پیش‌بینی تراز آب زیرزمینی در آبخوان گلپایگان با استفاده از ترکیب ANFIS و PSO

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه علوم و مهندسی آب، دانشکده کشاورزی و محیط زیست، دانشگاه اراک، اراک، ایران

چکیده

پیش‌بینی تراز آب زیرزمینی اولویتی ضروری برای برنامه‌ریزی و مدیریت منابع آب زیرزمینی می‌باشد. هدف از تحقیق حاضر مقایسه دقت سیستم استنتاج تطبیقی عصبی- فازی[1] (ANFIS) با مدل ترکیبی ANFIS آموزش دیده توسط الگوریتم بهینه‌سازی ازدحام ذرات[2] (ANFIS+PSO) در پیش‌بینی ماهانه تراز آب زیرزمینی آبخوان گلپایگان طی سال‌های 97-1381 می‌باشد. بدین منظور از داده‌های ماهانه بارندگی، دما، تبخیر از تشت در ایستگاه­های هواشناسی منتخب، حجم تخلیه از چاه­های بهره‌برداری و تراز آب زیرزمینی چاه‌های مشاهده‌ای استفاده شده است. پس از انجام تحلیل مکانی و زمانی، چهار چاه مشاهده‌ای با دو ساختار داده ورودی (S1 و S2) برای پیش‌بینی تراز آب زیرزمینی انتخاب گردید. نتایج آزمون‌های روند و همگنی حاکی از معنی‌داری 99 درصدی تغییرات تراز آب زیرزمینی در چاه‌های مشاهده‌ای منتخب 4، 8، 19 و 20 با افت ناگهانی 22، 17، 27 و 2 متر به ترتیب در قبل و بعد از ماه‌های خرداد، شهریور، تیر و مرداد 1389 می‌باشد. بیشترین و کمترین دقت پیش‌بینی تراز آب زیرزمینی مربوط به چاه‌های مشاهده‌ای 20 و 4 با مقادیر ریشه میانگین مربعات خطا[3] (RMSE) برابر 37/2 و 21/0 متر به ترتیب مربوط به مدل‌های ANFIS_S1 و ANFIS+PSO_S2 می‌باشد. نتایج کلی تحقیق حاکی از تأثیر بیشتر انتخاب تأخیرهای مناسب داده‌های ورودی (ساختار مدل) نسبت به ترکیب دو مدل (ANFIS و PSO) در افزایش دقت پیش‌بینی تراز آب زیرزمینی دارد، به طوری‌که ساختار مطلوب داده‌های ورودی و ترکیب الگوریتم بهینه‌ساز با مدل شبیه‌ساز به ترتیب  44 و 25 درصد دقت پیش‌بینی تراز آب زیرزمینی را افزایش داده‌اند.



[1] Adaptive Neuro-Fuzzy Inference System (ANFIS)


[2] Particle Swarm Optimization (PSO)


[3] Root Mean Squared Error (RMSE)

کلیدواژه‌ها


عنوان مقاله [English]

Groundwater Level Prediction in Golpayegan Aquifer Using ANFIS and PSO Combination

نویسندگان [English]

  • Sedigheh Salari
  • Mahnoosh Moghaddasi
  • Mehdi Mohammadi Ghaleni
  • Mahmood Akbari
Department of Water Science and Engineering, Faculty of Agriculture and Environment, Arak University, Arak, Iran.
چکیده [English]

Groundwater level prediction is an essential priority for planning and managing groundwater resources. This study aimed to compare the accuracy of the Neuro-Fuzzy Adaptive Inference System (ANFIS) model with the ANFIS model combined with particle swarm optimization algorithm (ANFIS+PSO) in predicting the monthly groundwater level of Golpayegan aquifer during 2002-2019. For this purpose, monthly data on rainfall, temperature, pan evaporation in the selected meteorological stations, discharge volume of exploitation wells and groundwater level of observation wells have been used. After spatial and temporal analysis, four observation wells with two input data structures (S1 and S2) were selected to predict the groundwater level. The results of trend and homogeneity tests show a 99% significance of groundwater level changes in the selected observation wells 4, 8, 19 and 20 with a sudden drop of 22, 17, 27 and 2 meters before and after June, September, July and August 2010, respectively. The highest and the lowest accuracy of groundwater level prediction is related to observation wells 20 and 4 with root mean square error values (RMSE) of 2.37 and 0.21 m, respectively, related to ANFIS_S1 and ANFIS + PSO_S2 models. Generally, the results of this study indicate that the selection of appropriate structure of input data is more effective than the combination of two models (ANFIS and PSO) in increasing the accuracy of groundwater level prediction. So, that the optimal structure of input data and the combination of optimized algorithm model have increased the accuracy of groundwater level prediction, 44% and 25%,  respectively.

کلیدواژه‌ها [English]

  • Agglomerative Hierarchical Clustering
  • Groundwater level prediction
  • Neuro-Fuzzy Adaptive Inference System
  • Particle Swarm Optimization Algorithm
  • Spatial and Temporal Analysis
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