A PSO-Optimized Neural Network and ABC Feature Selection Approach with eXplainable Artificial Intelligence (XAI) for Natural Disaster Prediction

Abstract

Listen

Translate article

Introduction: ”Artificial Intelligence will revolutionize our lives” is a phrase frequently echoed. The influence of Artificial Intelligence (AI) and Machine Learning (ML) extends across various aspects of our daily lives, encompassing health, education, economics, the environment, and more. Method: A particularly formidable challenge lies in decision support, especially in critical scenarios such as natural disaster management, where artificial intelligence significantly amplifies its ongoing capacity to assist in making optimal decisions. In the realm of disaster management, the primary focus often centers on preventing or mitigating the impact of disasters. Consequently, it becomes imperative to anticipate their occurrence in terms of both time and location, enabling the effective implementation of necessary strategies and measures. In our research, we propose a disaster forecasting framework based on a Multi-Layer Perceptron (MLP) empowered by the Particle Swarm Optimization (PSO) algorithm. The PSO-MLP is further fortified by the incorporation of the Artificial Bee Colony (ABC) algorithm for feature selection, pinpointing the most critical elements. Subsequently, we employ the LIME (Local Interpretable Model-agnostic Explanations) model, a component of eXplainable Artificial Intelligence (XAI). This comprehensive approach aims to assist managers and decision-makers in comprehending the factors influencing the determination of the occurrence of such disasters and increases the performance of the PSO-MLP model. The approach, specifically applied to predict snow avalanches, has yielded impressive results. Result: The obtained accuracy of 0.92 and an AUC of 0.94 demonstrate the effectiveness of the proposed framework. In comparison, the prediction precision achieved through an SVM is 0.75, while the RF classifier yields 0.86, and XGBoost reaches 0.77. Notably, the precision is further enhanced to 0.81 when utilizing XGBoost optimized by the grid-search. Conclusion: These results highlight the superior performance of the proposed methodology, showcasing its potential for accurate and reliable snow avalanche predictions compared to other established models.