Land use and water resources are two major environmental issues which necessitate conservation, management, and maintenance practices through the use of various engineering techniques. Water scientists and environmental engineers must address the various aspects of flood control, soil conservation, rainfall-runoff processes, and groundwater hydrology. Watershed Management and Applications of AI provides the necessary principles of hydrology to provide practical strategies useful for the planning, design, and management of watersheds. The book also synthesizes novel new approaches, such as hydrological applications of machine learning using neural networks to predict runoff and using artificial intelligence for the prediction of groundwater fluctuations.

Features:

• Presents hydrologic analysis and design along with soil conservation practices through proper watershed management techniques
• Provides analysis of land erosion and sediment transport in watersheds from small to large scale
• Includes estimations for runoff using different methodologies with systematic approaches for each
• Discusses water harvesting and development of water yield catchments

This book will be a valuable resource for students in hydrology courses, environmental consultants, water resource engineers, and researchers in related water science and engineering fields.

Land use and water resources are two major environmental issues which necessitate conservation, management, and maintenance practices through the use of various engineering techniques. Water scientists and environmental engineers must address the various aspects of flood control, soil conservation, rainfall-runoff processes, and groundwater hydrology. Watershed Management and Applications of AI provides the necessary principles of hydrology to provide practical strategies useful for the planning, design, and management of watersheds. The book also synthesizes novel new approaches, such as hydrological applications of machine learning using neural networks to predict runoff and using artificial intelligence for the prediction of groundwater fluctuations.

Features:

• Presents hydrologic analysis and design along with soil conservation practices through proper watershed management techniques
• Provides analysis of land erosion and sediment transport in watersheds from small to large scale
• Includes estimations for runoff using different methodologies with systematic approaches for each
• Discusses water harvesting and development of water yield catchments

This book will be a valuable resource for students in hydrology courses, environmental consultants, water resource engineers, and researchers in related water science and engineering fields.

Land use and water resources are two major environmental issues which necessitate conservation, management, and maintenance practices through the use of various engineering techniques. Water scientists and environmental engineers must address the various aspects of flood control, soil conservation, rainfall-runoff processes, and groundwater hydrology. Watershed Management and Applications of AI provides the necessary principles of hydrology to provide practical strategies useful for the planning, design, and management of watersheds. The book also synthesizes novel new approaches, such as hydrological applications of machine learning using neural networks to predict runoff and using artificial intelligence for the prediction of groundwater fluctuations.

Features:

• Presents hydrologic analysis and design along with soil conservation practices through proper watershed management techniques
• Provides analysis of land erosion and sediment transport in watersheds from small to large scale
• Includes estimations for runoff using different methodologies with systematic approaches for each
• Discusses water harvesting and development of water yield catchments

This book will be a valuable resource for students in hydrology courses, environmental consultants, water resource engineers, and researchers in related water science and engineering fields.