Enhanced Water Security and Resilience in Drought-Prone Zones with IoT and SVM classifier for Early Warning Systems

The worldwide risk of droughts to water security and agricultural sustainability is growing. Using Internet of Things (IoT) technology and a Support Vector Machine (SVM) classifier to create drought-prone early warning systems is an innovative solution to these difficulties. Better water resource management and resilience in water-scarce locations are the study goals. The IoT infrastructure collects real-time data from sensors strategically positioned over the research area to monitor soil moisture, weather, and water levels. SVM classifiers, which are effective in classification, get this large dataset. The SVM model uses data to predict and classify drought events, alerting decision-makers and stakeholders. The system shows that this integrated strategy works. The IoT-SVM early warning system reliably predicts droughts and alerts authorities in advance, enabling proactive mitigation and intervention. The system's drought prediction accuracy helps drought-prone areas achieve water security and resilience by allocating resources, managing water sustainably, and reducing effects on agriculture and people. It uses IoT and machine learning to improve drought preparation and response, improving long-term water sustainability and resilience in water-scarce areas.