A Novel Approach for Earthquake Prediction Using Random Forest and Neural Networks

作者：Nidhi Agarwal, Ishika Arora, Harsh Saini, Ujjwal Sharma · 发表于：EAI Endorsed Transactions on Energy Web · 年份：2023 · DOI：10.4108/ew.4329 · 被引用次数：28 · 研究领域：Computer Science

INTRODUCTION: This research paper presents an innovative method that merges neural networks and random forest   algorithms to enhance earthquake prediction. OBJECTIVES: The primary objective of the study is to improve the precision of earthquake prediction by developing a hybrid model that integrates seismic wave data and various extracted features as inputs. METHODS: By training a neural network to learn the intricate relationships between the input features and earthquake magnitudes and employing a random forest algorithm to enhance the model's generalization and robustness, the researchers aim to achieve more accurate predictions. To evaluate the effectiveness of the proposed approach, an extensive dataset of earthquake records from diverse regions worldwide was employed. RESULTS: The results revealed that the hybrid model surpassed individual models, demonstrating superior prediction accuracy. This advancement holds profound implications for earthquake monitoring and disaster management, as the prompt and accurate detection of earthquake magnitudes is vital for effective mitigation and response strategies. CONCLUSION: The significance of this detection technique extends beyond theoretical research, as it can directly benefit organizations like the National Disaster Response Force (NDRF) in their relief efforts. By accurately predicting earthquake magnitudes, the model can facilitate the efficient allocation of resources and the timely delivery of relief materials to a...