Quantifying Drought Using Machine Learning Models with SPEI indices and Weather Data - Hossain, Md. Alomgir; Begum, Momotaz; Akhtar, Md. Nasim (IAES Indonesian Section, 2025)
Drought 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