نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه مهندسی عمران، دانشکده فنی مهندسی، دانشگاه رازی کرمانشاه، ایران
2 دانشیار گروه مهندسی آب، دانشکده کشاورزی، دانشگاه رازی کرمانشاه، ایران
3 گروه مهندسی عمران، دانشکده فنی مهندسی، دانشگاه رازی، کرمانشاه، ایران.
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Accurate and reliable runoff forecasting has an important role in water resources management, but the complex nature of this parameter can create major challenges for the development of appropriate forecasting models. Two hybrid models based on the combination of two simple linear and non-linear models have been proposed for runoff modeling at hydrological station 02PL005 in the St. Lawrence River basin in Canada. Seasonal autoregressive moving average (SARIMA) linear model is proposed to address the linear and seasonal characteristics of runoff. While the artificial neural network (ANN) and Outlier Robust Extreme Learning Machine (ORELM) models have been used to deal with the nonlinear characteristics of the data through machine learning and pattern recognition. In order to increase the accuracy of the modeling, in the first stage of modeling, the normality and stationary of the data was examined, and by performing appropriate pre-processing, the data were prepared for modeling in the linear part. Then by defining different sub-scenarios and performing modeling through linear model, the best linear model was selected through different mathematical statistics including MAE, RMSE, R and AIC. In the final stage, the residuals of the linear model were modeled by two non-linear models including ANN and ORELM models. Comparing the results of the proposed hybrid models showed that the SARIMA-ORELM hybrid model with AIC=249.29, R=0.71, MAE=11.2 and RMSE=14.33 performs better than the SARIMA-MLP model in all mathematical criteria. Also, the results of the hybrid models were compared with the common MLP, ORELM and SARIMA models.
کلیدواژه‌ها [English]
EXTENDED ABSTRACT
Runoff is an important factor in a hydrological system and is influenced by various factors such as geographic location, topography, and climate. Runoff forecasting plays an essential role in reducing the effects of floods and droughts, controlling erosion and sedimentation in the basin. Various hydrological models including empirical models, physical models and data-based models have been developed for runoff modeling. The data-driven methods due to the need for less knowledge of the physical behavior of the phenomenon have become more popular.
At first, the data were divided into two categories, training (70% of the total data measured) and testing (30% of the total data measured). The value of the Hurst coefficient for the data was 0.63, which indicates that the length of the time series is sufficient for modeling. The results of the normality and stationarity test showed that the data have a non-normal distribution and a non-stationary behavior. Therefore, by performing normalization through normalizing functions and removing definite terms from the time series by performing seasonal differentiation, the data were normalized and stationary. By defining two scenarios (without preprocessing and with preprocessing) and by performing different modeling, the best linear model was selected. By calculating the residual of the linear model and checking the independence of the residuals through the Ljung–Box test, nonlinear modeling was performed by outlier robust extreme learning machine (ORELM) and multilayer perceptron (MLP) models. Then, the output of the nonlinear model was summed with the linear model.
For linear modeling with SARIMA model, two scenarios were defined. The best linear model in the first scenario was obtained with MAE=13.28, RMSE=17.23, R=0.62 and AIC=267.54 using seasonal parameters and without preprocessing. In the second scenario, four sub-scenarios were implemented. Sub-scenario 4 using preprocessing through the standardization with MAE=12.76, RMSE=13.11, R=0.57 and AIC=264.41 shows better results than other sub-scenarios. The comparison of the results obtained from the implementation of different nonlinear models showed that model 6 with MAE=10.25, RMSE=13.48 and R=0.7 has the lowest error value and the highest correlation compared to other models. The comparison of the results obtained from the SARIMA-MLP models showed that model 4 with MAE=11.35, RMSE=14.67, AIC=254.41 and R=0.65 has the lowest error and the highest correlation as well as the least complexity compared to other combinations. Comparing the results obtained from the SARIMA-ORELM model showed that model 6 with AIC=249.29, R=0.71, MAE=11.2 and RMSE=14.33 has the best performance in terms of accuracy and complexity compared to other models. By comparing the statistical indicators, the best SARIMA-ORELM and SARIMA-MLP models were selected. The comparison of the results obtained from the implementation of different linear models through the two scenarios showed that preprocessing through standardization increases the accuracy of the model and reduces the complexity of the model.
A summary of the comparison of the results of the hybrid models with the results obtained from modeling through SARIMA and MLP models is given below:
The results of comparing the predictions of the models through statistical indicators show SARIMA-ORELM model performs better than SARIMA-MLP model in all mathematical criteria.
SARIMA-MLP and SARIMA-ORELM models reduced the complexity of the model by 4.9% and 6.8%, respectively, compared to the linear modeling mode without preprocessing.
Among the six different models selected for runoff modeling, the weakest performance in terms of error and complexity criteria is achieved by modeling through the SARIMA model without preprocessing.
F.N.D.: Writing – original draft, Formal analysis, Conceptualization, Data curation, Methodology, Validation, Writing – review & editing. A.A.: Writing – original draft, Formal analysis, Conceptualization, Data curation, Methodology, Validation, Writing – review & editing. A.A.A.: Conceptualization, Data curation, Writing – review & editing.
Data is available on reasonable request from the authors.
The authors would like to thank all participants of the present study.
The authors avoided data fabrication, falsification, plagiarism, and misconduct.
The authors declare no conflict of interest
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