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BigData andmachine learning (ML) technologies have the potential to impactmany facets of environment andwatermanagement (EWM). BigData are information assets characterized by high volume, velocity, variety, and veracity. Fast advances in high-resolution remote sensing techniques, smart information and communication technologies, and socialmedia have contributed to the proliferation of BigData inmany EWMfields, such asweather forecasting, disastermanagement, smart water and energymanagement systems, and remote sensing. BigData brings about new opportunities for data-driven discovery in EWM, but it also requires new forms of information processing, storage, retrieval, as well as analytics.ML, a subdomain of artificial intelligence (AI), refers broadly to computer algorithms that can automatically learn fromdata.MLmay help unlock the power of BigData if properly integratedwith data analytics. Recent breakthroughs inAI and computing infrastructure have led to the fast development of powerful deep learning (DL) algorithms that can extract hierarchical features fromdata, with better predictive performance and less human intervention. Collectively BigData andML techniques have shown great potential for data-driven decisionmaking, scientific discovery, and process optimization. These technological advancesmay greatly benefit EWM, especially because (1)many EWMapplications (e.g. early floodwarning) require the capability to extract useful information from a large amount of data in autonomousmanner and in real time, (2)EWMresearches have become highlymultidisciplinary, and handling the ever increasing data volume/types using the traditional workflow is simply not an option, and last but not least, (3) the current theoretical knowledge aboutmany EWMprocesses is still incomplete, but whichmay now be complemented through data-driven discovery. A large number of applications onBigData andML have already appeared in the EWM literature in recent years. The purposes of this survey are to (1) examine the potential and benefits of data-driven research in EWM, (2) give a synopsis of key concepts and approaches in BigData andML, (3) provide a systematic review of current applications, andfinally (4) discussmajor issues and challenges, and recommend future research directions. EWM includes a broad range of research topics. Instead of attempting to survey each individual area, this review focuses on areas of nexus in EWM,with an emphasis on elucidating the potential benefits of increased data availability and predictive analytics to improving the EWMresearch.

How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions