Soft Computing in Hydrology: Application of Machine Learning, Optimization Algorithms, and Data Mining

In recent years, appropriate approaches such as soft computing have been widely used in many research areas and applications and it proved a suitable performance in environmental sciences such as hydrology and water resources management. Soft computing allows us to understand more about water resources management; However, soft computing approaches can give to researchers a new view for solving the challenges and overcoming the water resources management issues. In addition, the high capability of soft computing such as big data handling, handling the complexity of problems, high simulation speed, and high accuracy results can be used for future potential researches in hydrological studies. In this Special Issue editor would like to invite research works which incorporate soft computing techniques in hydrology and water resources management, such as (but not restricted to):

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Water resources management by soft computing approaches

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Hydrological modeling: application of soft computing

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Using optimization algorithms for water managing

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Data mining in hydrological studies

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Studying about irrigation efficiency by soft computing approaches

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Water management challenges: solutions by soft computing approaches

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Integrated time series analysis techniques for hydrological studies

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Improving precision of hydrologic models by soft computing approaches

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Decision making in water resources projects

Dr. Babak Mohammadi
Guest Editor

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