[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128008-en":3,"doc-seo-128008-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128008,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Enhancing - Analysis and Prediction of Seasonal Water Quality of Nepal Using Machine Learning Approach with SHAP","Water quality is a critical worldwide concern, particularly in Nepal, where efficient monitoring supports safe drinking water and helps prevent waterborne illnesses. This study applies machine learning to analyze and forecast Nepalese well water seasonal water quality index (WQI). Hybrid models with nested cross-validation are built using CatBoost, Decision Tree, Logistic Regression, and LSTM-GRU hybrid architectures. Evaluation uses R², accuracy, and RMSE, while SHAP with SVM identifies key predictive factors.","Enhancing  \nInternational Journal of Informatics, Information System and Computer Engineering  \nAnalysis and Prediction of Seasonal Water Quality of Nepal Using Machine Learning Approach with SHAP  \nAnalysis  \nBiplov Paneru *, Bishwash Paneru **, Sanjog Chhetri Sapkota **  \n*Dept. of Applied sciences and Chemical Engineering, Institute of Engineering, Nepal  \n**Dept. of Electronics and Communication Engineering, Pokhara University  \n***Nepal Research and collaboration centre  \n*Corresponding Email: Biplov Paneru ([biplovp019402@nec.edu.np](biplovp019402@nec.edu.np))  \nA B S T R A C T S  \nWater quality is a crucial concern worldwide, including in Nepal, where efficient monitoring is essential for safe drinking water and preventing waterborne illnesses. This study employs machine learning to analyze and forecast the seasonal water quality index (WQI) of Nepalese well water. Hybrid models with nested crossvalidation were introduced, using methods like CatBoost, Decision Tree, Logistic Regression, MLPGRU, and LSTM-GRU hybrids. Performance metrics included R², accuracy, and RMSE. CatBoost achieved the highest classification accuracy (99.35%), while the LSTM-GRU hybrid excelled in capturing complex temporal patterns. Nested cross-validation demonstrated 96.13% accuracy with low standard deviation. Additionally, SHAP analysis identified key predictive factors using the SVM model. This research highlights machine learning’s potential in predicting and managing water quality effectively.  \n© 2021 Tim Konferensi UNIKOM  \nA R T I C L E I N F O  \nArticle History:  \nReceived 08 Jul 2024  \nRevised 30 Aug 2024  \nAccepted 19 Sept 20xx Available online 24 Dec 2024 Publication date 01 Dec 2025  \nKeywords:  \nWater quality, Machine Learning, LSTM-GRUSHAP analysis DO  \npH  \n1. INTRODUCTION  \nIn underdeveloped countries, a variety of issues affecting the water supply process can lead to water tap contamination. While machine learning techniques have gained popularity for making accurate water quality predictions, gathering the necessary data for modeling in underdeveloped nations has proven tobe difficult (Kuroki, et al., 2023) [1] . Water quality is extremely important to humans, animals, plants, industries, and the environment. Water quality is measured through Water Quality Index (WQI). In this study, the parameters were optimized and tuned to increase the accuracy of numerous machine learning models and techniques that were used to measure WQI and WQC (Shams, et al., 2024) . Conversely, lakes and reservoirs serve as essential sources of water. These reservoirs play a vital role in sustaining life, offering clean water and supporting a rich diversity of aquatic ecosystems (Solanki, et al., 2015) . Groundwater plays an essential role in maintaining natural water reserves, serving as a vital resource for drinking water, farming, and diverse industrial uses. However, industrial and agricultural activities greatly impact groundwater quality, often leading to contamination. This highlights the necessity of evaluating water quality to ensure safe consumption and efficient irrigation practices (Abbas, et al., 2014) . To limit harmful components' access into water bodies, particularly rivers, timely monitoring and rapid decision-making are crucial. Conventional approaches to assessing water quality can occasionally be expensive and require significant time (Najafzadeh, & Basirian, 2023) .  \nFig. 1. Global water dryness problem  \nThe analysis and prediction of water quality of a country or area using machine learning arpproach highlights a significant step towards utilizing artificial intelligence methods for water quality prediction and analysis making.  \nAn important development in the use of artificial intelligence for environmental management is the analysis and forecast of water quality by machine learning techniques. Large datasets containing several water quality indicators, like dissolved oxygen (DO), pH, temperature, and electrical conductivity ","cbCaicZ7TFOAiwn3","https://ap.wps.com/l/cbCaicZ7TFOAiwn3","pdf",1297015,6,1,20,"English","en",105,"# Keywords\n## Water quality\n## Machine Learning\n## LSTM-GRU\n## SHAP analysis\n## DO, pH\n# 1. INTRODUCTION\n## Importance of water quality and WQI\n## Limitations of conventional monitoring\n## Study motivation and data-driven monitoring\n# Methods and Models\n## Hybrid ML models and nested cross-validation\n## Evaluation metrics (R², accuracy, RMSE)","[{\"question\":\"What data and target does the study use for predicting water quality in Nepal?\",\"answer\":\"The study focuses on seasonal well water in Nepal and predicts the water quality index (WQI) using water quality indicators and related parameters. It also describes using a dataset with many cases and selected characteristics.\"},{\"question\":\"Which machine learning models are used, and what are the main evaluation metrics?\",\"answer\":\"Models include CatBoost, Decision Tree, Logistic Regression, and hybrid LSTM-GRU approaches. Performance is assessed using R², accuracy, and RMSE, with additional classification metrics such as precision, F1 score, recall, and MCC.\"},{\"question\":\"How does SHAP contribute to the research results?\",\"answer\":\"SHAP analysis is used alongside an SVM model to identify key predictive factors driving the water quality predictions.\"}]","Enhancing - Analysis and Prediction of Seasonal Water Quality of Nepal Using Machine Learning Approach with SHAP | PDF",1785943805,50,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"enhancing-analysis-and-prediction-of-seasonal-water-quality-of-nepal-using-machine-learning-approach-with-shap","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/enhancing-analysis-and-prediction-of-seasonal-water-quality-of-nepal-using-machine-learning-approach-with-shap/128008/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What data and target does the study use for predicting water quality in Nepal?","Question",{"text":77,"@type":78},"The study focuses on seasonal well water in Nepal and predicts the water quality index (WQI) using water quality indicators and related parameters. It also describes using a dataset with many cases and selected characteristics.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which machine learning models are used, and what are the main evaluation metrics?",{"text":82,"@type":78},"Models include CatBoost, Decision Tree, Logistic Regression, and hybrid LSTM-GRU approaches. Performance is assessed using R², accuracy, and RMSE, with additional classification metrics such as precision, F1 score, recall, and MCC.",{"name":84,"@type":75,"acceptedAnswer":85},"How does SHAP contribute to the research results?",{"text":86,"@type":78},"SHAP analysis is used alongside an SVM model to identify key predictive factors driving the water quality predictions.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,115,120,123,127,130,134],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":30,"slug":114},"Technology","technology",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":22,"slug":126},9,"Religion & Spirituality","religion-spirituality",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":22,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":47,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":47,"category_name":136,"show_sort_weight":108,"slug":137},19,"General","general"]