The Potential of Big Data and Machine Learning for Ground Water Quality Assessment and Prediction

Abstract

Water, a priceless gift from nature, acts as Earth's matrix, medium, and life-sustaining substance. While the planet is predominantly covered by water, only 3% is available as freshwater, with 99% of that sourced underground. This groundwater supplies nearly half of the global population. Unfortunately, many areas have experienced recent pollution and overexploitation of this precious resource, adversely affecting the development, sustainability, and economy of people and the planet. Therefore, the evaluation and prediction of Groundwater Quality become indispensable for effective water resource management. Nevertheless, with the continuous advancement of technology, the sheer magnitude of data in Groundwater Science surpasses the capabilities of traditional methods to store, process, and analyse it accurately, leading to erroneous assessments and predictions. Machine Learning is among the promising advanced techniques for processing and extracting new insights from such “Big Data”. This paper explores the scope of Big Data and Machine Learning algorithms for Ground Water Quality Assessment and Prediction (GWQAP). The primary objective of this paper is to identify the impact of Big Data and the effectiveness of Machine learning models in GWQAP. This paper discusses the significance of different Big Data techniques and Machine Learning algorithms for GWQAP. It includes a systematic review of various recently deployed Big Data and Machine Learning applications for Groundwater Quality Management. It also highlights the challenges and future scope of Big Data and Machine Learning in Groundwater Quality Management. Ultimately, this paper is the first step towards enhancing our understanding towards Ground Water Resource Management through Big Data and Machine Learning applications. According to the study, Big Data and Machine Learning can substantially impact water resource management and analysis. Big Data ensures new possibilities for data-driven discovery and decision-making if correctly assessed and managed.