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تخمین تغییرات سطح آب زیرزمینی با استفاده از چهار تکنیک متفاوت شبکه عصبی تکاملی (مطالعه موردی دشت عباس، استان ایلام)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد دانشگاه ازاد کرمانشاه

2 دانشجوی کارشناسی ارشد دانشگاه اراک

3 مرکز تحقیقات کشاورزی و منابع طبیعی ایلام

چکیده

در نواحی خشک و نیمه خشک، آب عمده‌ترین عامل محدودیت کشاورزی است. در این مناطق به دلیل کمبود جریان‌های سطحی، فشار عمده بر آب‌های زیرزمینی وارد می‌شود. منابع آب زیرزمینی محدوده مورد مطالعه (دشت عباس) نیز به دلیل استفاده بی‌رویه دچار افت شدید گردیده است. در این تحقیق، ما از چهار مدل متفاوت شبکه عصبی تکاملی شامل، شبکه عصبی پرسپترون چندلایه با الگوریتم ژنتیک (ANN-GA)، شبکه عصبی پرسپترون چندلایه با بهینه‌سازی ازدحام ذرات (ANN-PSO)، شبکه عصبی پرسپترون چندلایه با الگوریتم رقابت استعماری (ANN-ICA) و شبکه عصبی پرسپترون چندلایه با بهینه سازی کلونی مورچگان (ANN-ACOR) برای تخمین سطح آب زیرزمینی بر طبق جریان ورودی زیرزمینی، نفوذ موثر از بارندگی، نفوذ موثر از جریان سطحی و سیلاب، نفوذ موثر از آب برگشتی کشاورزی، جریان خروجی زیرزمینی، برداشت از آبخوان جهت کشاورزی، تبخیر از سطح آب زیرزمینی و داده‌های گذشته سطح آب زیرزمینی استفاده کرده‌ایم. ترکیب ورودی‌ها با استفاده از تجزیه و تحلیل خود همبستگی، خود همبستگی جزئی و همبستگی متقابل برای هر مدل آماده شده ‌است. مدل‌های بهینه با تغییر پارامترهای کنترلی به دست آمده اند. بهترین دقت از بین مدل‌های ارائه شده برای ورودی (GWLt-1 ، GWLt-2، Qint، Qpt-1، Qrt-1، Qit-1، Qoutt-1، Qwt-1 و Qet-1) به دست آمده است. دقت میانگین مربعات خطا در فاز آزمایش برای مدل‌های ANN-PSO، ANN-ICA، ANN-ACOR به ترتیب برابر 1.2208، 0.9456و 1.7720 و برای مدل ANN-GA برابر 0.8739 به دست آمده است. میانگین خطای نسبی مدل ANN-GA برابر 3.6% و ضریب اطمینان آن 0.9388 است. با توجه به نتایج به دست آمده مدل ANN-GA عملکرد بهتری نسبت به سه مدل دیگر برای تخمین سطح آب زیرزمینی از خود نشان داده است.

کلیدواژه‌ها

عنوان مقاله [English]

The Estimation of groundwater level changes using four different techniques of evolutionary neural network, case study of Dasht-e-Abbas plain, Ilam province

نویسندگان [English]

  • mohammmad javad rezaei 1
  • mohammad reza rezaei 2

1 . Student of Computer Science, Department of Computer, , Islamic Azad University Kermanshah,

2 Student of Irrigation and Drainage Engineering, Department of Irrigation and Drainage, Arak University

چکیده [English]

Abstract
In dry and semi-arid areas, water is the most factor of limiter in agriculture. In these areas, due to the lack of surface flows, major pressures enter on groundwater. Groundwater resources in the studied area (Dashte-Abbas plain) also suffered a severe drop in surface water due to unplanned use. In this study, we compared four different models of evolutionary neural network, a multi-layered perceptron neural network with Genetic Algorithm (ANN-GA), a multilayered perceptron neural network with particle swarm optimization (ANN-PSO), a multilevel perceptron neural network with Imperialism competitive algorithm (ANN-ICA) and multi-layered perceptron neural network with ant colony optimization (ANN-ACOR) for estimating groundwater level according to groundwater inflow, effective penetration of rainfall, effective penetration of surface flow and flood, effective penetration of return water Agriculture, underground outflow, withdrawal from aquifer for agriculture, evaporation from groundwater level and past groundwater data Were used. groundwater level comparisons are the combination of inputs has been prepared using Auto-correlation analysis, partial Auto-correlation and cross-correlation for each model. Optimal models are obtained by changing the control parameters. The best results are obtained from the input models (GWLt-1, GWLt-2, Qint, Qpt-1, Qrt-1, Qit-1, Qoutt-1, Qwt-1, and Qet-1). The accuracy of the mean squared error in the test phase for ANN-PSO, ANN-ICA, ANN-ACOR models was 1.2208, 0.9456 and 1.7720, respectively, and for the ANN-GA model, it was 0.8739. The mean relative error of ANN-GA model is 3.6% and its determined coefficient is 0.9388. According to the results, the ANN-GA model showed better performance than the other three models for estimating groundwater level.

کلیدواژه‌ها [English]

  • Groundwater changes
  • Evolutionary neural networks
  • Genetic Algorithm
  • Particle Swarm Optimization
  • Imperialism Competition Algorithm
  • Ant Colony Optimization
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مهندسی و مدیریت آبخیز
دوره 12، شماره 3
مهر 1399
صفحه 737-755
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