[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122449-en":3,"doc-seo-122449-105":30,"detail-sidebar-cat-0-en-105":91},{"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},122449,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Quantifying Drought Using Machine Learning Models with SPEI indices and Weather Data","Drought prediction supports effective water-resource management in drought-prone regions such as Rajshahi, Bangladesh. The study proposes a machine-learning framework to quantify and forecast drought using the Standardized Precipitation Evapotranspiration Index (SPEI). Monthly meteorological variables and climatic water balance spanning 1965–2022 are encoded into categorical drought situations. Individual classifiers and regressors are benchmarked, then two hybrid forecasters are built, achieving 92% accuracy and 96% accuracy, improving reliability for drought management.","Quantifying Drought Using Machine Learning Models with SPEI indices and Weather Data  \nMd. Alomgir Hossain1, Momotaz Begum2, Md. Nasim Akhtar3  \n1, 2, 3Department of Computer Science and Engineering,Dhaka University of Engineering & Technology, Gazipur, Bangladesh.  \n1International University of Business Agriculture and Technology, Dhaka, Bangladesh.  \nArticle history:  \nReceived Mar 13, 2025 Revised Aug 17, 2025 Accepted Aug 30, 2025  \nKeywords:  \nDrought prediction SPEI  \nMachine learning Climate change Rajshahi  \nDrought prediction is crucial for effective water resource management, particularly in regions prone to frequent droughts, such as Rajshahi, Bangladesh. This study presents a novel approach to quantifying and predicting drought conditions in Rajshahi, Bangladesh, utilizing machine learning models with the Standardized Precipitation Evapotranspiration Index (SPEI) as drought indices. We utilized monthly meteorological data (temperature, precipitation, humidity, wind speed, number of sunshine hours, cloud cover, potential evapotranspiration, and the climatic water balance) from 1965 to 2022. To train machine learning models, SPEI drought indicators were numerically encoded and classified into categorical drought situations. To forecast drought conditions in the Rajshahi region, we tested a variety of individual classification and regression algorithms, including Gradient Boosting, XGBoost, Multi-Layer Perceptron (MLP), Random Forest, Logistic Regression, Support Vector Machines, CatBoostClassifier, and Decision Trees. These models performed differently, with accuracy rates ranging from 85% to 88% for classification tests and R² scores from 0.25 to 0.71 for regression tasks. To increase forecast accuracy, we created two hybrid models: the Multi-Model Drought Forecaster and the Drought Anticipation Super Model. The \"Multi-Model Drought Forecaster,\" which combines MLP, Random Forest, Gradient Boosting Classifier, and Decision Tree Classifier, obtained 92% accuracy. The \"Drought Anticipation Super Model,\" incorporating Random Forest, Gradient Boosting, Decision Trees, Support Vector Regression, and CatBoost Classifier, increased the accuracy to 96% . The hybrid model's improved performance demonstrates that it can give more accurate and reliable drought forecasts in the Rajshahi region. These findings improve drought management strategies in Bangladesh and other climate-vulnerable areas. This study also created advanced hybrid machine learning models for drought forecasting in Rajshahi, Bangladesh, with the help of 58 years of meteorological data from 1965 to 2022 and SPEI indices. The “Multi-Model Drought Forecaster” is 92% accurate by utilizing MLP, Random Forest, Gradient Boosting, and Decision Trees. The “Drought Anticipation Super Model” is 96% accurate by adding Support Vector Regression and CatBoost Classifier to provide a better drought forecast to manage water resources effectively.  \nCopyright © 2025 Institute of Advanced Engineering and Science.  \nAll rights reserved.  \nCorresponding Author:  \nMd. Alomgir Hossain  \nDepartment of Computer Science and Engineering,  \nDhaka University of Engineering & Technology, Gazipur, Bangladesh& International University of Business Agriculture and Technology (IUBAT) , Dhaka, Bangladesh.  \n[alomgir.hossain@iubat.edu](alomgir.hossain@iubat.edu)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nClimate change has affected mankind and is still doing so in the present. Heat waves, drought, cyclones, and heavy rains are known to cause displacement, hunger, and starvation [1] . Drought is a rather generic and widespread meteorological event that has adverse effects on the ecosystems, water supply, and agriculture all around the globe. Heat, dryness, velocity, and the absence of rain in the arriving months are attributes of drought conditions and can have an extreme impact on biophysical and social systems [2] . Drought is a climatic condition that is experienced in all parts of the world ","cbCairvrEjnsVUkS","https://ap.wps.com/l/cbCairvrEjnsVUkS","pdf",729704,1,16,"English","en",105,"# Abstract\n# Introduction\n## Drought impacts and vulnerability in Bangladesh\n## SPEI as a drought indicator\n# Materials and Methods\n## Data sources and variables (1965–2022)\n## Model encoding and algorithms\n# Results\n## Individual model performance\n## Hybrid model performance\n# Conclusion","[{\"question\":\"What drought index is used to quantify drought conditions in this study?\",\"answer\":\"The study uses the Standardized Precipitation Evapotranspiration Index (SPEI) as the drought index to characterize drought situations.\"},{\"question\":\"Which time range and meteorological variables are used for model training and testing?\",\"answer\":\"Monthly meteorological data from 1965 to 2022 are used, including temperature, precipitation, humidity, wind speed, sunshine hours, cloud cover, potential evapotranspiration, and climatic water balance.\"},{\"question\":\"How do the hybrid models improve drought forecasting performance?\",\"answer\":\"The hybrid models combine multiple machine-learning algorithms, increasing classification accuracy to 92% for the Multi-Model Drought Forecaster and 96% for the Drought Anticipation Super Model, leading to more accurate drought forecasts.\"}]","Quantifying Drought Using Machine Learning Models with SPEI indices and Weather Data | 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drought index is used to quantify drought conditions in this study?","Question",{"text":75,"@type":76},"The study uses the Standardized Precipitation Evapotranspiration Index (SPEI) as the drought index to characterize drought situations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which time range and meteorological variables are used for model training and testing?",{"text":80,"@type":76},"Monthly meteorological data from 1965 to 2022 are used, including temperature, precipitation, humidity, wind speed, sunshine hours, cloud cover, potential evapotranspiration, and climatic water balance.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the hybrid models improve drought forecasting performance?",{"text":84,"@type":76},"The hybrid models combine multiple machine-learning algorithms, increasing classification accuracy to 92% for the Multi-Model Drought Forecaster and 96% for the Drought Anticipation Super Model, leading to more accurate drought 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