پیش‌بینی سطح آبخوان با استفاده از شبکه عصبی تابع بنیادی رگرسیونی و ماشین بردار پشتیبان (دشت آمل_ بابل)

تعیین عمق سفره آب و توجه به تغییرات سطح ایستابی آبخوان در مناطق مختلف از جمله مناطق جلگه‌ای و دشتی جهت بهره‌برداری امری ضروری می‌باشد، شناخت رفتار سیستم آب زیرزمینی و پیش‌بینی سطح نوسانات آن یکی از مهمترین اقدامات به منظور دستیابی به مدیریت جامع و پایدار منابع آب زیرزمینی در مناطق مختلف جهان می‌باشد. بنابراین برای استفاده بهینه از منابع آب‌های زیرزمینی باید ابتدا به درستی مورد مطالعه قرار گیرد تا در مواقع بحرانی شرایط تعادل آب‌های زیر‌زمینی حفظ شود. در این تحقیق برای پیش‌بینی سطح آب زیرزمینی با استفاده از آمار سالانه 95 حلقه چاه پیزومتری در دشت آمل- بابل در استان مازندران با استفاده از شبکه عصبی تابع شعاعی احتمالی و ماشین بردار پشتیبان مورد استفاده قرار گرفت. در ادامه با روش آزمون و خطا از بین عوامل موثر در پیش‌بینی سطح آبخوان با استفاده از بارندگی، فاصله از منابع آب و قابلیت تشکیل آبخوان شبکه بهینه برای آزمون به‌دست آمد. نتایج حاصل از پژوهش نشان داد که هر دو مدل می‌توانند سطح آب زیرزمینی را با دقت نسبتاً بالایی پیش‌بینی کنند هرچند مدل تابع شعاعی احتمالی می‌تواند یک ابزار مفید برای شبیه‌سازی و پیش‌بینی آب‌های زیرزمینی باشد، زیرا ضریب همبستگی آن نسبتاً مناسب و برابر 0.82 و متوسط قدرمطلق خطای آن کوچک‌تر و برابر با 1.94 می‌باشد.

simulation aquifer level amol- babol plain using to by radial basis function and support vector machine

introduction:groundwater forms part of the water cycle and is a reliable source of water for human consumption, as well as in iran, most of the water used in the drinking, agricultural and industrial sectors is supplied from groundwater. due to the condition of iran, due to the deficit surface water resources, the use of groundwater resources for water supply has been considered. materials and methods:1) use of the radial basis function of the neural networkif a generalized regression function of the neural network, pnn / grnn, is selected, all of the network weights can be calculated as probable. in rbf, a gaussian transmission function is used which is similar to a ring (grnn) one of the benefits of these networks is its rapid learning of other networks, including the multi-layered perceptron network of mlps. the gaussian networks of the transfer function network are of an unidentified learning type, but the output is a controlled learning type. the network is very practical in simulating hydrological and hydrological issues, due to its rapid training, generalizability and ease of use. 2) use a support vector machine. a support vector machine is proposed based on the principle of minimizing structural error. a support vector machine can be used both for categorization issues and for the estimation of functions. used a new error function called ε-insensitive for machine application in regression problems, so that this function ignores errors that are at a given distance from actual values. this function is defined as how to design a network. in this study, the data used were 95 piezo metric wells in the amol-babol plain. data were used with a mean average of 30 years. in order to simulate the depth of the groundwater table, effective factors such transmissivity of aquifer formations, precipitation values and distance from water resources. for the design of the network, for both models, there are two classes of training and testing data. one important criterion for training a network is the number of repetitions or epoch during training. the higher the number of replays, the error decreases so that training data can be converted, which will increase the number of unsuccessful repetitions at that time. for network optimization purposes, the goal of network training is to reduce network error, which can improve the relationship between the input and output of the model. due to the lack of specific rules for designing artificial neural networks (anns), various structures have been investigated to optimize the design. select the number and type of input parameters for the model is important. for this reason, seven design input patterns are given. which was carried out in the software of the neurosolutions.discussion of results: for the optimal simulation model based on all parameters and the provision of all its input data will require a great deal of time and cost, a method based on the main parameters of input (optimal inputs) is modeled and validated. it was observed that the predicted aquifer level for both models is about to its actual value.conclusions: the results obtained with the radial basis function and the support vector machine represent this point where the support vector machine and the radial function have the ability to have approximately the same ability to predict and modeling, although generally the results of the radial basis function are more acceptable. the results of the model test are shown. as the results of the survey are presented, among the methods implemented in the model, using effective factors such as transmissivity of aquifer formations, precipitation values and distance from water resources to predict level of level the aquifer was used. the results of the test showed that the radial basis function of the support vector machine with a correlation coefficient of 0.82 and a mean absolute error of 1.94 is an appropriate tool for prediction of water resource management.