Machine Learning and Data Analytics for Environmental Science: A Review, Prospects and Challenges

Abstract

Innovations in Machine Learning and Data Analytics can possibly affect numerous aspects of Environmental Science (ES). Data Analytics refers to a collection of data resources indicated in terms of variety, velocity, veracity and volume. Big data contributes to the ES arena in applications such as weather forecasting, energy sustainability and disaster management with the advent of techniques such as Remote Sensing, Information and Communication technologies. Though big data is used to accomplish data analysis and interpretation for ES, there are still requirements for efficient ways of data storage, processing and retrieval. Machine Learning and Deep Learning are the sub fields of artificial intelligence which deals with training the models to learn from data without being explicitly programmed. When Machine Learning and Deep Learning are combined together it is possible to unleash the supremacy of data analytics. These techniques show high prospective for process optimization, information-centric decision making and scientific discovery. Scientific developments like these will assist ES to make real time autonomous decisions by extracting useful insights from huge data. These advancements also aid in bridging the gap between the theoretical backgrounds on ES to practical implementation. The primary objective of this survey is to figure out the basic concepts of Machine Learning, Deep Learning, and Data Analytics and find the state-of-the-art applications in ES, and observe the impending benefits of information-centric investigation on ES.