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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.

Water is a necessity for all living and non-living organisms on this planet. It is understood that clean water sources are decreasing by the day, and the rapid rise of Industries and technology has led to an increase in the release of toxic effluents that are discharged into the environment. Wastewater released from Industries, agricultural waste, and municipalities must be treated before releasing into the environment as they contain harmful pollutants such as organic dyes, pharmaceuticals wastes, inorganic materials, and heavy metal ions. If not controlled, they can cause serious risks to human beings' health and contaminate our environment. Membrane filtration is a proven method for the filtration of various harmful chemicals and microbes from water. Carbon nanomaterials are applied in wastewater treatment due to their high surface area, making them efficient adsorbents. Carbon nanomaterials are being developed and utilized in membrane filtration for the treated wastewater before getting discharged with the rise of nanotechnology. This review studies carbon nanomaterials like fullerenes, graphenes, and CNTs incorporated in the membrane filtration to treat wastewater contaminants. We focus on these CNM based membranes and membrane technology, their properties and applications, and how they can enhance the commonly used membrane filtration performance by considering adsorption rate, selectivity, permeability, antimicrobial disinfectant properties, and compatibility with the environment.

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