Big Data Driven Map Reduce Framework for Automated Flood Disaster Detection Based on Heuristic-Based Ensemble Learning

Flooding disaster causes huge impacts on the socio-economic world. In the inundated area, some geo-referenced images are shared through some media posts, which assist in providing alertness to the critical volunteers and managing the financial loss crisis. In this work, the Adaptive Billiards-Inspired Optimization (A-BIO) and Optimized Ensemble-learning-based detection (OED) with map reduce framework is proposed for flood disaster detection. Initially, the big data is gathered and processed for detection. During the map phase, data preprocessing is performed to enhance the performance of the data, which helps in removing the noise or unwanted attributes. Furthermore, the reduction phase can be done through weighted feature selection, where the features to be selected and the weight is optimized through A-BIO, which assists in getting the most significant features for improving the performance and reducing the complexity of the designed model. Finally, OED is performed by a set of classifiers like Convolutional Neural Networks, Adaboost, XGBoost, Long Short-Term Memory, and Deep Neural Networks, where the parameters of ensemble learning classifiers are optimized by A-BIO algorithm. Finally, through the performance analysis, this detection model can provide high accuracy and better detection performance to avoid the huge impacts of flood disasters.