Guest Editors
Dr. Alban Kuriqi, CERIS, Universidade de Lisboa, Portugal.
Dr. Ozgur Kisi, Ilia State University, Georgia.
Dr. Andrzej Wałęga, University of Agriculture in Krakow, Poland.
Summary
Rapid population growth, high agricultural use, and industrial development, coupled with climate changes during the past few decades, have induced an increasing pressure on land and particularly on water resources in almost all regions around the world. Therefore, enhancing the water resources management strategies is crucial to fulfill the current human needs and ensure sustainable development for future generations. On the other side, because of the increasing competition for water resources, fluvial ecosystems are severely threatened. Thus, water resources planning and management require a profound understanding of several complex hydrological processes which are currently altered to a considerable degree due to climate changes and anthropogenic factors.
Water resource planning and management involve many uncertainties related to hydrological and social aspects that are highly non-linear and stochastic. For instance, the prediction of water availability is highly influenced by evaporation and precipitation; a high level of uncertainty characterizes both these two components of the hydrological cycle. On the other side, the demand patterns also have a high level of uncertainty due to social behavior and awareness towards water-saving and ecosystem protection. Researchers and engineers have been developing a handful of empirical and soft computing models for decades to decrease the level of uncertainty. While the empirical models have several limitations mainly related to the data requirement and spatial scale at which they can be applied, the soft computing models have shown great potential and flexibility regarding the data requirement and their applicability at different spatial applications. In this regard, it is widely recognized that adaptation of the fuzzy theory to solve water resources planning and management problems represents an essential turning point in the evolution of the modern concept of uncertainty and decision-making. After fuzzy theory, many other soft computing models emerged and were applied to solve different non-linear and stochastic issues related to hydrological processes and water resources management issues.
Soft computing models can model highly non-linear and stochastic phenomena and are becoming very popular to monitor, analyze, and predict different hydrological and ecological processes. Soft computing models can also construct predictive models for decision support in water resources management and planning, natural hazard risk prediction and prevention, and many other environmental-related issues.
The primary goal of this Special Issue to attract novel research, review articles, and technical notes on the applications of advanced soft computing modeling strategies to simulate different processes related to water resources planning and management, hydrology, and ecology. Thus, submitted work should advance further understanding of the potential of the soft computing models in solving very complex processes related to decision-making strategies to improve water resources planning and management at a distinct level.
Potential topics welcomed in this SI include but are not limited to the following:
• Advanced soft computing models in improving the irrigation efficiency;
• Soft computing models in improving the droughts prediction;
• Water scarcity and climate change;
• Decision tools and management-based models;
• Water resources management in arid and semi-arid regions;
• Time series prediction in hydrology;
• Watershed Monitoring and management;
• Sustainable utilization and management of water resources;
• Complexity in water resources planning and management;
• Water supply systems;
• Fluvial ecosystem and environmental flows;
• Application of soft computing models for fluvial habitat modeling;
• Application of soft computing to estimate environmental flows.
Keywords
• Soft computing;
• Hybrid model;
• Artificial intelligence;
• Irrigation systems;
• Machine learning;
• Water management;
• Wavelet system prediction;
• Drought modeling;
• Hydrological extreme.
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