Manuscript Topics

Hydroinformatics was first proposed by Professor Mike Abbott in the influential 1991 publication. After that, hydroinformatics was organized as a technology of favorable assimilation for numerical modeling and data process. Climate crisis and population growth require the computational tools to solve the increasing water problems in the world. Since the drought and flooding risks influence the human being’s accident and economic damage tremendously, it is recommended that the continuous researches and collaborative works to boost our knowledge for mitigating the drought and flooding in human environment.

This is an interdisciplinary issue for requesting the collaborative works from the various researchers and scientists including hydrologist, agronomist, biologist, meteorologist, hydro engineer, and environmental engineer and so on. The target of addressed Special Issue is to optimize the supply and offering of finite water resources and maintain the stable water quality. To accomplish the appropriate solution of complex water resources and quality problems, the trustworthy and steady methods are required. The addressed Special Issue will feature the advanced technology and development in the field of hydrologic, water resources, and water quality operation (e.g., prediction, forecasting, modeling, and simulation). The focus concentrates on the progressive computational approaches such as machine learning, deep learning, data-driven, and soft computing for optimizing the water resources and water quality. The accessible capacity of computational tools today permits the researchers to implement the desirable challenges in water resources operation which were impossible activity a few decades ago. Even the present circumstances, however, the time requiring for the adequate operation is insufficient for various engineering application and development including construction of flood warning system, calibration and verification of model parameters, real-time modeling, and the error reduction of uncertainty analysis and so on. To solve the mentioned problems, the speed improvement of calculating model seems to be encouraging scheme. It does not demand the large investment to build new hardware and software system, and the duplicate tools can be applied to solve diverse complications. The core arguments of the addressed Special Issue include but not restricted to the following:

(1) Implementation and application of state-of-the-art machine learning, deep learning, data-driven, and soft computing approaches for hydrologic and water quality prediction, forecasting, modelling, and simulation including precipitation, discharge, water level, streamflow, groundwater, evaporation, evapotranspiration, water temperature, dissolved oxygen, and biochemical oxygen demand and so on.
(2) Combination of various data preprocessing techniques and machine learning, deep learning, data-driven, and soft computing approaches to improve the accuracy of standalone model.
(3) Utilization of diverse metaheuristic algorithms with machine learning, deep learning, data-driven, and soft computing approaches to optimize the standalone model.
(4) Coupling of ensemble models with machine learning, deep learning, data-driven, and soft computing approaches for solving the different water resources problems.

Keywords
Machine learning; Deep learning; Data-driven; Soft computing; Data preprocessing; Metaheuristic algorithms; Ensemble models; Water resources management; Prediction; Forecasting; Modelling; Simulation; Streamflow; Evaporation and evapotranspiration; Groundwater; Flood; Drought; Water temperature; Dissolved oxygen; Biochemical oxygen demand; Total phosphorus

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