Machine Learning Algorithms as State-of-the-Art Tools for Prediction of Climatic Conditions: With Focus on Global Land Temperatures

Machine Learning Algorithms as State-of-the-Art Tools for Prediction of Climatic Conditions: With Focus on Global Land Temperatures | 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 Machine Learning Algorithms as State-of-the-Art Tools for Prediction of Climatic Conditions: With Focus on Global Land Temperatures Thomas James Wanyama This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6172411/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 Temperatures in various places are drastically increasing or reducing. Skyrocketing land temperatures are expected to change the frequency and intensity of current land temperature extremes. Determining the evolving trends in land temperatures is thus immeasurable. Most importantly, global land temperatures can be forecasted using machine learning algorithms. In our study, polynomial regression and artificial neural networks were used to predict global land temperatures for the next 100 years. Scenario analysis was also done using business-as-usual, moderate mitigation, and aggressive mitigation approaches. All data visualizations of the historical data, predicted data, and data from scenario analysis were done with the aid of MATLAB R2024a. Predictions from polynomial regression revealed that a rapid increase in global land temperatures was to occur from 2012 to 2032 while a rapid increase in global land temperatures was predicted to occur from 2012 to 2032 followed by a gentle rise from 2032 to 2100 based on the artificial neural networks’ prediction. The results of the scenario analysis revealed a dire need for aggressive mitigation to be adopted and implemented as soon as possible. Despite the predictions made by the two algorithms, predictions by artificial neural networks were more reliable compared to those obtained from polynomial regression. Artificial Intelligence and Machine Learning Climate Analysis and Modeling artificial intelligence artificial neural networks machine learning polynomial regression predictive modeling scenario analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction It is key to mention that since time immemorial, climate changes have been noticed especially by indigenous communities. These climate changes are inexorable, with their effects like the warming of the atmosphere and ocean, changes in the amount and distribution of precipitation, rise in sea level, and increase in the concentration of greenhouse gases (Mishra and Sadhu 2023 ). Other than these climate change parameters, other parameters such as global land temperatures are skyrocketing. Temperature changes are one of the adverse effects of climate change (Zhang et al. 2024 ). Escalation in land temperatures is expected to alter the frequency and intensity of current land temperature extremes (Das et al. 2023 ), which in the long run may affect food security (Mehrabi et al. 2022 ), energy demand (Barreca et al. 2022 ), and public health (Ebi et al. 2021 ). It is worth noting that the Global Historical Climate Network is dependent on global land temperatures which are divided into various sections such as temperatures by major cities, states, and countries (Kaur and Randhawa 2018 ). It is thus of immense importance that climate departments across the globe determine the changing trends in temperature partly due to the fact that the temperatures of various places have either risen or dropped (Kaur and Randhawa 2018 ). All these various changes in these temperature ranges are of great concern and need to be attended to by the Global Historical Climate Network department (Kaur and Randhawa 2018 ) since rising temperatures are known to pose a threat to the ecosystem (Pande et al. 2023 ). These rising temperatures are driven by the need for industrialization and other anthropogenic activities by humans (Akter et al. 2021 ). Interestingly, global land temperatures can be predicted using state-of-the-art methodologies such as machine learning algorithms. Many of these algorithms have been available for decades (Huntingford et al. 2019 ). The use of these algorithms is formidable as it allows systems to learn and adjust automatically from the former experiences (Sarker et al. 2020 ). Some of these algorithms include feature engineering, dimensionality reduction, regression, classification analysis, data clustering (Han et al. 2022 ), and artificial neural networks (Sarker 2021 ). These algorithms have shown good performance in predicting climate variables and other datasets (Elbeltagi et al. 2023 ; Pande et al. 2023 ). In this research work, two algorithms; polynomial regression and artificial neural networks, were considered and thus utilized to help forecast global land temperatures for the next 100 years. 2. Materials and Methods 2.1 Data and its Features The dataset used in this study was obtained from the Climate Data Store website upon request. Climate data store gives access to crucial information regarding historical, present, and future climatic conditions. This information is regional, continental, and also global. The dataset obtained from this data source included various files such as; Global Average Land Temperatures by Country, Global Average Land Temperatures by State, Global Average Land Temperatures by Major City, and Global Average Land Temperatures by City. However, only the Global Average Land Temperatures by City dataset was considered for the current study. This dataset contained land temperatures recorded from 1743 to 2012. Within this data used, information on the date of data recording, Average Temperature, latitude, and longitude was included. 