A Hybrid Prediction Model Integrating Artificial Intelligence and Geospatial Analysis for Disaster Management

Abstract

A natural or man-made disaster must be predicted accurately and in time. Advanced modelling is essential to predict increasingly frequent and intense natural events, enabling better response and recovery strategies. Hybrid machine learning is used to predict floods, assess earthquake damage, and control wildfires. Convolutional Neural Networks (CNNs), Gradient Boosting Machines (GBMs) and Support Vector Machines (SVMs) are used in the proposed model. We evaluate the proposed machine learning model in comparison to conventional statistical analysis and single machine learning techniques. The proposed hybrid model provided 90% correct predictions for flood events in Bangladesh with precision and recall values of 88% and 85%, respectively. In the assessment of earthquake damage in Japan, an accuracy of 92% was achieved with a precision of 90% and a recall of 89% with an F1 score of 89%. In the management of wildfires in California, an accuracy of 88% with a precision of 85% were achieved. The proposed hybrid model outperforms conventional techniques due to its higher reliability in predicting floods.

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