Neurocomputing in surface water hydrology and hydraulics: A review of two decades retrospective, current status and future prospects - Zounemat-Kermani, Mohammad; Matta, Elena; Cominola, Andrea; Xia, Xilin; Zhang, Qing; Liang, Qiuhua; Hinkelmann, Reinhard (Journal of hydrology (Amsterdam), 2020)
•Review of neurocomputing methods in surface water hydrology and hydraulic sciences.•Detailed analysis on six relevant topics in surface water hydrology and hydraulics.•Past and current research trends, main findings, and gaps for future exploration.•Problem-specific guidelines and recommendations for future development. Neurocomputing methods have contributed significantly to the advancement of modelling techniques in surface water hydrology and hydraulics in the last couple of decades, primarily due to their vast performance advantages and usage amenity. This comprehensive review considers the research progress in the past two decades, the current state-of-the-art, and future prospects of the application of neurocomputing to different aspects of hydrological sciences, i.e., quantitative surface hydrology and hydraulics. An extensive literature survey, by running over more than 800 peer-reviewed papers, outlines and concisely explores the past and recent tendencies in the application of conventional neural-based approaches and modern neurocomputing models in relevant topics of hydrological and hydraulic sciences. Apart from segregated descriptions and analyses of the main facets of surface hydrology and hydraulics, this review offers a practical summary of prevailing neurocomputing methods used in different subfields of hydrology and water engineering. Six relevant topics to modelling hydrological and hydraulic sciences are articulated and analysed, including modelling of water level in surface water bodies, flood and risk assessment, sediment transport in river systems, urban water demand prediction, modelling flow through hydro-structures, and hydraulics of sewers. This review is meant to be a mainstream guideline for researchers and practitioners whose work is associated with data mining and machine learning methods in various areas of water engineering and hydrological sciences to assist them to decide on suitable methods, network structures and modelling strategies for a given problem.