Water scarcity prediction for global region using machine learning Online publication date: Mon, 01-Feb-2021
by Shubra Jain; Ankit Kumar Parida; Suresh Sankaranarayanan
International Journal of Water (IJW), Vol. 14, No. 1, 2020
Abstract: Water is a big challenge not only in India but in many countries of the world. Machine learning and forecasting model has been employed towards water demand and ground water level prediction. But in terms of water scarcity, much less work has been carried out by employing machine learning algorithms like 'artificial neural network' (ANN) and 'grey forecasting' model for forecasting water scarcity and none has focused on historical data like water availability, water consumption for a particular area and stress value for predicting water scarcity. So accordingly, we here have developed a water scarcity prediction system based on historical data by employing 'deep neural networks' which is an advanced form of 'artificial neural networks'. We have also compared 'deep neural network' with existing machine learning algorithms such as "support vector machine (SVM), logistic regression and Naive Bayes". From the analysis of algorithms based on dataset, deep neural networks have been found as the best prediction model for water scarcity.
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