Overview

Evolutionary algorithms and allied fields are getting more visibility as well as familiarity due to their numerous flexibilities such as handling high-dimensional non-linear problems and more. This book will help budding researchers to formulate their research problems, and comprises 10 chapters: three on optimization, five on machine learning algorithms, one on Internet of Things, and one on remote sensing. In Focus – a book series that showcases the latest accomplishments in water research. Each book focuses on a specialist area with papers from top experts in the field. It aims to be a vehicle for in-depth understanding and inspire further conversations in the sector.

Full Product Details

Author:   Dasika Nagesh Kumar ,  Komaragiri Srinivasa Raju
Publisher:   IWA Publishing
Imprint:   IWA Publishing
Dimensions:   Width: 15.60cm , Height: 1.80cm , Length: 23.40cm
ISBN:  

9781789063240

ISBN 10:   1789063248
Pages:   200
Publication Date:   15 July 2022
Audience:   College/higher education ,  Professional and scholarly ,  Tertiary & Higher Education ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

Editorial: Evolutionary Algorithms in Water Resources Dasika Nagesh Kumar & Komaragiri Srinivasa Raju Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: a state-of-the-art review M. Janga Reddy & D. Nagesh Kumar Multi-objective fuzzy optimization for sustainable irrigation planning Jyotiba B. Gurav & D. G. Regulwar Application of artificial neural network for predicting water levels in Hooghly estuary, India Kalyan Kumar Bhar & Susmita Bakshi Decision tree-based reduction of bias in monthly IMERG satellite precipitation dataset over India Shushobhit Chaudhary, C. T. Dhanya Comparative performance evaluation of self-adaptive differential evolution with GA, SCE and DE algorithms for the automatic calibration of a computationally intensive distributed hydrological model Saswata Nandi & M. Janga Reddy Quantifying natural organic matter concentration in water from climatological parameters using different machine learning algorithms Sina Moradi, Anthony Agostino, Ziba Gandomkar, Seokhyeon Kim, Lisa Hamilton, Ashish Sharma, Rita Henderson & Greg Leslie Modelling hydrological responses under climate change using machine learning algorithms – semi-arid river basin of peninsular India G. Sireesha Naidu, M. Pratik & S. Rehana Real-time monitoring of water level and storage dynamics of irrigation tank using IoT Muthiah Krishnaveni, S. K. Praveen Kumar, E. Arul Muthusamy, J. Kowshick & K. G. Arunya Development of algorithms for evaluating performance of flood simulation models with satellite-derived flood Tushar Surwase, P. Manjusree, Sachin Prakash & Saikiran Kuntla Modelling runoff in an arid watershed through integrated support vector machine Sandeep Samantaray & Dillip K. Ghose

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