2.2 Data Cleaning This was done with the use of Matlab’s data cleaning tool. It entailed normalizing all the data and removing outliers. 2.3 Predictive Modeling This was done to simulate the future global land average temperatures of each city for the next 100 years. Two machine learning algorithms were used and these were polynomial regression and Artificial Neural Networks. During the predictive modeling, data preparation for the Artificial Neural Networks (ANN) was done by splitting the data into training and testing sets, creating and training the Artificial Neural Networks (ANN), making the predictions on the future global land temperatures, and then visualizing the prediction made. Evaluation was also done for the two machine learning algorithms using the mean squared error (MSE) to assess their performance. 2.4 Scenario Analysis This was done to provide knowledge for risk management, policy development, understanding uncertainties, and stakeholder engagement. Three approaches of scenario analysis were used and these were; business-as-usual (BAU) which put forth that current trends in climatic parameters such as global land temperatures can and will continue without significant interventions to combat climate change, moderate mitigation approach which put forth that implementing moderate policies to reduce emission was of supreme importance. The last approach was the aggressive which considered implementing strong policies to significantly reduce global land temperatures. 2.5 Optimization of machine learning algorithms This was done using least squares optimization to help provide information on the accuracy and efficiency of the predictions made from polynomial regression and Artificial Neural Networks. The algorithms were further evaluated by calculating the error between the observed and fitted temperatures. 2.6 Data Visualization All data visualizations of the historical data, predicted data and data from scenario analysis were done with the aid of MATLAB R2024a ( http://www.mathworks.com/ ) to help identify trends and patterns in the global average land temperatures. 3. Experimental Results and Discussion 3.1 Predictive modeling Historical data ; the data of global land temperatures from 1743 to 2012 was collected. This data was studied and visualized to provide empirical meaning of land temperatures in different areas. Graphs are shown below. Figure 1 shows the historical land temperatures that have been recorded since 1743 up to 2012. There have been some rises and drops in global land temperatures across the globe from 1743 to 2012. The rise and drop in global land temperatures across the globe from 1743 to 2012 is similar to the finding of another study (Kaur and Randhawa 2018 ) which highlighted that the temperature of various places has either drastically gone down or up. Another similar study has reported dramatic rises and drops in the earth's surface land temperatures (Rohde et al. 2013 ). Factors such as industrialization, pollution, urbanization, and anthropogenic activities may be attributable to the rise in global land temperatures (Akter et al. 2021 ). On the other side, the drop in land temperatures may be attributed to some actions that the human society has adopted which actions might have contributed to reducing land temperatures. Such actions may include efforts by bodies such as the Intergovernmental Panel on Climate Change which has provided political leaders with immense knowledge on scientific evaluations regarding climate change, its ramifications and risks, and also its mitigation (Shivanna 2022 ). Predicted global land temperatures ; In Fig. 2 , it is clear that in the next 100 years from now, a sharp increase in global land temperatures is expected based on predictions using the polynomial regression. According to Fig. 3 , a rapid increase in global land temperatures was predicted to occur from 2012 to 2032. After this, based on the predictions made by the same algorithm, a gentle rise in global land temperatures was forecasted to occur from 2032 to 2100. The findings from these two algorithms suggest that global land temperatures are expected to increase. This finding coincides with another study that stressed that land surface temperatures were to be extremely high in the coming years (Ripple et al. 2024 ). Another similar study has stressed that future years will almost certainly be even hotter (Vecellio et al. 2023 ). This increase in global land temperatures in the coming years may be attributed to the rapid increase in human and ruminant livestock populations at approximately 200,000 and 170,000 per day respectively (Ripple et al. 2024 ). Additionally, the fact that climatic conditions across the globe are still changing away from what is needed for most of the world's population to thrive may also be attributable to the increase of global land temperatures in the coming years (Vecellio et al. 2023 ). 