Utilizing classic evolutionary algorithms to assess the Brown trout (Salmo trutta) habitats by ANFIS-based physical habitat model

Sedighkia, Mahdi, Datta, Bithin, and Abdoli, Asghar (2022) Utilizing classic evolutionary algorithms to assess the Brown trout (Salmo trutta) habitats by ANFIS-based physical habitat model. Modeling Earth Systems and Environment, 8. pp. 857-869.

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Abstract

Present study evaluates the application of coupled evolutionary algorithm- adaptive neuro-fuzzy inference system in the Brown trout riverrine habitats. We implemented the proposed method in the Lar national park as one of the most important Brown trout habitats in the southern Caspian Sea basin. Two classic evolutionary algorithms including the genetic algorithm and the particle swarm optimization were coupled with adaptive neuro-fuzzy inference system. Moreover, two conventional training methods including backpropagation and hybrid algorithm were utilized. Evaluation of developed models was carried out in two stages including assessment of habitat suitability index in observed habitats and using practical hydraulic simulation in a representative reach. Measurement indices consisting of root mean square error, mean absolute error, Nash–Sutcliffe model efficiency coefficient, reliability and vulnerability indices and fuzzy technique of order preference similarity to the ideal solution as decision-making system were used. Results demonstrate the efficiency of the coupled evolutionary algorithm- adaptive neuro-fuzzy inference system to simulate hydraulic habitats of the Brown trout. The first stage of evaluation indicates particle swarm optimization is the best method. However, practical hydraulic simulation corroborates GA is the best method for the training process. Evaluations demonstrate that backpropagation is not an appropriate method for ANFIS-based hydraulic habitat simulation.

Item ID: 66961
Item Type: Article (Research - C1)
ISSN: 2363-6211
Keywords: Back propagation, Genetic algorithm, Hybrid algorithm, Particle swarm optimization, Physical habitat simulation
Copyright Information: © The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature 2021
Date Deposited: 01 Jun 2022 00:25
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