Drought and Flood Early Warning Systems: Iran vs. United States

The increasing frequency and intensity of natural hazards such as droughts and floods pose significant challenges to environmental management, urban planning, and disaster mitigation worldwide. Iran, due to its diverse climatic regions and geographic location, experiences both prolonged droughts and sudden flood events. Similarly, the United States faces varied climate extremes, including extensive droughts in western states and frequent floods in coastal and riverine regions. Effective early warning systems (EWS) are crucial for reducing human casualties, economic losses, and infrastructure damage in both countries. This study investigates machine learning (ML) algorithms, including Linear Regression (LR), Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), for early warning of droughts and floods in Iran and the United States. Historical climate, hydrological, and remote sensing datasets were analyzed. The study introduces region-specific adaptations, hybrid models, and feature engineering strategies to enhance predictive accuracy. Metrics such as Accuracy, Precision, Recall, F۱-score, and ROC-AUC were used to evaluate model performance. Results indicate that ML-based models significantly outperform traditional statistical methods. RF and ANN show superior handling of complex, non-linear patterns, while SVM is effective for rare-event classification (Duan & Zhang, ۲۰۲۲; Nevo et al., ۲۰۲۱). Integrating ML into EWS improves disaster preparedness, urban management, and policy-making, particularly when models are adapted to local data characteristics and climate conditions.

کلیدواژه ها:

Machine Learning ، Early Warning Systems ، Drought Prediction ، Flood Forecasting ، ANN ، Random Forest ، SVM ، Iran ، United States ، Climate Extremes