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Accuracy enhancement for monthly evaporation predicting model utilizing evolutionary machine learning methods

  • S. MohamadiEmail author
  • M. Ehteram
  • A. El-Shafie
Original Paper
  • 5 Downloads

Abstract

Evaporation is an important parameter for water resource management. In this article, two case studies with different climates were considered in the prediction of monthly evaporation. The optimization algorithms, namely shark algorithm (SA) and firefly algorithms (FFAs), were used to train the adaptive neuro-fuzzy interface system (ANFIS), multilayer perceptron (MLP) model and radial basis function (RBF) model for the prediction of monthly evaporation. The monthly weather data from two stations, Mianeh station and Yazd station, operated by the Iran Meteorological Service were used to examine the proposed models. In the quantitative analysis, the hybrid ANFIS-SA improved the MAE index over the ANFIS, RBF, MLP, RBF-SA, MLP-SA, RBF-FFA, MLP-FFA and ANFIS-SA up to 47% during training and to 51% during testing while examining Yazd station. It should be mentioned that the higher RSR and MAE were attained by the hybrid soft computing (ANN-FFA, RBF-FFA and ANFIS-FFA) models in two stations. The results proved that the developed ANFIS models that have been integrated with shark algorithms could be considered as a powerful tool for predicting evaporation.

Keywords

Soft computing models Optimization algorithms Water resource management Evaporation 

Abbreviations

A

Linguistic fuzzy

B

Linguistic fuzzy

AI

Artificial intelligence

ANN

Artificial neural network

ANFIS

Adaptive neuro-fuzzy interface system

a

Premise parameters

b

Premise parameters

c

Premise parameters

FFA

Firefly algorithm

GP

Genetic programming

M

Number of points in the local search

MLP

Multilayer perceptron

MF

Membership function

PCA

Principal component analysis

p

Consequent parameter

q

Consequent parameter

r

Consequent parameter

rij

Euclidean distance

RBF

Radial basis function

SSA

Shark algorithm

x

Input

y

Output

\( Z_{i}^{k + 1,m} \)

Shark position after rotational movement

\( \beta_{0} \)

Attractiveness

\( \mu_{{A_{i} }} \)

The shape of members

\( \mu_{{B_{i} }} \)

The shape of members

Notes

Acknowledgement

The authors are grateful to the University of Malaya, Malaysia, for supporting the study.

Funding

This study was funded by the University of Malaya (University of Malaya Research Grant GPF082A-2018).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Islamic Azad University (IAU) 2020

Authors and Affiliations

  1. 1.Department of Ecology, Institute of Science and High Technology and Environmental SciencesGraduate University of Advanced TechnologyKermanIran
  2. 2.Department of Water Engineering and Hydraulic Structures, Faculty of Civil EngineeringSemnan UniversitySemnanIran
  3. 3.Civil Engineering Department, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia

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