map is shown in the Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eSlope map\u003c/h3\u003e\n\u003cp\u003eTo get the groundwater potential zones, slope is very important key parameter. Surface water is directly affected by the process of infiltration in the study area. (Rajaveni et al., 2015). The slope map was generated using SMRT-30 m DEM data in Arc GIS. The slope varies from 341\u0026ndash;651 meters above mean sea level. The present study of slope is classified in five classes viz., very low, low, moderate, high and very high. It was found that the maximum area comes under very low slope of terrain. About 63% (2865.29 Sq Km) of the total study area comes under very low class. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e shows the details of the slope map.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eLand Use / Land Cover\u003c/h2\u003e\n \u003cp\u003eLand use changes depends upon the manmade growth with respect to industrialization. In this malwa region it is discovered that the main occupation of the people is agriculture. Therefore, maximum land is under the agricultural land, i.e, crop land and fallow land. The classes are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. About 34% and 29% of the total study area is covered with fallow land and agriculture land respectively. In total 63% of the total study area is covered with crop land.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eLineament density\u003c/h2\u003e\n \u003cp\u003eLinear feature like fracture and joints are present below the earth surface of any area. Water holding and water transmission capacity of these lineaments is very good as they increases the porosity, hydraulic conductivity and permeability of the land surface. Thus, presence of lineaments in particular area is considered helpful for the groundwater recharge. The lineament density of the malwa region is generated through lineament map. The lineament map is shown in the Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eDrainage density\u003c/h2\u003e\n \u003cp\u003eFor and watershed area, the ratio of total distance travelled by all the major and minor rivers to the total surface area is called drainage density. It is mainly expressed as km/km\u003csup\u003e2\u003c/sup\u003e. To evaluate the groundwater potential zones drainage density is an important factor. Higher is the drainage density value, higher is the runoff and vice versa, this ration directly affects the recharge capability of the particular area. The highest stream order in the study area was found as 4. The detailed stream order and the drainage density is shown in the map. (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e respectively).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eElevation\u003c/h2\u003e\n \u003cp\u003eThe elevation is developed using SRTM DEM- 30m using spatial analysis tools. It was observed that the elevation lies between 371 to 579 meters (MSL) in the study area. The elevation map is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eWeightage calculation\u003c/h2\u003e\n \u003cp\u003eMulti-influencing factor technique is used in this study. The major and the minor effect of the thematic maps are observed carefully and proposed score is calculated. Every thematic layer has weightage classification. Weighted value of each class is referred from different literature review (Masitoh et al, (2022); Senanayake et. al, (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e); Maity and Mandal (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); Mangesh et. al, (2012); Acharya et. al., (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); Raju et. al., (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); Etikala et al., (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Weightage given to each major impact is 1 and that of minor impact is 0.5. The major and the minor effect is shown in the Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. From the Eq. 1 the proposed score is calculated and weightage is assigned to every thematic map. Thus, forming the groundwater potential zone map.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMajor and minor effect of thematic layer and the proposed score\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMajor (X)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMinor (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{X}}^{\\varvec{{\\prime\\:}}}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{X}+{\\varvec{X}}^{\\varvec{{\\prime\\:}}}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProposed score\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026thinsp;+\u0026thinsp;1\u0026thinsp;+\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeomorphology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026thinsp;+\u0026thinsp;1\u0026thinsp;+\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLand Use Land Cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrainage Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u0026thinsp;+\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLineament Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026thinsp;+\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026thinsp;+\u0026thinsp;1\u0026thinsp;+\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u0026thinsp;+\u0026thinsp;0.5\u0026thinsp;+\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElevation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026thinsp;+\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Sigma; (X\u0026thinsp;+\u0026thinsp;X\u0026apos;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eWeightage and rating of the sub classes influencing the groundwater potential zones.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSub-classes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" style=\"width: 11.4861%;\"\u003e\n \u003cp\u003eRating\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 9.8564%;\"\u003e\n \u003cp\u003eWeightage\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eAlluvium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eBasalt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eLaterite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eSandstone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eShale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeomorphology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eAlluvium Plan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eFlood Plan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eLow Dissected Hill and Valley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eLow Dissected Platue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eModerately Dissected Hill and Valley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003ePedi plane complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLand Use Land Cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eAgriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eBuilt-up area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eForest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003ePasture Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eWaste Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eWaterbodies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElevation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003e0\u0026ndash;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003e1\u0026ndash;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003e2\u0026ndash;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003e3\u0026ndash;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrainage Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eVery