[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-detail-450263-en":59,"doc-seo-450263-105":80},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":5,"data":60},{"doc_id":61,"user_id":62,"nickname":63,"user_avatar":64,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":66,"doc_content":67,"file_id":68,"file_url":69,"file_type":70,"file_size":71,"view_count":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":72,"language":73,"language_code":74,"site_id":75,"html_lang":74,"table_of_contents":76,"faqs":77,"seo_title":78,"seo_description":66,"update_tm":79,"read_time":26},450263,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Prediction of groundwater quality assessment by integrating boosted learning with DE optimizer","Groundwater quality is threatened by industrialization, urbanization, over-extraction, and contamination from agricultural and urban sources, creating serious health risks when minerals such as calcium, magnesium, sodium, potassium, fluoride, and chloride appear in excess. This work develops a hybrid machine learning approach to predict the groundwater quality index (GWQI) and identify key contaminants driving water unsafety. Using 1,989 samples from Jajpur district in Odisha’s Sukinda Valley, LCBoost fusion combines CatBoost and LightGBM to improve accuracy, achieving RMSE 0.6826 and R2 0.9810, with potassium, fluoride, and total hardness as most influential.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nPrediction of groundwater quality assessment by integrating boosted learning with DE optimizer  \nSonalika Subudhi1, Alok Kumar Pati1􀀍, Sephali Bose1, Subhasmita Sahoo1, Avipsa Pattanaik1, Biswa Mohan Acharya1􀀍 & Rakesh Ranjan Thakur2  \nGroundwater is eventually undermined by human activities, such as rapid industrialization, urbanization, over-extraction, and contamination from agricultural and urban sources. Among the different contaminants, the presence of minerals such as calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), fluoride (F), and chloride (Cl) proves to have serious health risks when present in excess concentrations. This study addresses this gap by developing a predictive machine learning model to evaluate the groundwater quality index (GWQI) and to identify the critical contaminants affecting water safety. A total of 1989 groundwater samples were collected from Jajpur district, where the Sukinda Valley is located, and analyzed for multiple physicochemical parameters, as this region is known for extensive chromite mining activities and has been identified as one of the most critically polluted areas in India, posing significant groundwater contamination risks. This study introduces the novel hybrid machine learning model, LCBoost fusion, which distinguishes this work from previous studies by combining the strengths of CatBoost and LightGBM to enhance predictive accuracy. It has been achieved with the help of a hybrid machine learning model i.e. LCBoost fusion. The model outperforms individual models (CatBoost and LightGBM), by achieving low RMSE (0.6826), MSE (0 .5100), MAE (0 .3148) and a high R2 score of 0.9810. Feature importance analysis highlights potassium (K), fluoride (F) and total hardness (TH) as the most influential indicators of groundwater contamination. This research successfully demonstrates the application of machine learning in assessing groundwater quality risks in Odisha, with practical implications for real-time groundwater monitoring and risk mitigation. LCBoost Fusion model offers a reliable and efficient approach for realtime groundwater monitoring and risk mitigation. These findings will help environmental organizations and policy makers to map out targeted places for sustainable groundwater management.  \nKeywords Groundwater quality, LCBoost fusion, Machine learning, Differential evolution, Feature importance, Sukinda valley.  \nGroundwater is a crucial source of drinking water, supporting domestic, agricultural, and industrial needs1,2. However, its quality is often compromised by natural and human-induced factors, posing significant health and environmental risks3,4. Contaminants like chloride, sodium, fluoride, potassium, and magnesium, along with pollutants from agricultural runoff, industrial waste, and untreated sewage, degrade groundwater quality5–7. Factors such as soil composition, groundwater depth, and climate variability further influence water quality8,9. Given these threats, continuous monitoring and pollution control are essential to safeguard groundwater resources10, 11.  \nEvaluating groundwater quality is critical for safe drinking water, sustainable agriculture, industrial productivity, and environmental conservation12. Traditional methods, such as field sampling and laboratory analysis, are time-consuming, costly, and limited by sparse spatial coverage, hindering early detection of pollutants13, 14. Contaminated groundwater can damage soil, reduce crop yields, corrode equipment, and increase operational costs, while also leaching toxins into aquatic systems, harming biodiversity15–17.Thus, there is a pressing need for more efficient, scalable, and real-time monitoring solutions to ensure sustainable groundwater management18–20.  \nTraditional groundwater quality monitoring methods face limitations due to the subsurface characteristics of aquifers, limited accessibility, and slow contami","cbCaiicJGrVPLSqz","https://ap.wps.com/l/cbCaiicJGrVPLSqz","pdf",5810017,24,"English","en",105,"# Introduction\n## Problem and health risks\n## Importance of evaluating groundwater quality\n## Limitations of traditional monitoring\n## Motivation for machine learning approaches","[{\"question\":\"What contaminants does the study focus on for groundwater safety?\",\"answer\":\"The study targets minerals and ions including calcium, magnesium, sodium, potassium, fluoride, and chloride, and evaluates their role in affecting the GWQI and overall water safety.\"},{\"question\":\"How is the proposed LCBoost fusion model constructed?\",\"answer\":\"LCBoost fusion is a hybrid model that combines CatBoost and LightGBM to strengthen predictive performance for groundwater quality assessment.\"},{\"question\":\"What indicators are identified as most influential in contamination?\",\"answer\":\"Feature importance analysis highlights potassium (K), fluoride (F), and total hardness (TH) as the most influential indicators of groundwater contamination.\"}]","Prediction of groundwater quality assessment by integrating boosted learning with DE optimizer | PDF",1790732660,{"code":4,"msg":81,"data":82},"ok",{"site_id":75,"language":74,"slug":83,"title":65,"keywords":84,"description":66,"schema_data":85,"social_meta":139,"head_meta":141,"extra_data":143,"updated_unix":144},"prediction-of-groundwater-quality-assessment-by-integrating-boosted-learning-with-de-optimizer","",{"@graph":86,"@context":138},[87,101,121],{"@type":88,"itemListElement":89},"BreadcrumbList",[90,94,96,99],{"item":91,"name":92,"@type":93,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":95,"name":9,"@type":93,"position":14},"https://docshare.wps.com/document/",{"item":97,"name":40,"@type":93,"position":98},"https://docshare.wps.com/document/research-report/",3,{"item":100,"name":65,"@type":93,"position":19},"https://docshare.wps.com/document/prediction-of-groundwater-quality-assessment-by-integrating-boosted-learning-with-de-optimizer/450263/",{"url":100,"name":65,"@type":102,"image":103,"author":108,"headline":65,"publisher":110,"fileFormat":113,"inLanguage":74,"description":66,"dateModified":114,"datePublished":115,"encodingFormat":113,"isAccessibleForFree":116,"interactionStatistic":117},"DigitalDocument",{"url":104,"@type":105,"width":106,"height":107},"https://docshare.wps.com/thumbnails/prediction-of-groundwater-quality-assessment-by-integrating-boosted-learning-with-de-optimizer/450263.png","ImageObject",300,407,{"name":63,"@type":109},"Person",{"url":91,"name":111,"@type":112},"DocShare","Organization","application/pdf","2026-10-06","2026-09-30",true,{"@type":118,"interactionType":119,"userInteractionCount":24},"InteractionCounter",{"@type":120},"ViewAction",{"@type":122,"mainEntity":123},"FAQPage",[124,130,134],{"name":125,"@type":126,"acceptedAnswer":127},"What contaminants does the study focus on for groundwater safety?","Question",{"text":128,"@type":129},"The study targets minerals and ions including calcium, magnesium, sodium, potassium, fluoride, and chloride, and evaluates their role in affecting the GWQI and overall water safety.","Answer",{"name":131,"@type":126,"acceptedAnswer":132},"How is the proposed LCBoost fusion model constructed?",{"text":133,"@type":129},"LCBoost fusion is a hybrid model that combines CatBoost and LightGBM to strengthen predictive performance for groundwater quality assessment.",{"name":135,"@type":126,"acceptedAnswer":136},"What indicators are identified as most influential in contamination?",{"text":137,"@type":129},"Feature importance analysis highlights potassium (K), fluoride (F), and total hardness (TH) as the most influential indicators of groundwater contamination.","https://schema.org",{"og:url":100,"og:type":140,"og:title":65,"og:site_name":111,"og:description":66},"article",{"robots":142,"canonical":100},"index,follow",{"doc_id":61,"site_id":75},1791030461]