Simulations and Projections Applied in Different Water Systems: Hydrological and Hydrogeological Models Selection, Errors, and Uncertainties
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".
Deadline for manuscript submissions: closed (30 August 2023) | Viewed by 1401
Special Issue Editors
Interests: numerical and AI modelling; water quality, groundwater vulnerability; risk analysis and assessment; multiple model discipline; uncertainty
Interests: groundwater; hydrogeology; surface and groundwater interactions; subsurface characterization; riverbank seepage; uncertainty analysis
Special Issues, Collections and Topics in MDPI journals
Interests: graph modeling for conflict resolution; water resource allocation
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Special Issue Information
Dear Colleagues,
In recent decades, various numerical and artificial intelligence codes have been adopted to simulate, calibrate, and predict hydrological and hydrogeological systems. Several models may lead to the same or similar results for each water system, and the main problem is selecting the superior model (single model) or using the multiple model discipline between them. The error of the model results and uncertainty of the input data, within and between models, are the main criteria for model evaluation.
Different approaches are applied to select a superior model or use multiple models (MMs) to generate optimal results from numerical and artificial intelligence models in different water systems, such as the prediction of runoff, rainfall, groundwater level, water contaminants and estimation of groundwater vulnerability and risk, land subsidence and so on. The main challenge is to determine whether single or multiple models are preferred for the simulation/projection of water systems. This Special Issue focuses on these approaches in hydrological and hydrogeological sciences, as follows:
- Estimation of the uncertainty of artificial intelligence and numerical models;
- Different approaches for efficiency evaluation of artificial intelligence and numerical models;
- Superior model selection approaches for artificial intelligence and numerical models;
- Different approaches for aggregate models or generating multiple models (MMs) of the artificial intelligence and numerical models;
- Comparison of advantages and disadvantages of multiple models and superior models.
Prof. Dr. Ata Allah Nadiri
Prof. Dr. Frank Tsai
Dr. Mohammad Reza Nikoo
Guest Editors
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Keywords
- inverse problems
- numerical models
- artificial intelligence
- machine learning
- uncertainty analysis
- single models
- multiple models
- groundwater hydraulics
- water quality
- groundwater vulnerability and risk
- land subsidence
- rainfall–run off models
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