A Contemporary Review on Deep Learning Models for Drought Prediction

Amogh Gyaneshwar, Anirudh Mishra, Utkarsh Chadha, P. M. Durai Raj Vincent (), Venkatesan Rajinikanth, Ganapathy Pattukandan Ganapathy and Kathiravan Srinivasan ()
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Amogh Gyaneshwar: School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
Anirudh Mishra: School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
Utkarsh Chadha: Faculty of Applied Sciences and Engineering, University of Toronto, St. George Campus, Toronto, ON M5S 1A1, Canada
P. M. Durai Raj Vincent: School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
Venkatesan Rajinikanth: Department of Computer Science and Engineering, Division of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai 602105, India
Ganapathy Pattukandan Ganapathy: Centre for Disaster Mitigation and Management, Vellore Institute of Technology, Vellore 632014, India
Kathiravan Srinivasan: School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India

Sustainability, 2023, vol. 15, issue 7, 1-31

Abstract: Deep learning models have been widely used in various applications, such as image and speech recognition, natural language processing, and recently, in the field of drought forecasting/prediction. These models have proven to be effective in handling large and complex datasets, and in automatically extracting relevant features for forecasting. The use of deep learning models in drought forecasting can provide more accurate and timely predictions, which are crucial for the mitigation of drought-related impacts such as crop failure, water shortages, and economic losses. This review provides information on the type of droughts and their information systems. A comparative analysis of deep learning models, related technology, and research tabulation is provided. The review has identified algorithms that are more pertinent than others in the current scenario, such as the Deep Neural Network, Multi-Layer Perceptron, Convolutional Neural Networks, and combination of hybrid models. The paper also discusses the common issues for deep learning models for drought forecasting and the current open challenges. In conclusion, deep learning models offer a powerful tool for drought forecasting, which can significantly improve our understanding of drought dynamics and our ability to predict and mitigate its impacts. However, it is important to note that the success of these models is highly dependent on the availability and quality of data, as well as the specific characteristics of the drought event.

Keywords: deep learning; drought prediction; environmental sustainability; Big Data; artificial intelligence (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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