SMART AI-ML FRAMEWORKS FOR EARLY DETECTION AND RISK MITIGATION OF FLOODS, DROUGHTS, AND WILDFIRES

Abstract

Increasing frequency and severity of climate-induced catastrophes including floods, droughts, and wildfires call for creative early detection and risk reduction techniques. Traditional forecasting Lack of adaptability and real-time reaction in methods lowers their utility in disaster preparedness. This study aims to evaluate and validate Smart AI-ML systems for early detection, predictive modeling, and risk mitigation of floods, droughts, and wildfire within high-risk Pakistan.  A cross-sectional analytical research design was employed. Using stratified random sampling technique, 50 disaster-prone areas in Pakistan were chosen, including flood-prone areas of Sindh and Southern Punjab, d drought-prone regions of Thar Desert (Sindh) and Balochistan, and fire-prone areas including the Margalla Hills (Islamabad), Balochistan forests, and Khyber Pakhtunkhwa hills. Data were gathered by satellite imagery, meteorologically files, Arranged into a Disaster Data Collection Framework (DDCF), remote sensing databases, IoT-based environmental sensors, and AI-ML algorithms deep learning for image classification, ensemble learning for forecasting, were used along with reinforcement learning for adaptive reactions. Statistical analysis including ROC curve analysis, confusion matrix assessment, and McNemar's test was employed to evaluate predictive accuracy Compared to traditional models, the AI-ML framework showed considerably better predictive accuracy (p < 0.05), with reduced false alarms and better early detection. Windows, especially for floods and wildfires. Hybrid AI-ML models showed superior robustness compared to single-algorithm approaches, while IoT integration enabled real-time, dynamic risk assessment. Smart AI-ML frameworks present a transformative solution for climate disaster management. In Pakistan, particularly in high-risk regions such as Sindh, Southern Punjab, Balochistan, Khyber Pakhtunkhwa, and Islamabad, these frameworks can strengthen disaster resilience, minimize vulnerability, and support evidence-based policy-making for sustainable adaptation strategies.