Engineers have attempted to solve water resources engineering problems with the help of empirical, regression-based and numerical models. Empirical models are not universal, nor are regression-based models. The numerical models are, on the other hand, physics-based but require substantial data measurement and parameter estimation. Hence, there is a need to employ models that are robust, user-friendly, and practical and that do not have the shortcomings of the existing methods. Artificial intelligence methods meet this need. Soft Computing in Water Resources Engineering introduces the basics of artificial neural networks (ANN), fuzzy logic (FL) and genetic algorithms (GA). It gives details on the feed forward back propagation algorithm and also introduces neuro-fuzzy modelling to readers. Artificial intelligence method applications covered in the book include predicting and forecasting floods, predicting suspended sediment, predicting event-based flow hydrographs and sedimentographs, locating seepage path in an earth-fill dam body, and the predicting dispersion coefficient in natural channels. The author also provides an analysis comparing the artificial intelligence models and contemporary non-artificial intelligence methods (empirical, numerical, regression, etc.). The ANN, FL, and GA are fairly new methods in water resources engineering. The first publications appeared in the early 1990s and quite a few studies followed in the early 2000s. Although these methods are currently widely known in journal publications, they are still very new for many scientific readers and they are totally new for students, especially undergraduates. Numerical methods were first taught at the graduate level but are now taught at the undergraduate level. There are already a few graduate courses developed on AI methods in engineering and included in the graduate curriculum of some universities. It is expected that these courses, too, will soon be taught at the undergraduate levels.

Engineers have attempted to solve water resources engineering problems with the help of empirical, regression-based and numerical models. Empirical models are not universal, nor are regression-based models. The numerical models are, on the other hand, physics-based but require substantial data measurement and parameter estimation. Hence, there is a need to employ models that are robust, user-friendly, and practical and that do not have the shortcomings of the existing methods. Artificial intelligence methods meet this need. Soft Computing in Water Resources Engineering introduces the basics of artificial neural networks (ANN), fuzzy logic (FL) and genetic algorithms (GA). It gives details on the feed forward back propagation algorithm and also introduces neuro-fuzzy modelling to readers. Artificial intelligence method applications covered in the book include predicting and forecasting floods, predicting suspended sediment, predicting event-based flow hydrographs and sedimentographs, locating seepage path in an earth-fill dam body, and the predicting dispersion coefficient in natural channels. The author also provides an analysis comparing the artificial intelligence models and contemporary non-artificial intelligence methods (empirical, numerical, regression, etc.). The ANN, FL, and GA are fairly new methods in water resources engineering. The first publications appeared in the early 1990s and quite a few studies followed in the early 2000s. Although these methods are currently widely known in journal publications, they are still very new for many scientific readers and they are totally new for students, especially undergraduates. Numerical methods were first taught at the graduate level but are now taught at the undergraduate level. There are already a few graduate courses developed on AI methods in engineering and included in the graduate curriculum of some universities. It is expected that these courses, too, will soon be taught at the undergraduate levels.

Soft Computing Techniques in Solid Waste and Wastewater Management is a thorough guide to computational solutions for researchers working in solid waste and wastewater management operations. This book covers in-depth analysis of process variables, their effects on overall efficiencies, and optimal conditions and procedures to improve performance using soft computing techniques. These topics coupled with the systematic analyses described will help readers understand various techniques that can be effectively used to achieve the highest performance. In-depth case studies along with discussions on applications of various soft-computing techniques help readers control waste processes and come up with short-term, mid-term and long-term strategies. Waste management is an increasingly important field due to rapidly increasing levels of waste production around the world. Numerous potential solutions for reducing waste production are underway, including applications of machine learning and computational studies on waste management processes. This book details the diverse approaches and techniques in these fields, providing a single source of information researchers and industry practitioners. It is ideal for academics, researchers and engineers in waste management, environmental science, environmental engineering and computing, with relation to environmental science and waste management. - Provides a comprehensive reference on the implementation of soft computing techniques in waste management, drawing together current research and future implications - Includes detailed algorithms used, enabling authors to understand and appreciate potential applications - Presents relevant case studies in solid and wastewater management that show real-world applications of discussed technologies

Presents knowledge and experience of soft computing techniques in civil engineering. The principal concern of the book is to show how soft computing techniques can be applied to solve problems in research and practice.

This book comprises the proceedings of the 26th International Conference on Hydraulics, Water Resources and Coastal Engineering (HYDRO 2021) focusing on broad spectrum of emerging opportunities and challenges in the field of soft computing and geospatial techniques in water resources engineering. It covers a range of topics, including, but not limited to, satellite derived data for hydrologic applications, GIS and RS applications in water resources management, rainfall and streamflow prediction, hydro-informatics, data driven and artificial intelligent based hydrological modelling, optimization of water resources systems, etc. Presenting recent advances in the form of illustrations, tables, and text, it offers readers insights for their own research. In addition, the book addresses fundamental concepts and studies in the field of Soft Computing and Geospatial Techniques in Water Resources Engineering, making it a valuable resource for both beginners and researchers wanting to further their understanding of hydraulics, water resources and coastal engineering.

This book comprises proceedings of the 28th International Conference on Hydraulics, Water Resources, River and Coastal Engineering (HYDRO 2023). It focuses on emerging opportunities and challenges in the field of soft computing and geospatial techniques in water resources engineering. The book covers a range of topics including, but not limited to, satellite-derived data for hydrologic applications, Geospatial Information System (GIS) and Remote Sensing (RS) applications in water resources management, rainfall and streamflow prediction, hydro-informatics, data-driven and artificial intelligent-based hydrological modelling, optimization of water resources systems. The book presents these topics in the form of illustrations and tables, thereby providing the readers with an in-depth insight into the recent research. It also addresses fundamental concepts and studies in the field of soft computing and geospatial techniques in water resources engineering, making it a valuable resource for researchers and professionals working in the fields of hydraulics, water resources and coastal engineering.