Amirabadizadeh, Mehdi, Ramezani, Yousef, nazeri tahroudi, mohammad, Zeynali, Mohammad Javad. (1398). Assessment of Data-driven Models in Downscaling of the Daily Temperature in Birjand Synoptic Station. نشریه مهندسی عمران امیرکبیر, 3(2), 193-200. doi: 10.22060/ajce.2018.15115.5522
Mehdi Amirabadizadeh; Yousef Ramezani; mohammad nazeri tahroudi; Mohammad Javad Zeynali. "Assessment of Data-driven Models in Downscaling of the Daily Temperature in Birjand Synoptic Station". نشریه مهندسی عمران امیرکبیر, 3, 2, 1398, 193-200. doi: 10.22060/ajce.2018.15115.5522
Amirabadizadeh, Mehdi, Ramezani, Yousef, nazeri tahroudi, mohammad, Zeynali, Mohammad Javad. (1398). 'Assessment of Data-driven Models in Downscaling of the Daily Temperature in Birjand Synoptic Station', نشریه مهندسی عمران امیرکبیر, 3(2), pp. 193-200. doi: 10.22060/ajce.2018.15115.5522
Amirabadizadeh, Mehdi, Ramezani, Yousef, nazeri tahroudi, mohammad, Zeynali, Mohammad Javad. Assessment of Data-driven Models in Downscaling of the Daily Temperature in Birjand Synoptic Station. نشریه مهندسی عمران امیرکبیر, 1398; 3(2): 193-200. doi: 10.22060/ajce.2018.15115.5522

Assessment of Data-driven Models in Downscaling of the Daily Temperature in Birjand Synoptic Station

AUT Journal of Civil Engineering
مقاله 7، دوره 3، شماره 2، اسفند 2019، صفحه 193-200    اصل مقاله (765.44 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22060/ajce.2018.15115.5522
نویسندگان
Mehdi Amirabadizadeh1؛ Yousef Ramezani1؛ mohammad nazeri tahroudi* 2؛ Mohammad Javad Zeynali1
1Water Engineering Department, Faculty of Agricultural, Birjand University. Iran
2Ph.D Student, Department sciences and water engineering, University of Birjand, Birjand, Iran
چکیده
 In this study, seven models such as multivariate regression, Contemporaneous Autoregressive-Moving Average (CARMA), CARMA-ARCH (Autoregressive Conditional Heteroskedasticity), Support Vector Regression (SVR), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) and Genetic Programming (GP) were investigated to downscaling the maximum daily temperature of Birjand synoptic station using 26 predictor’s parameters that resulting from the fifth Intergovernmental Panel on Climate Change (IPCC) report and compared. The max daily temperature values measured from 12/03/1961 until 20/12/2005. In all mentioned methods from 26 predictive parameters using the Pearson correlation test, 15 parameters were selected that have a high correlation with the max daily temperature values. The results of evaluating the accuracy and model indicated that from the same models such as GP, ANFIS and SVM, the GP model has the least amount of errors (about 4 °C) and in the regression models (multivariate regression and SVR), SVR have been lowest error rate (about 1 °C) and the highest accuracy in simulated max daily temperature values. The results of the investigation the error rate of the mentioned models indicated that after the SVR model, two CARMA and CARMA-ARCH stochastic models have high and acceptable accuracy about 97 percentages. In general, the results of the simulation the max daily temperature indicates the best accuracy of regression toward another methods. One of the reasons for the results of the SVR model is to optimize the parameters of the model using the ant colony algorithm for estimating the maximum temperature values of the Birjand synoptic station.
کلیدواژه‌ها
Down Scaling؛ Genetic Algorithm؛ Neural Network؛ Support Vector Regression؛ Temperature
مراجع
آمار
تعداد مشاهده مقاله: 966
تعداد دریافت فایل اصل مقاله: 559
پیوندهای مفید
آمار
تعداد نشریات9
تعداد شماره‌ها455
تعداد مقالات5,771
تعداد مشاهده مقاله8,375,616
تعداد دریافت فایل اصل مقاله6,934,534