[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119019-en":3,"doc-seo-119019-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119019,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Machine learning approaches for real-time forecasting of solar still distillate output - Decision tree prediction for optimized freshwater production","Solar stills offer an effective freshwater solution for water-scarce and remote regions, yet their efficiency is limited by highly variable climatic conditions. Traditional prediction techniques often fail to deliver consistent yield forecasts, creating a gap in optimizing solar still operation and resource use. This research develops and compares multiple machine learning models—linear regression, decision trees, random forest, support vector machines, and multilayer perceptron—using MAE and cross-validation with grid and randomized search. Results show decision trees as the best predictor for the dataset (MAE 5.43–5.74), improving real-time output forecasting accuracy and supporting better solar still design and optimization.","Environmental Challenges 13 (2023) 100779  \nContents lists available at ScienceDirect  \nEnvironmental Challenges  \njournal [homepage: www.elsevier.com/locate/envc](homepage: www.elsevier.com/locate/envc)  \n| Machine learning approaches for real-time forecasting of solar still distillate output\u003Cbr>Deepak Kumar Murugan a, *, Zafar Said b, h, Hitesh Panchalc, Naveen Kumar Gupta d, i, Sekar Subramanie, Abhinav Kumar f, Kishor Kumar Sadasivunig,j, *\u003Cbr>a Department of Mechanical Engineering, Velammal Engineering College, Chennai, India\u003Cbr>b Department of Sustainable and Renewable Energy Engineering, University of Sharjah, United Arab Emirates c Department of Mechanical Engineering, Government Engineering College Patan, Gujarat, India d Department of Mechanical Engineering, GLA University, Mathura, India.\u003Cbr>e Department of Mechanical Engineering, Rajalakshmi Engineering College, Chennai, India\u003Cbr>f Department of Nuclear and Renewable Energy, Ural Federal University Named after the First President of Russia Boris Yeltsin, Ekaterinburg 620002, Russia g Centre for Advanced Materials, Qatar University, Qatar\u003Cbr>h Department of Industrial and Mechanical Engineering, Lebanese American University (LAU), Byblos, Lebanon i Department of Mechanical Engineering, Harcourt Butler Technical University, Kanpur, Uttar Pradesh, India j Department of Mechanical and Industrial Engineering, Qatar University, PO Box 2713, Doha, Qatar |  |  |  |\n| --- | --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Solar still\u003Cbr>Machine learning techniques Decision tree modelling Predictive models Productivity estimation |  | Solar stills provide a promising avenue for freshwater production in regions grappling with water scarcity, especially remote locales. However, their efficiency is often constrained by the variable climatic conditions. Conventional prediction methods fall short in consistently forecasting the yield, leaving a significant gap in optimizing solar still operations. Recognizing this, the introduction of machine learning becomes pivotal. With a robust predictive model, operators can avoid inefficiencies, inconsistent outputs, and sub-optimal resource utilization. The primary objective of this research is to determine the most suitable machine learning model tailored for predicting solar still output under specific environmental conditions. This research work assessed various machine learning models, including linear regression, decision trees, random forest, support vector machines, and multilayer perceptron. Evaluation metrics encompassed Mean Absolute Error (MAE), cross-validation, grid search, and randomized search techniques. Our results identified the Decision Tree model, registering a MAE of 5.43 and 5.74 through random and grid search methods, respectively, as the preeminent predictor for our dataset. This machine learning-centric methodology elevates the precision of solar still output predictions and paves the way for enhanced solar still designs and superior optimization of solar energy conversion mechanisms. |  |\n\n1. Introduction  \nWater scarcity, increasingly exacerbated by factors like droughts, over-exploitation of aquifers, and burgeoning population demands, has emerged as a critical global concern (Atteya & Abbas, 2023; Panchalet al. 2019). This crisis transcends mere water shortages, manifesting in catastrophic ramifications like agricultural downturns, economic setbacks, and heightened resource competitions that occasionally escalate into conflicts (Panchal, 2017). A crucial aspect of this challenge lies in the health sector: inadequate access to potable water can be a breeding ground for waterborne ailments such as cholera, hepatitis, and typhoid, with studies revealing diseases like these leading to fatalities, notably in  \nchildren (Khatod et al., 2022). The global narrative is grim, with over a billion people deprived of clean water, culminating in an alarming annual death toll of 3.4 million d","cbCaikGfBFBzcUim","https://ap.wps.com/l/cbCaikGfBFBzcUim","pdf",6281859,1,10,"English","en",105,"# Introduction\n## Water scarcity and health impacts\n## Solar stills as low-energy desalination\n## Limitations of conventional prediction methods\n# Machine learning for forecasting\n## Models evaluated\n## Evaluation metrics and search strategies\n# Results and findings\n## Decision tree performance\n# Implications\n## Improved prediction accuracy and optimization","[{\"question\":\"Why is real-time forecasting of solar still distillate output important?\",\"answer\":\"Solar still efficiency varies with climatic conditions, and inconsistent yield predictions reduce operational effectiveness. Reliable forecasting helps avoid inefficiencies and supports better resource utilization.\"},{\"question\":\"Which machine learning models were compared in the study?\",\"answer\":\"The study evaluated linear regression, decision trees, random forest, support vector machines, and multilayer perceptron for predicting solar still output.\"},{\"question\":\"What model performed best according to the reported metrics?\",\"answer\":\"The decision tree model was identified as the preeminent predictor, achieving a mean absolute error (MAE) of 5.43 and 5.74 using random and grid search methods, respectively.\"}]","Machine learning approaches for real-time forecasting of solar still distillate output - Decision tree prediction for optimized freshwater production | PDF",1785721940,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-approaches-for-real-time-forecasting-of-solar-still-distillate-output-decision-tree-prediction-for-optimized-freshwater-production","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-approaches-for-real-time-forecasting-of-solar-still-distillate-output-decision-tree-prediction-for-optimized-freshwater-production/119019/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is real-time forecasting of solar still distillate output important?","Question",{"text":76,"@type":77},"Solar still efficiency varies with climatic conditions, and inconsistent yield predictions reduce operational effectiveness. Reliable forecasting helps avoid inefficiencies and supports better resource utilization.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning models were compared in the study?",{"text":81,"@type":77},"The study evaluated linear regression, decision trees, random forest, support vector machines, and multilayer perceptron for predicting solar still output.",{"name":83,"@type":74,"acceptedAnswer":84},"What model performed best according to the reported metrics?",{"text":85,"@type":77},"The decision tree model was identified as the preeminent predictor, achieving a mean absolute error (MAE) of 5.43 and 5.74 using random and grid search methods, respectively.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":21,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]