Research series in civil engineering: Soft computing applications in hydro-environment

Abstract

In nature, hydrological components such as rainfall, inflow, infiltration and evaporation are required when attempting to design a water resources system. Hence, studies on the accuracy of these components for the planning and management of various types of water resources are essential. However, these components non-linear in nature and most of the conventional models unable to capture the stochastic nature and the non-linearity of these components. Recently, Artificial Intelligent (AI) techniques have been widely used in developing non-linear models for solving engineering problems. This book is focusing on presenting the implementation of Artificial Intelligent (AI) techniques in optimizing and predicting some of the water resources components such as river flow, sediment transport and precipitations. More recently, with the existence of the new generation of soft computing models, hydrologists and environmentalists switched to advanced models and to artificial intelligence techniques to tackle the non-linearity and the stochasticity problem associate with water resources. Therefore, this book is focusing on presenting the implementation of these techniques in optimizing and predicting some of the water resources components such as river flow, sediment transport and precipitations. The book consists of four chapters of related studies in hydrological modeling which will be of direct interest to universities, research institutions, private and public sector institutions, international organizations and for the water and the associated resources sectors. Keywords: Hydrological Modeling; Machine Learning; Rainfall Forecasting; Flood Analysis; Sediment Transport Prediction