Quantifying Drought Using Machine Learning Models with SPEI indices and Weather Data - Hossain, Md. Alomgir; Begum, Momotaz; Akhtar, Md. Nasim (Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 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 "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.