Machine Learning With Metaheuristic Algorithms For Sustainable Water Resources Management
Download Machine Learning With Metaheuristic Algorithms For Sustainable Water Resources Management PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Machine Learning With Metaheuristic Algorithms For Sustainable Water Resources Management book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management
The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management.
Climate Risk and Sustainable Water Management
Author: Qiuhong Tang
language: en
Publisher: Cambridge University Press
Release Date: 2022-04-07
A comprehensive interdisciplinary exploration of climate risks to water security for students, researchers, civil and environmental engineers, and management professionals.
Optimizing Smart and Sustainable Agriculture for Sustainability
This reference text addresses the importance of smart crop management for increasing yield and presents a framework for smart monitoring and regulation of crop observation. Further, it comprehensively covers important topics such as spatial decision support systems for precision farming, swarm intelligence in the optimal management of aquaculture farms, and intelligent harvesting algorithms for improving productivity. This book: • Presents meta-heuristic algorithms for optimization, economic crop planning, and use of effective water resource management. • Discusses spatial decision support systems for crop productivity management, watershed management, and precision farming. • Illustrates swarm intelligence-based optimization techniques, data mining, and machine learning methods for aquaculture operations. • Highlights artificial intelligence and machine learning-based harvesting algorithms for improving productivity. • Explains the use of green Internet of Things security solutions for agriculture, plant condition management, and greenhouse simulation. It is primarily written for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, agricultural science, and information technology.