3.2 Scenario analysis According to Fig. 4 , three approaches were hypothesized that can help in the mitigation of global land temperatures. It is clear from the prediction made that if aggressive mitigation is adopted, then global land temperatures will be expected to increase gradually in the coming years. If moderate mitigation is adopted, according to the predictions made, the global land temperatures will be expected to increase more gradually than when aggressive mitigation is adopted. Lastly, it is indicative that if the business-as-usual (BAU) approach is instead adopted, global land temperatures will rapidly increase in the coming years. Optimization of machine learning algorithms Based on the findings in table 1, it is clear that the two algorithms used in our study performed well an implication that the predictions made for global land temperatures are accurate and reliable. However, in terms of which algorithm outperformed the other, it was the Artificial Neural Networks algorithm since its mean squared error (MSE) value (104.0783) was lower than that of the Polynomial Regression model. The lower MSE value of the Artificial Neural Networks also implied that it accurately captured the underlying patterns in the data it was exposed to. Table 1: Mean squared error (MSE) values for predictions done using Artificial Neural Networks and Polynomial Regression Sr. No Machine Learning algorithms Mean Squared Error (MSE) 1 Artificial Neural Networks 104.0783 2 Polynomial Regression 107.9263 4. Conclusion and recommendation The findings of the present study are pivotal in helping policymakers and other bodies tasked with designing climate change combatting measures. From our findings, it is clear that global land temperatures will increase in the next 100 years. This increase was predicted to escalate based on the polynomial regression algorithm, while for the artificial neural networks, a sharp increase in the global land temperatures was predicted from 2012 to 2032 followed by a gradual increase from 2032 to 2100. These findings prove the information of global warming increasing day by day due to the increase in temperature. Our scenario analysis results also showed that extremely high global land temperatures can only be minimized if aggressive mitigation measures are quickly adopted. Some of these measures have already been put in place but not fully implemented in several countries. Most importantly, some countries that participate in the Conference of the Parties to the United Nations Framework Convention on Climate Change (COP) which are always organized to devise measures to combat climate change are still not doing much to implement climate change combatting measures. Based on all these results, it is of the utmost importance to emphasize that machine learning algorithms are promising and reliable tools in helping to predict future climatic conditions. Our study thus proposes the immediate use and incorporation of machine learning in the global fight against climate change. Declarations Acknowledgments The author acknowledges the Climate Data Store for the raw data provided on global land temperatures by city. Your contribution to this work was pivotal for the present study Disclosure statement The author declares no competing interests. Data Availability statement The dataset used in this study is accessible using its DOI. Funding statement This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. ORCID Wanyama Thomas James: https://orcid.org/0009-0003-2235-1573 References Abate RS, Kronk EA (2013) Commonality among unique indigenous communities: An introduction to climate change and its impacts on indigenous peoples. Climate change and indigenous peoples. Edward Elgar Publishing, pp 3–18 Akter T, Gazi MY, Mia MB (2021) Assessment of land cover dynamics, land surface temperature, and heat island growth in northwestern Bangladesh using satellite imagery. Environ Process 8:661–690 Barreca A, Park RJ, Stainier P (2022) High temperatures and electricity disconnections for low-income homes in California. Nat Energy 7(11):1052–1064 Das P, Zhang Z, Ghosh S, Lu J, Ayugi B, Ojara MA, Guo X (2023) Historical and projected changes in Extreme High Temperature events over East Africa and associated with meteorological conditions using CMIP6 models. Glob Planet Change 222:104068 Ebi KL, Capon A, Berry P, Broderick C, de Dear R, Havenith G, Honda Y, Kovats RS, Ma W, Malik A (2021) Hot weather and heat extremes: health risks. Lancet 398(10301):698–708 Elbeltagi A, Pande CB, Kumar M, Tolche AD, Singh SK, Kumar A, Vishwakarma DK (2023) Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models. Environ Sci Pollut Res 30(15):43183–43202 Han J, Pei J, Tong H (2022) Data mining: concepts and techniques. Morgan kaufmann Huntingford C, Jeffers ES, Bonsall MB, Christensen HM, Lees T, Yang H (2019) Machine learning and artificial intelligence to aid climate change research and preparedness. Environ Res Lett 14(12):124007. 10.1088/1748-9326/ab4e55 Kaur S, Randhawa S (2018) Global land temperature prediction by machine learning combo approach. In: 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE. pp. 1–8 Mehrabi Z, Delzeit R, Ignaciuk A, Levers C, Braich G, Bajaj K, Amo-Aidoo A, Anderson W, Balgah RA, Benton TG (2022) Research priorities for global food security under extreme events. One Earth 5(7):756–766 Mishra V, Sadhu A (2023) Towards the effect of climate change in structural loads of urban infrastructure: A review. Sustain Cities Soc 89:104352 Pande CB, Moharir KN, Varade AM, Abdo HG, Mulla S, Yaseen ZM (2023) Intertwined impacts of urbanization and land cover change on urban climate and agriculture in Aurangabad city (MS), India using google earth engine platform. J Clean Prod 422:138541 Ripple WJ, Wolf C, Gregg JW, Rockström J, Mann ME, Oreskes N, Lenton TM, Rahmstorf S, Newsome TM, Xu C (2024) The 2024 state of the climate report: Perilous times on planet Earth. Bioscience 74(12):812–824 Rohde R, Muller RA, Jacobsen R, Muller E, Perlmutter S, Rosenfeld A, Wurtele J, Groom D, Wickham C (2013) A new estimate of the average Earth surface land temperature spanning 1753 to 2011. Geoinfor Geostat: Overv 1:1 Sarker IH (2021) Deep cybersecurity: a