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Evolutionary Algorithms in Water Resources [Pehme köide]

  • Formaat: Paperback / softback, 200 pages, kõrgus x laius x paksus: 234x156x18 mm
  • Sari: In Focus Special Book Series
  • Ilmumisaeg: 15-Jul-2022
  • Kirjastus: IWA Publishing
  • ISBN-10: 1789063248
  • ISBN-13: 9781789063240
Teised raamatud teemal:
  • Formaat: Paperback / softback, 200 pages, kõrgus x laius x paksus: 234x156x18 mm
  • Sari: In Focus Special Book Series
  • Ilmumisaeg: 15-Jul-2022
  • Kirjastus: IWA Publishing
  • ISBN-10: 1789063248
  • ISBN-13: 9781789063240
Teised raamatud teemal:
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.
Editorial: Evolutionary Algorithms in Water Resources vii
Dasika Nagesh Kumar
Komaragiri Srinivasa Raju
Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: a state-of-the-art review
1(54)
M. Janga Reddy
D. Nagesh Kumar
Multi-objective fuzzy optimization for sustainable irrigation planning
55(17)
Jyotiba B. Gurav
D. G. Regulwar
Application of artificial neural network for predicting water levels in Hooghly estuary, India
72(15)
Kalyan Kumar Bhar
Susmita Bakshi
Decision tree-based reduction of bias in monthly IMERG satellite precipitation dataset over India
87(20)
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
107(22)
Saswata Nandi
M. Janga Reddy
Quantifying natural organic matter concentration in water from climatological parameters using different machine learning algorithms
129(15)
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
144(18)
G. Sireesha Naidu
M. Pratik
S. Rehana
Real-time monitoring of water level and storage dynamics of irrigation tank using IoT
162(9)
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
171(14)
Tushar Surwase
P. Manjusree
Sachin Prakash
Saikiran Kuntla
Modelling runoff in an arid watershed through integrated support vector machine
185
Sandeep Samantaray
Dillip K. Ghose