[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-127021-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-127021-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","streamflow-simulation-and-forecasting-using-remote-sensing-and-machine-learning-techniques-study-results","Streamflow simulation and forecasting using remote sensing and machine learning techniques - Study results","","The study integrates remote sensing data with machine learning to simulate and forecast streamflow in the Klang River Basin. It targets higher prediction accuracy for water resource management and flood control in Malaysia. Five models—KNN, SVM, Random Forests, ANN, and LSTM—are evaluated through 25 model configurations trained on Malaysia DID datasets and NASA Giovanni climate data. Random Forests (RF-III) achieves the strongest results (SMAPE 0.36, MdAPE 0.37, Lp 0.908), with air temperature identified as a major driver.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/streamflow-simulation-and-forecasting-using-remote-sensing-and-machine-learning-techniques-study-results/127021/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/streamflow-simulation-and-forecasting-using-remote-sensing-and-machine-learning-techniques-study-results/127021.png","ImageObject",300,407,{"name":42,"@type":43},"Ezra","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-21","2026-08-05",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",10,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Which river basin and application context does the study focus on?","Question",{"text":63,"@type":64},"The study focuses on the Klang River Basin, aiming to improve streamflow prediction for water resource management and flood control in Malaysia.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What datasets were used to train and evaluate the models?",{"text":68,"@type":64},"Training used streamflow datasets from Malaysia’s Department of Irrigation and Drainage (DID) combined with climate data from NASA’s Giovanni Portal.",{"name":70,"@type":61,"acceptedAnswer":71},"Which machine learning model configuration performed best?",{"text":72,"@type":64},"Random Forests (RF-III) delivered the best performance, with SMAPE 0.36, MdAPE 0.37, and Lp 0.908.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},127021,1785936368,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":55,"slug":125},"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":22,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":80,"read_time":30},2336474466412,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Streamflow simulation and forecasting using remote sensing and machine  \nlearning techniques  \nABSTRACT  \nThe study investigates the integration of remote sensing data and machine learning (ML) techniques in streamflow simulation and forecasting in Klang River Basin. The motivation behind the research lies in the need for improved accuracy in streamflow prediction to support effective water resource management and flood control in Malaysia. Five ML models, including K-Nearest Neighbours (KNN), Support Vector Machines (SVM), Random Forests (RF), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM) were tested, with 25 different model configurations trained on datasets from Malaysia’s Department of Irrigation and Drainage (DID) and climate data from NASA’s Giovanni Portal. Among the models, RF − III showed the best performance with SMAPE, MdAPE and Lp values of 0.36 and 0.37 and 0.908, respectively. Further analysis underscores air temperature’s substantial impact on streamflow prediction, deviating significantly from the benchmark value. The findings contribute to advancements in streamflow forecasting, offering potential applications in flood management and water resource planning for the region.","cbCaifK228gPjo9z","https://ap.wps.com/l/cbCaifK228gPjo9z","pdf",42978,"English","# Abstract\n## Study focus and data sources\n## Machine learning models and evaluation\n## Key findings and implications","[{\"question\":\"Which river basin and application context does the study focus on?\",\"answer\":\"The study focuses on the Klang River Basin, aiming to improve streamflow prediction for water resource management and flood control in Malaysia.\"},{\"question\":\"What datasets were used to train and evaluate the models?\",\"answer\":\"Training used streamflow datasets from Malaysia’s Department of Irrigation and Drainage (DID) combined with climate data from NASA’s Giovanni Portal.\"},{\"question\":\"Which machine learning model configuration performed best?\",\"answer\":\"Random Forests (RF-III) delivered the best performance, with SMAPE 0.36, MdAPE 0.37, and Lp 0.908.\"}]","Streamflow simulation and forecasting using remote sensing and machine learning techniques - Study results | PDF"]