Intelligent System for Crisis Analysis and Resource Allocation

Natural disasters are common events in human history, but the way we deal with them changed over time. Due to many factors like climate change, the frequency of calamities has increased. Early prediction of these catastrophic events may help us be prepared, but we cannot stop their occurrence. This demands the urgency of a robust system that handles post-disaster analysis and assists in resource allocation to counter the consequences of the misfortune event. One of the biggest challenges is the assessment of disasters quickly and accurately. To counter this issue we introduce the novel end-to-end disaster response pipeline that: (1) identifies disaster events from imagery through CNN; (2) predicts severity; (3) allocates resources dynamically based on live inventory streams and (4) solves detection and allocation jointly under a new hybrid loss. To achieve this we propose a custom lightweight convolutional neural network which analyzes aerial imagery to distinguish among disaster types such as floods, wildfires and earthquakes, and a fine-tuned Mo-bileNetV2 model that assigns severity scores to each affected region. Those two outputs are merged with live inventories of personnel, medical supplies and shelter availability before being processed by a LightGBM gradient boosting engine to generate an optimized distribution plan. Moreover deep learning models can improve continuously by learning from additional information that is fed over time. Manual errors and efforts can be reduced by the system’s ability to automate large-scale damage assessment, additionally this accelerates response time and enhances disaster management strategies.