comprehensive overview from neural network and deep learning perspective. SN Comput Sci 2(3):154 Sarker IH, Furhad MH, Nowrozy R (2021) Ai-driven cybersecurity: an overview, security intelligence modeling and research directions. SN Comput Sci 2(3):173 Sarker IH, Kayes ASM, Badsha S, Alqahtani H, Watters P, Ng A (2020) Cybersecurity data science: an overview from machine learning perspective. J Big data 7:1–29 Shivanna KR (2022) Climate change and its impact on biodiversity and human welfare. Proc Indian Natl Sci Acad 88(2):160–171 Vecellio DJ, Kong Q, Kenney WL, Huber M (2023) Greatly enhanced risk to humans as a consequence of empirically determined lower moist heat stress tolerance. Proc Natl Acad Sci 120(42):e2305427120 Zhang X, Huang T, Wang W, Shen P (2024) Change of global land extreme temperature in the future. Glob Planet Change 242:104583. https://doi.org/10.1016/j.gloplacha.2024.104583 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6172411","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":425197810,"identity":"531a7279-ea56-4b21-9c98-0f645ca57a2f","order_by":0,"name":"Thomas James Wanyama","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0003-2235-1573","institution":"MAKERERE UNIVERSITY","correspondingAuthor":true,"prefix":"","firstName":"Thomas","middleName":"James","lastName":"Wanyama","suffix":""}],"badges":[],"createdAt":"2025-03-06 16:46:15","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6172411/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6172411/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78711417,"identity":"b1174c4e-1be4-423f-8e37-cc556fa4750b","added_by":"auto","created_at":"2025-03-18 01:21:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":630889,"visible":true,"origin":"","legend":"\u003cp\u003eHistorical land temperatures recorded from 1743 to 2012\u003c/p\u003e","description":"","filename":"Historicallandtemperaturesrecordedfrom1743to2012.png","url":"https://assets-eu.researchsquare.com/files/rs-6172411/v1/2973eefd50b4ac9429ca2934.png"},{"id":78711739,"identity":"bbd42ceb-e120-44f2-8676-c103835cbb63","added_by":"auto","created_at":"2025-03-18 01:29:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":377975,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted Global Land temperatures from 2012 up to 2100 using polynomial regression\u003c/p\u003e","description":"","filename":"PredictedGlobalLandtemperaturesfrom2012upto2100usingpolynomialregression.png","url":"https://assets-eu.researchsquare.com/files/rs-6172411/v1/6e4a06c585c5e736f101f9c6.png"},{"id":78711412,"identity":"1e29c855-93bc-42cc-a868-b999dbf9f5d6","added_by":"auto","created_at":"2025-03-18 01:21:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":562285,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted Global Land temperatures from 2012 up to 2100 using Artificial Neural Networks\u003c/p\u003e","description":"","filename":"PredictedGlobalLandtemperaturesfrom2012upto2100usingArtificialNeuralNetworks.png","url":"https://assets-eu.researchsquare.com/files/rs-6172411/v1/205887d86826854c71544b54.png"},{"id":78711740,"identity":"6ec0dc2f-f238-434d-81b0-649fd266b794","added_by":"auto","created_at":"2025-03-18 01:29:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1355032,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted global land temperatures over the years based on scenario analysis\u003c/p\u003e","description":"","filename":"Predictedgloballandtemperaturesovertheyearsbasedonscenarioanalysis.png","url":"https://assets-eu.researchsquare.com/files/rs-6172411/v1/6a5c0e0fa4bb1f429b76bee4.png"},{"id":78712399,"identity":"d58c1ad8-a4c5-4232-b7e7-3dfdee7bc235","added_by":"auto","created_at":"2025-03-18 01:53:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2715415,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6172411/v1/9cc38c95-2de1-41c1-98d6-0814e75f6032.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eMachine Learning Algorithms as State-of-the-Art Tools for Prediction of Climatic Conditions: With Focus on Global Land Temperatures\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIt is key to mention that since time immemorial, climate changes have been noticed especially by indigenous communities. These climate changes are inexorable, with their effects like the warming of the atmosphere and ocean, changes in the amount and distribution of precipitation, rise in sea level, and increase in the concentration of greenhouse gases (Mishra and Sadhu \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Other than these climate change parameters, other parameters such as global land temperatures are skyrocketing. Temperature changes are one of the adverse effects of climate change (Zhang et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Escalation in land temperatures is expected to alter the frequency and intensity of current land temperature extremes (Das et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which in the long run may affect food security (Mehrabi et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), energy demand (Barreca et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and public health (Ebi et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is worth noting that the Global Historical Climate Network is dependent on global land temperatures which are divided into various sections such as temperatures by major cities, states, and countries (Kaur and Randhawa \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is thus of immense importance that climate departments across the globe determine the changing trends in temperature partly due to the fact