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A Contemporary Review on Drought Modeling Using Machine Learning Approaches

Karpagam Sundararajan1, Lalit Garg2,*, Kathiravan Srinivasan4,*, Ali Kashif Bashir3, Jayakumar Kaliappan4, Ganapathy Pattukandan Ganapathy5, Senthil Kumaran Selvaraj6, T. Meena7

1 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, 632014, India
2 Faculty of Information and Communication Technology, University of Malta, Msida, MSD2080, Malta
3 Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, M15 6BH, UK
4 School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, India
5 Centre for Disaster Mitigation and Management, Vellore Institute of Technology, Vellore, 632014, India
6 Department of Manufacturing Engineering, School of Mechanical Engineering, Vellore Institute of Technology, Vellore, 632014, India
7 School of Civil Engineering, Vellore Institute of Technology, Vellore, 632014, India

* Corresponding Authors: Lalit Garg. Email: email; Kathiravan Srinivasan. Email: email

Computer Modeling in Engineering & Sciences 2021, 128(2), 447-487. https://doi.org/10.32604/cmes.2021.015528

Abstract

Drought is the least understood natural disaster due to the complex relationship of multiple contributory factors. Its beginning and end are hard to gauge, and they can last for months or even for years. India has faced many droughts in the last few decades. Predicting future droughts is vital for framing drought management plans to sustain natural resources. The data-driven modelling for forecasting the metrological time series prediction is becoming more powerful and flexible with computational intelligence techniques. Machine learning (ML) techniques have demonstrated success in the drought prediction process and are becoming popular to predict the weather, especially the minimum temperature using backpropagation algorithms. The favourite ML techniques for weather forecasting include support vector machines (SVM), support vector regression, random forest, decision tree, logistic regression, Naive Bayes, linear regression, gradient boosting tree, k-nearest neighbours (KNN), the adaptive neuro-fuzzy inference system, the feed-forward neural networks, Markovian chain, Bayesian network, hidden Markov models, and autoregressive moving averages, evolutionary algorithms, deep learning and many more. This paper presents a recent review of the literature using ML in drought prediction, the drought indices, dataset, and performance metrics.

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APA Style
Sundararajan, K., Garg, L., Srinivasan, K., Bashir, A.K., Kaliappan, J. et al. (2021). A contemporary review on drought modeling using machine learning approaches. Computer Modeling in Engineering & Sciences, 128(2), 447-487. https://doi.org/10.32604/cmes.2021.015528
Vancouver Style
Sundararajan K, Garg L, Srinivasan K, Bashir AK, Kaliappan J, Ganapathy GP, et al. A contemporary review on drought modeling using machine learning approaches. Comput Model Eng Sci. 2021;128(2):447-487 https://doi.org/10.32604/cmes.2021.015528
IEEE Style
K. Sundararajan et al., "A Contemporary Review on Drought Modeling Using Machine Learning Approaches," Comput. Model. Eng. Sci., vol. 128, no. 2, pp. 447-487. 2021. https://doi.org/10.32604/cmes.2021.015528

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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