پيش بيني پارامترهاي كيفي (NO۳ ,CL) رودخانه كرج با استفاده از مدل هاي تركيبي شبكه عصبي-موجكي، ANN وMLR

آب و خاک  

دوره 30 - شماره 1

نوع مقاله: Original Article
چكيده: كنترل كيفيت آب رودخانه كرج، به عنوان يكي از مهم ترين منابع تأمين كننده آب از اهميت بسزايي برخوردار است. پيش بيني پارامترهاي كيفي آب، ابزاري سودمند در جهت مديريت منابع آب مي باشد. در اين تحقيق، عملكرد مدل‌هاي شبكه عصبي مصنوعي (ANN)، مدل تركيبي شبكه عصبي–موجك (WANN) ورگرسيون خطي چند متغيره (MLR)، در پيش بيني يك ماه آينده يون نيترات و كلرايد ايستگاه ورودي آبگير بيلقان واقع در رودخانه كرج، مورد ارزيابي قرار گرفت. براي اين منظور از يك دوره آماري جهت ورودي مدل ها استفاده شد. در مدل تركيبي WANN سري هاي زماني واقعي دبي و پارامتر كيفي مورد نظر (نيترات و كلرايد) توسط آناليز موجك در سطوح مختلف تجزيه شده و به عنوان ورودي ANN به كار گرفته شد. كارايي مدل ها با ضريب تببين (E) و ريشه ميانگين مربعات خطا (RMSE) ارزيابي شدند. نتايج حاكي از دقت بالاي مدل تركيبي شبكه عصبي- موجكي نسبت به دو مدل ديگر بوده است؛ به‌طوريكه مدل تركيبي شبكه عصبي –موجكي قادر بود ميزان RMSE را براي يون نيترات در مقايسه با مدل شبكه عصبي و رگرسيون خطي چند متغيره به ترتيب به مقدار 30.13 درصد و 71.89 درصد و براي يون كلرايد، به اندازه 31.3 درصد و57.1 درصد بهبود بخشد. در ادامه، توانايي هر سه مدل، براي پيش بيني نقاط پيك سري زماني بررسي شد كه مدل تركيبي WANNپيش بيني بهتري را در مقايسه با دو مدل ديگر در برداشت.
Prediction ofWater Quality Parameters (NO3, CL) in Karaj Riverby Usinga Combinationof Wavelet Neural Network, ANN and MLRModels
Article Type: Original Article
Abstract: IntroductionThe water quality is an issue of ongoing concern. Evaluation of the quantity and quality of running waters is considerable in hydro-environmental management.The prediction and control of the quality of Karaj river water, as one of the important needed water supply sources of Tehran, possesses great importance. In this study, Performance of Artificial Neural Network (ANN), Wavelet Neural Network combination (WANN) and multi linear regression (MLR) models, to predict next month the Nitrate (NO3) and Chloride (CL) ions of "gate ofBylaqan sluice" station located in Karaj River has been evaluated.
Materials and MethodsIn this research two separate ANN models for prediction of NO3 and CL has been expanded. Each one of the parameters for prediction (NO3 / CL) has been put related to the past amounts of the same time series (NO3 / CL) and its amounts of Q in past months.From astatisticalperiod of10yearswas usedforthe input of the models. Hence 80% of entire data from (96 initial months of data) as training set, next 10% of data (12 months) and 10% of the end of time series (terminal 12 months) were considered as for validation and test of the models, respectively. In WANNcombination model, the real monthly observed time series of river discharge (Q) and mentioned qualityparameters(NO3 / CL) were decomposed to some sub-time series at different levels by wavelet analysis.Then the decomposed quality parameters to predict and Q time series were used at different levels as inputs to the ANN technique for predicting one-step-ahead Nitrate and Chloride. These time series play various roles in the original time series and the behavior of each is distinct, so the contribution to the original time series varies from each other. In addition, prediction of high NO3 and CL values greater than mean of data that have great importancewere investigated by the models. The capability of the models was evaluated by Coefficient of Efficiency (E) and the Root Mean Square Error (RMSE).An efficiency of one corresponds to an accurate match of forecasted data to the observed data. RMSE indicates the discrepancy between the observed and predicted values.
Results Discussion: The results indicates that the accuracy and the ability of hybrid model of wavelet neural network had been better than the other two modes; so that hybrid model of Wavelet artificial neural network was able the improve the rate of RMSE for Nitrate ions in comparison with ANN and MLR models respectively, amounting to 30.13% and 71.89%, for chloride ion as much as 31.3% and 57.1%. In the WANN model increasing the decomposition level, in level 1 to Level 3, increases the model’s performance, but increasing the decomposition level, in levels over Level 3, decreases the model’s efficiency, because high decomposition levels lead to a large number of parameters with complex nonlinear relationships in the ANN technique.The WANN model needed 1 to 7 neurons in the hidden layer for the best performance result. In prediction of high NO3 values the amount RMSE for ANN, MLR and WANN models are 1.487, 2.645 and 0.834 ppm, respectively. Also, for CL values the mentioned statistical parameter is 0.990, 3.003 and 0.188 ppm, respectively for models.The results exhibits that the combined model of WANN the forecast was better than the other two models.
Conclusion: Wavelet transforms provide useful decompositions of original time series, so that wavelettransformed data improve the ability of a predicting model by capturing useful information on various resolution levels. The main advantage of this study is that only from the Q and slightly quality of parameter time series are used until the same quality of parameter in one month ahead is predicted. The purpose of entering Q time series with quality of parameter as inputs of models is analysis the efficacy of Q in the accuracy of prediction. owing of the high capability wavelet neural network in the prediction of quality parameters of river's water, this model can be convenient and fast way to be proposed for management of water quality resources and assurance from water quality monitoring results and reduction its costs
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