that the temperatures of various places have either risen or dropped (Kaur and Randhawa \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). All these various changes in these temperature ranges are of great concern and need to be attended to by the Global Historical Climate Network department (Kaur and Randhawa \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) since rising temperatures are known to pose a threat to the ecosystem (Pande et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese rising temperatures are driven by the need for industrialization and other anthropogenic activities by humans (Akter et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Interestingly, global land temperatures can be predicted using state-of-the-art methodologies such as machine learning algorithms. Many of these algorithms have been available for decades (Huntingford et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The use of these algorithms is formidable as it allows systems to learn and adjust automatically from the former experiences (Sarker et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Some of these algorithms include feature engineering, dimensionality reduction, regression, classification analysis, data clustering (Han et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and artificial neural networks (Sarker \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These algorithms have shown good performance in predicting climate variables and other datasets (Elbeltagi et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pande et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this research work, two algorithms; polynomial regression and artificial neural networks, were considered and thus utilized to help forecast global land temperatures for the next 100 years.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data and its Features\u003c/h2\u003e \u003cp\u003eThe dataset used in this study was obtained from the Climate Data Store website upon request. Climate data store gives access to crucial information regarding historical, present, and future climatic conditions. This information is regional, continental, and also global. The dataset obtained from this data source included various files such as; Global Average Land Temperatures by Country, Global Average Land Temperatures by State, Global Average Land Temperatures by Major City, and Global Average Land Temperatures by City. However, only the Global Average Land Temperatures by City dataset was considered for the current study. This dataset contained land temperatures recorded from 1743 to 2012. Within this data used, information on the date of data recording, Average Temperature, latitude, and longitude was included.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Cleaning\u003c/h2\u003e \u003cp\u003eThis was done with the use of Matlab\u0026rsquo;s data cleaning tool. It entailed normalizing all the data and removing outliers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Predictive Modeling\u003c/h2\u003e \u003cp\u003eThis was done to simulate the future global land average temperatures of each city for the next 100 years. Two machine learning algorithms were used and these were polynomial regression and Artificial Neural Networks. During the predictive modeling, data preparation for the Artificial Neural Networks (ANN) was done by splitting the data into training and testing sets, creating and training the Artificial Neural Networks (ANN), making the predictions on the future global land temperatures, and then visualizing the prediction made. Evaluation was also done for the two machine learning algorithms using the mean squared error (MSE) to assess their performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Scenario Analysis\u003c/h2\u003e \u003cp\u003eThis was done to provide knowledge for risk management, policy development, understanding uncertainties, and stakeholder engagement. Three approaches of scenario analysis were used and these were; business-as-usual (BAU) which put forth that current trends in climatic parameters such as global land temperatures can and will continue without significant interventions to combat climate change, moderate mitigation approach which put forth that implementing moderate policies to reduce emission was of supreme importance. The last approach was the aggressive which considered implementing strong policies to significantly reduce global land temperatures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Optimization of machine learning algorithms\u003c/h2\u003e \u003cp\u003eThis was done using least squares optimization to help provide information on the accuracy and efficiency of the predictions made from polynomial regression and Artificial Neural Networks. The algorithms were further evaluated by calculating the error between the observed and fitted temperatures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Data Visualization\u003c/h2\u003e \u003cp\u003eAll data visualizations of the historical data, predicted data and data from scenario analysis were done with the aid of MATLAB R2024a (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.mathworks.com/\u003c/span\u003e\u003cspan address=\"http://www.mathworks.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to help identify trends and patterns in the global average land temperatures.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Experimental Results and Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Predictive modeling\u003c/h2\u003e \u003cp\u003e \u003cb\u003eHistorical data\u003c/b\u003e; the data of global land temperatures from 1743 to 2012 was collected. This data was studied and visualized to provide empirical meaning of land temperatures in different areas. Graphs are shown below. