Machine Learning/Deep Learning for Natural Disasters

Abstract

Natural disasters occurrence is inevitable and its consequences can impact the inhabitants of that area harshly. Researchers throughout the world are concerned about discovering ways which can give accurate warnings about the occurrence of these disasters. Various technologies such as IoT, object sensing, 5G and cellular networks, smartphone-based systems, and satellite-based systems are being applied actively these days for gathering the data about natural disaster occurrence and the details of the regions where they happen. Machine Learning (ML) has speeded up the potential of understanding the patterns of occurrence and the factors that lead to natural hazards effectively. ML algorithms can handle multi-dimensional, large volumes of data related to disaster management that is created naturally in environments and are particularly applicable for related key tasks such as classification and recognition. Applications of these algorithms are found to be useful in the prediction of disasters and in assisting the disaster management tasks such as determining crowd evacuation routes, analysing social media posts, and handling sustainable development. This exploratory chapter reviews various ML/Deep Learning (DL) approaches that have been applied for the prediction of natural disasters and their management. Background information of feature maps tracing to the localization of natural disaster susceptible regions is discussed. Insight to the performance of state of art approaches like Convolutional Neural Networks, time-distributed Long-Short Term Memory (LSTM) cells is also provided. Finally, this chapter presents some hybrid approaches which can be explored and expected future trends of applications of ML and DL in natural disasters.

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