A Contemporary Review on Drought Modeling Using Machine Learning Approaches

• Published in: Computer modeling in engineering & sciences Vol. 128; no. 2; pp. 447 - 487
• Main Authors: Sundararajan, Karpagam, Garg, Lalit, Srinivasan, Kathiravan, Bashir, Ali Kashif, Kaliappan, Jayakumar, Ganapathy, Ganapathy Pattukandan, Selvaraj, Senthil Kumaran, Meena
• Format: Journal Article
• Language: English
• Published: Henderson Tech Science Press 01.01.2021
• Subjects: Adaptive systems; Algorithms; Artificial intelligence; Artificial neural networks; Autoregressive models; Back propagation; Back propagation networks; Bayesian analysis; Decision trees; Deep learning; Drought; Drought Forecasting; Drought Indices; Dynamic Method; Evolutionary algorithms; Fuzzy Logic; Hybrid Method; Literature reviews; Machine Learning; Markov chains; Natural disasters; Natural resources; Performance measurement; Regression; Stochastic Models; Support vector machines; Weather forecasting
• ISSN: 1526-1492; 1526-1506; 1526-1506
• DOI: 10.32604/cmes.2021.015528

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 singular 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.