High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eVery Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eVery Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eVery High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eCalcareous loamy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eFine clay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eLoamy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLineament Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eVery High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eVery Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eVery High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 39.0437%;\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eVery Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 8.4284%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 12.9141%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eGroundwater Potential Zone Map\u003c/h2\u003e\n \u003cp\u003eRemote sensing technique provides a complete realistic database on resource, while the GIS technique helps in storage and analysis of spatial database in a computer system Therefore, the application of remote sensing and GIS tools and techniques carried out for the identification of groundwater potential zones and recharge zone. Based on the results of the study, the groundwater potential zone map is generated in Arc GIS 10.5 and ERDAS. Multi-influencing factor technique (MIF) is deliberately hired to assess the influence of all the thematic layers for the identification of different groundwater potential zones. Total nine thematic layers viz., soil, slope, lineament, geology, geomorphology rainfall, drainage density LULC and topographic elevation are considered for the study of groundwater potential zoning. The groundwater potential zones are very high, high, moderate, poor, and very poor. The very high area covers about 442.58 sq km, high area covers about 1266.45 sq km, moderate area covers 1736.51 sq km, poor 755.04 sq km and very poor covers 430.66 sq km.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe proper understanding of groundwater resources is essential for the recharge and management of groundwater. This study provides an updated summary of groundwater potential approaches based on a comprehensive literature assessment. Various researchers' findings indicate that the identification of groundwater potential zones necessitates the construction of distinct thematic layers. The potential zones are obtained by using Remote Sensing and GIS which requires a thorough evaluation of the weightage. This study utilized geospatial techniques, including Multi-Influencing Factors (MIF), remote sensing, and GIS, to assess groundwater potential zones in Madhya Pradesh's semi-arid region. Key factors like geology, geomorphology, drainage density, slope, land use, and rainfall were integrated with groundwater level data. The results categorized areas into five classes: very high, high, moderate, poor, and very poor. The dominant category was moderate, covering approximately 37.50% (1736.51 sq km) of the total area, followed by good (27.35%), poor (16.30%), very good (9.56%), and very poor (9.30%) groundwater potential zones.\u003c/p\u003e \u003cp\u003eFuture research should focus on precise placement of rainwater harvesting structures based on geological and geomorphological factors. These efforts aim to ensure sustainable groundwater management in the region.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAntony Ravindran A (2012) Azimuthal Square Array Resistivity Method and Groundwater Exploration in Sanganoor, Coimbatore District, Tamilnadu, India, Res. J. recent sci, 1(4), 41\u0026ndash;45\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsadi et al (2007) Remote Sensing and GIS Techniques for Evaluation of Groundwater Quality 512 in Municipal Corporation of Hyderabad (Zone-V). India. 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New Delhi: CentreforPolicy Research.pp.446\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeh H, Lee C, Hsu K, Chang P (2009) GIS for the assessment of the groundwater recharge. potential zone Environ Geol 58:185\u0026ndash;195\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYousif LD (2022) Groundwater Potential Zones Delineation Using AHP and GIS Techniques, for the Al-Ajeej Drainage Basin, Northwest Iraq. Iraqi Bull Geol Min 18(1):95\u0026ndash;59. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.7018209\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.7018209\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Remote sensing, GIS, Multi-Influencing Factor, Groundwater recharge, Groundwater potential zones","lastPublishedDoi":"10.21203/rs.3.rs-5750566/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5750566/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWater is a major natural resource and a key constituent of all living beings. Water is available in two introductory forms i.e. surface water and groundwater. One of the most important accumulated resources in the world is groundwater, which is inversely distributed around the world. To boost the groundwater-recharge is one of the primary hydrological parameters for assessment, budgeting, management, and modelling of ground water resources. Although information and data regarding recharge rate is vital for recharge assessment of any region, determination of this parameter is not easy and straigntforward. The current study has been conducted in northwest part of Madhya Pradesh. The Neemuch and Mandsaur district comes under semi-arid zones and faces the problem of scarcity of water annually. Thus study aims to assess the groundwater recharge zones using an integrated approach of remote sensing and geographical information system (GIS). The parameters considered for identifying the groundwater potential zone of geology, slope, drainage density, soil, geomorphology and lineament density. These thematic maps were generated using the SRTM DEM, Sentinel-2 Imaginary and Survey of India (SOI) toposheets of scale 1:50000 and integrated them to identify the groundwater potential of the study area. Appropriate weightage factors were assigned for each class of these parameters. For the various geomorphic units, weightage factors were assigned based on their capability to store ground-water using multi-influencing factor (MIF). This procedure was repeated for all the other layers and resultant layers were re-classified. After reclassification, the layers were then united to demarcate zones as very good, good, moderate, poor and very poor. The assessment of groundwater potentiality information using RS \u0026amp; GIS could be used for effective identification of suitable locals for birth of potable water for rural populations.\u003c/p\u003e","manuscriptTitle":"Geospatial based assessment of Groundwater Potential Zones using MIF technique in a watershed of Malwa region, Madhya Pradesh, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-06 15:34:24","doi":"10.21203/rs.3.rs-5750566/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":"c9624dfa-225a-4a6c-89be-605874d0272b","owner":[],"postedDate":"January 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42262336,"name":"Agricultural Engineering"}],"tags":[],"updatedAt":"2025-01-06T15:34:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-06 15:34:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5750566","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5750566","identity":"rs-5750566","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}