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the historical land temperatures that have been recorded since 1743 up to 2012. There have been some rises and drops in global land temperatures across the globe from 1743 to 2012.\u003c/p\u003e \u003cp\u003eThe rise and drop in global land temperatures across the globe from 1743 to 2012 is similar to the finding of another study (Kaur and Randhawa \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) which highlighted that the temperature of various places has either drastically gone down or up. Another similar study has reported dramatic rises and drops in the earth's surface land temperatures (Rohde et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Factors such as industrialization, pollution, urbanization, and anthropogenic activities may be attributable to the rise in global land temperatures (Akter et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On the other side, the drop in land temperatures may be attributed to some actions that the human society has adopted which actions might have contributed to reducing land temperatures. Such actions may include efforts by bodies such as the Intergovernmental Panel on Climate Change which has provided political leaders with immense knowledge on scientific evaluations regarding climate change, its ramifications and risks, and also its mitigation (Shivanna \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePredicted global land temperatures\u003c/b\u003e; In Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it is clear that in the next 100 years from now, a sharp increase in global land temperatures is expected based on predictions using the polynomial regression. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, a rapid increase in global land temperatures was predicted to occur from 2012 to 2032. After this, based on the predictions made by the same algorithm, a gentle rise in global land temperatures was forecasted to occur from 2032 to 2100.\u003c/p\u003e\u003cp\u003eThe findings from these two algorithms suggest that global land temperatures are expected to increase. This finding coincides with another study that stressed that land surface temperatures were to be extremely high in the coming years (Ripple et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Another similar study has stressed that future years will almost certainly be even hotter (Vecellio et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This increase in global land temperatures in the coming years may be attributed to the rapid increase in human and ruminant livestock populations at approximately 200,000 and 170,000 per day respectively (Ripple et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, the fact that climatic conditions across the globe are still changing away from what is needed for most of the world's population to thrive may also be attributable to the increase of global land temperatures in the coming years (Vecellio et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Scenario analysis\u003c/h2\u003e \u003cp\u003eAccording to Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, three approaches were hypothesized that can help in the mitigation of global land temperatures. It is clear from the prediction made that if aggressive mitigation is adopted, then global land temperatures will be expected to increase gradually in the coming years. If moderate mitigation is adopted, according to the predictions made, the global land temperatures will be expected to increase more gradually than when aggressive mitigation is adopted. Lastly, it is indicative that if the business-as-usual (BAU) approach is instead adopted, global land temperatures will rapidly increase in the coming years.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOptimization of machine learning algorithms\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on the findings in table 1, it is clear that the two algorithms used in our study performed well an implication that the predictions made for global land temperatures are accurate and reliable. However, in terms of which algorithm outperformed the other, it was the Artificial Neural Networks algorithm since its mean squared error (MSE) value (104.0783) was lower than that of the Polynomial Regression model. The lower MSE value of the Artificial Neural Networks also implied that it accurately captured the underlying patterns in the data it was exposed to.\u003c/p\u003e \u003c/div\u003e\n\u003cp\u003eTable 1: Mean squared error (MSE) values for predictions done using Artificial Neural Networks and Polynomial Regression\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"564\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7021%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSr. No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.4255%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMachine Learning algorithms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47.8723%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Squared Error (MSE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7021%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.4255%;\"\u003e\n \u003cp\u003eArtificial Neural Networks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47.8723%;\"\u003e\n \u003cp\u003e104.0783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7021%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.4255%;\"\u003e\n \u003cp\u003ePolynomial Regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47.8723%;\"\u003e\n \u003cp\u003e107.9263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4. Conclusion and recommendation","content":"\u003cp\u003eThe findings of the present study are pivotal in helping policymakers and other bodies tasked with designing climate change combatting measures. From our findings, it is clear that global land temperatures will increase in the next 100 years. This increase was predicted to escalate based on the polynomial regression algorithm, while for the artificial neural networks, a sharp increase in the global land temperatures was predicted from 2012 to 2032 followed by a gradual increase from 2032 to 2100. These findings prove the information of global warming increasing day by day due to the increase in temperature. Our scenario analysis results also showed that extremely high global land temperatures can only be minimized if aggressive mitigation measures are quickly adopted. Some of these measures have already been put in place but not fully implemented in several countries. Most importantly, some countries that participate in the Conference of the Parties to the United Nations Framework Convention on Climate Change (COP) which are always organized to devise measures to combat climate change are still not doing much to implement climate change combatting measures. Based on all these results, it is of the utmost importance to emphasize that machine learning algorithms are promising and reliable tools in helping to predict future climatic conditions. Our study thus proposes the immediate use and incorporation of machine learning in the global fight against climate change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe author acknowledges the Climate Data Store for the raw data provided on global land temperatures by city. Your contribution to this work was pivotal for the present study\u003c/p\u003e\n\u003ch2\u003eDisclosure statement\u003c/h2\u003e\n\u003cp\u003eThe author declares no competing interests.\u003c/p\u003e\n\u003ch2\u003eData Availability statement\u003c/h2\u003e\n\u003cp\u003eThe dataset used in this study is accessible using its DOI.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFunding statement\u003c/h2\u003e\n\u003cp\u003eThis study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch2\u003eORCID\u003c/h2\u003e\n\u003cp\u003eWanyama Thomas James: https://orcid.org/0009-0003-2235-1573\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbate RS, Kronk EA (2013) Commonality among unique indigenous communities: An introduction to climate change and its impacts on indigenous peoples. Climate change and indigenous peoples. 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Glob Planet Change 242:104583. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gloplacha.2024.104583\u003c/span\u003e\u003cspan address=\"10.1016/j.gloplacha.2024.104583\" 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":"Makerere University","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":"artificial intelligence, artificial neural networks, machine learning, polynomial regression, predictive modeling, scenario analysis","lastPublishedDoi":"10.21203/rs.3.rs-6172411/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6172411/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTemperatures in various places are drastically increasing or reducing. Skyrocketing land temperatures are expected to change the frequency and intensity of current land temperature extremes. Determining the evolving trends in land temperatures is thus immeasurable. Most importantly, global land temperatures can be forecasted using machine learning algorithms. In our study, polynomial regression and artificial neural networks were used to predict global land temperatures for the next 100 years. Scenario analysis was also done using business-as-usual, moderate mitigation, and aggressive mitigation approaches. All data visualizations of the historical data, predicted data, and data from scenario analysis were done with the aid of MATLAB R2024a. Predictions from polynomial regression revealed that a rapid increase in global land temperatures was to occur from 2012 to 2032 while a rapid increase in global land temperatures was predicted to occur from 2012 to 2032 followed by a gentle rise from 2032 to 2100 based on the artificial neural networks\u0026rsquo; prediction. The results of the scenario analysis revealed a dire need for aggressive mitigation to be adopted and implemented as soon as possible. Despite the predictions made by the two algorithms, predictions by artificial neural networks were more reliable compared to those obtained from polynomial regression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Machine Learning Algorithms as State-of-the-Art Tools for Prediction of Climatic Conditions: With Focus on Global Land Temperatures","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-18 01:20:56","doi":"10.21203/rs.3.rs-6172411/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":"05ce086d-4965-44e6-b6e9-44a7fdbd3615","owner":[],"postedDate":"March 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45318622,"name":"Artificial Intelligence and Machine Learning"},{"id":45318623,"name":"Climate Analysis and Modeling"}],"tags":[],"updatedAt":"2025-03-18T01:20:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-18 01:20:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6172411","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6172411","identity":"rs-6172411","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}