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Hybrid signal processing/machine learning and PSO optimization model for conjunctive management of surface–groundwater resources

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Abstract

Conjunctive management of surface–groundwater resources systems by means of mathematical optimization–simulation techniques becomes an important issue for sustainable water resources development, namely in water-scarce regions. In this study, the particle swarm optimization (PSO) method has been coupled with a hybrid wavelet/ANFIS–fuzzy C-means (FCM) simulation model to determine the optimal agricultural irrigation water allocation in the Miandarband plain, western Iran. Firstly, the optimal amount of conveyed water (CW) from the Gavoshan Dam into the plain is determined by constrained PSO. The constraints are the long-term minimum monthly exceedance streamflows that are estimated for different exceedance probabilities—with a 70% value found to best reflect the average annual river inflow of 3.4 m3/s into the dam—using the two-parameter Weibull distribution as well as the classical Weibull nonparametric plotting position method. Then, based on the politically prioritized proportions of the dam’s allocated water for domestic, environmental and agricultural uses, as well as the share of the plain devoted to  agriculture, the optimal monthly CW available for the plain (= 112 MCM/a) is obtained. However, the subsequent estimation of the irrigation water request (IWR) (= 265.8 MCM/a), calculated by the FAO-56 method and using empirical crop coefficients of the present agricultural pattern in the plain, indicates that there is an irrigation water deficit of 153.1 MCM/a that must be made up by groundwater withdrawal (GW), in a way that neither waterlogging nor severe drop conditions in groundwater levels (GL) will occur. The latter are then calculated by the hybrid wavelet/ANFIS (FCM) model, wherefore good performance indicators R2 and RMSE, equal to 0.98 and 0.21 m and 0.94 and 0.31 m in the training and testing phases, respectively, are obtained. Finally, PSO and the hybrid model are coupled to simulate the GL fluctuations—with the above GL constraints—under conjunctive use of the optimal surface (CW) and groundwater resources (GW) in the Miandarband plain. In conclusion, the innovative coupled simulation/optimization model turns out to be a very useful tool for optimal and sustainable conjunctive management of surface–groundwater resources in an irrigation area.

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Abbreviations

AI:

Artificial intelligence

ANFIS:

Adaptive neuro-fuzzy inference

ANN:

Artificial neural network

CW:

Conveyed water

CWR:

Crop water requirement

CWT:

Continuous wavelet transform

Db:

Daubechies wavelet

DWT:

Discrete wavelet transform

ETc:

Crop evapotranspiration

FAO:

Food and Agriculture Organization

FCM:

Fuzzy C-means clustering method

FFNN:

Feedforward neural network

GA:

Genetic algorithm

GL:

Groundwater level

GW:

Groundwater withdrawal

IWR:

Irrigation water requirement

MCM:

Million cubic meter

ML:

Machine learning

MLE:

Maximum likelihood estimation

MRA:

Multiresolution analysis

PF:

Penalty function

PSO:

Particle swarm optimization

RIW:

Recommended volume of irrigation water

Sym:

Symlet wavelet

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Authors and Affiliations

  1. Currently at Research and Education Department (RED), RSS-Hydro Sarl-S, Innovation Hub Dudelange, 100, route de Volmerange, 3593, Dudelange, Luxembourg

    Mohammad Zare

  2. Institute of Civil and Environmental Engineering (INCEEN), Faculty of Science, Technology and Communication (FSTC), University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Mohammad Zare

  3. Department of Geohydraulics and Engineering Hydrology, University of Kassel, Kassel, Germany

    Mohammad Zare & Manfred Koch

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  1. Mohammad Zare
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Zare, M., Koch, M. Hybrid signal processing/machine learning and PSO optimization model for conjunctive management of surface–groundwater resources. Neural Comput & Applic 33, 8067–8088 (2021). https://doi.org/10.1007/s00521-020-05553-8

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