همکاری با انجمن علمی مدیریت و کنترل مناطق بیابانی ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مدیریت و کنترل بیابان، گروه احیاء مناطق خشک و کوهستانی، دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران

2 دانشیار، گروه احیاء مناطق خشک و کوهستانی، دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران

چکیده

مدل‌سازی و پیش‌بینی سطح ایستابی چاه‌ها یکی از کار‌های اساسی برایرسیدن به مدیریت بهینه منابع آب می‌باشد. یکی از راه‌های پیش‌بینی سطح آب زیرزمینی استفاده از تکنیک‌های هوش مصنوعی نظیر شبکه عصبی مصنوعی و برنامه‌ریزی بیان ژن می‌باشد. هدف از این پژوهش بررسی کارایی روش شبکه عصبی مصنوعی و برنامه‌ریزی بیان ژن در پیش‌بینی سطح ایستابی آب زیرزمینی آبخوان دشت جیرفت می‌باشد. به این منظور از داده‌های سطح ایستابی 65 چاه موجود در آبخوان دشت جیرفت برای یک دوره یازده ساله استفاده شد. سطح ایستابی چاه‌ها توسط هر یک از تکنیک‌های شبکه عصبی و برنامه‌ریزی بیان ژن به‌طور جداگانه شبیه‌سازی شد و در انتها از آماره‌های ریشه میانگین مربعات خطا، میانگین مطلق خطا، شاخص تطابق و R2 برای تعیین دقت پیش‌بینی هر کدام از روش‌ها استفاده شد. نتایج این پژوهش کارایی و دقت بالای هر دو تکنیک شبکه عصبی و برنامه‌ریزی بیان ژن را در پیش‌بینی سطح ایستابی چاه‌های منطقه نشان داد. ضریب همبستگی در روش شبکه عصبی مصنوعی برابر با 96/0 و در روش برنامه‌ریزی بیان ژن برابر با 72/0 شد که نشان دهنده این است روش شبکه عصبی مصنوعی در این تحقیق دقت بالاتری را در پراکنش داده‌های دشت جیرفت طی سال های 1381-1391 دارا می‌باشد.
 

کلیدواژه‌ها

عنوان مقاله [English]

Comparison of the performance of Artificial Neural Networks and Gene Expression to predict the groundwater level in arid and semi-arid areas (Case study: Jiroft plain)

نویسندگان [English]

  • Bahareh Jebalbarezi 1
  • Arash Malekian 2

1 Ph.D. Student in Desert Management, Department of Rehabilitation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran

2 Associate Professor, Department of Rehabilitation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran

چکیده [English]

    Modeling and prediction of groundwater level is one of the basic tasks to achieve optimal management of water resources. One way to predict the groundwater level is using artificial intelligence techniques such as neural networks and gene expression planning. The aim of this study was to evaluate the effectiveness of artificial neural network (ANN) and gene expression methods in predicting groundwater level of Jiroft plain aquifer. For this purpose, the data from 65 piezometric wells in the Jiroft plain aquifer was used for a period of eleven years. The level of piezometric wells by each of the techniques of gene expression and neural network were simulated separately and at the end, the root mean square, mean absolute error, and R2 were used to determine the accuracy of the predictions of each of the methods.The results of this study showed the higher efficiency and accuracy of both neural network techniques and gene expression in predicting the groundwater level region. The correlation coefficient in the artificial neural network method gene expression method was equal to 0.96 and 0.72, respectively, indicating the higher efficiency of artificial neural network in the simulation of Jiroft plain groundwater data over the period studied.

کلیدواژه‌ها [English]

  • Artificial neural networks
  • Groundwater table
  • Gene expression
  • Jiroft plain
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