بررسی کارایی مدل هیبریدی هالت-وینترز موجکی (WHW)در شبیه¬سازی تراز سطح ایستابی آبخوان ساحلی ارومیه
محورهای موضوعی :
کلید واژه: هالت وینترز تبدیل موجک شبکه عصبی رگرسیون بردار پشتیبان آب زیرزمینی,
چکیده مقاله :
پیش بینی تراز سطح آب زیرزمینی به‌منظور درک صحیح در مورد روند تغییرات آبخوان و مدیریت و برنامه ریزي این منابع آبی ارزشمند، بسیار مهم است. این مقاله از مدل هیبریدی هالت وینترز موجکی(WHW) برای اولین بار در شبیه‌سازی تراز سطح ایستابی بهره گرفته است. بدین منظور از سری زمانی 16 ساله نوسانات ماهانه تراز سطح آب زیرزمینی در دو چاه مشاهده-ای آبخوان ساحلی ارومیه استفاده شد. در WHW سری زمانی مادر به چندین زیر سری با مقیاس های زمانی مختلف تبدیل شد، سپس زیر سری های زمانی به‌صورت تک‌تک به‌عنوان ورودی مدل HW قرار گرفته و با تجمیع خروجی ها تراز محاسباتی سطح ایستابی به دست آمد. پس از آن عملکرد مدل WHW با مدل های خطی ARIMA، HW وSARIMA و نیز مدل های هوشمند غیرخطی شبکه عصبی(ANN) و رگرسیون بردار پشتیبان(SVR) مورد ارزیابی قرار گرفت. نتایج نشان داد که در مدل WHW در مقایسه با مدل های خطی، معیار خطای NSE و RMSE در مرحله آزمون به ترتیب تا 30 و 60 درصد ارتقا یافته و در مقایسه با مدل های غیرخطی ANN و SVR عملکرد برابر و مشابه داشته است. همچنین نتایج نشان داد که هرچه تراز سطح آب زیرزمینی از تناوب های چندگانه و غیرفصلی بیشتری برخوردار باشد، دقت مدل WHWدر قیاس با مدل های خطی بیشتر می شود.
For management and planning valuable groundwater resources, it is very important to predict groundwater level and have a correct understanding about aquifer changes. In this paper for the first time, the wavelet Halt-Winters hybrid models (WHW) were used and tested for groundwater forecasting. A monthly data set of 16 years consisting of groundwater level fluctuations was used in two observation wells of Urmieh coastal aquifer. In the WHW, the dataset was converted into several sub-dataset with different time scales. Then, the sub-series were used in the HW model as inputs. Subsequently, the performance of the WHW model was compared with ARIMA, HW, and SARIMA as linear models and neural network models (ANN) and Support Vector Regression (SVR) as nonlinear models. The results showed that the NSE and RMSE values of the WHW model were upgraded up to 30% and 60% respectively, in comparison with linear models. The WHW hybrid model also has the same performance compared to nonlinear models. This research reflects that if there are multiple seasonal fluctuations in the groundwater time series, the performance of the WHW model compared with linear models will be more accurate.
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