J
J. Sreekanth
Researcher at Commonwealth Scientific and Industrial Research Organisation
Publications - 25
Citations - 746
J. Sreekanth is an academic researcher from Commonwealth Scientific and Industrial Research Organisation. The author has contributed to research in topics: Aquifer & Groundwater. The author has an hindex of 12, co-authored 24 publications receiving 624 citations. Previous affiliations of J. Sreekanth include Cooperative Research Centre & James Cook University.
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Multi-objective management of saltwater intrusion in coastal aquifers using genetic programming and modular neural network based surrogate models.
TL;DR: Two different surrogate models based on genetic programming and modular neural network are developed and linked to a multi-objective genetic algorithm (MOGA) to derive the optimal pumping strategies for coastal aquifer management, considering two objectives.
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Coupled simulation‐optimization model for coastal aquifer management using genetic programming‐based ensemble surrogate models and multiple‐realization optimization
J. Sreekanth,Bithin Datta +1 more
TL;DR: The ensemble‐based approach is found to provide reliable solutions for coastal aquifer management while retaining the advantage of surrogate models in reducing computational burden.
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Comparative Evaluation of Genetic Programming and Neural Network as Potential Surrogate Models for Coastal Aquifer Management
J. Sreekanth,Bithin Datta +1 more
TL;DR: This study evaluates genetic programming (GP) as a potential surrogate modeling tool and compares the advantages and disadvantages with the neural network based surrogate modeling approach and develops two linked simulation optimization models based on ANN and GP.
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Review: Simulation-optimization models for the management and monitoring of coastal aquifers
J. Sreekanth,Bithin Datta +1 more
TL;DR: In this article, the authors review the literature on the application of simulation-optimization approaches for management and monitoring of coastal aquifers and provide an efficient framework for preliminary designs of saltwater-intrusion management schemes.
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Stochastic and Robust Multi-Objective Optimal Management of Pumping from Coastal Aquifers Under Parameter Uncertainty
TL;DR: Genetic programming based ensemble surrogate models are utilized to characterize coastal aquifer water quality responses to pumping, under parameter uncertainty, and are coupled with multiple realization optimization for the stochastic and robust optimization of groundwater management in coastal aquifers.