جستجوی مقالات مرتبط با کلیدواژه « بارش ماهانه » در نشریات گروه « آب و خاک »

Precipitation is one of the most important hydrological events and its prediction can be used as a practical tool for optimum utilization and management of water resources. In the present study, artificial neural network (ANN) and wavelet-artificial neural network (WANN) were used for monthly precipitation perdiction at selected synoptic stations in Ardabil province, including Ardabil, Khalkhal, Meshginshahr and Parsabad during the 225 months for the years 1996-2016. For the short-term forecast of monthly precipitation (one month later), different scenarios were defined based on precipitation delays. Results indicated that the WANN model with the highest determination coefficient (R2) and minimum root mean square error (RMSE) was acceptable for all stations. The values of R2 and RMSE for Ardabil station were equal to 0.88 and 7.13 mm, for Khalkhal station were equal to 0.91 and 6.36 mm, for Meshginshahr station were equal to 0.92 and 6.97 mm and for Parsabad station were equal to 0.86 and 8.51 mm, respectively. In all stations, utilization of  the superior model (WANN model) with the combination scenarios i.e. rainfall delays, the minimum and maximum temperature improved the results of the model, but on the other hand, increased the computational cost of the model. Also, in all stations, the addition of relative humidity and wind speed as input variables somewhat reduced the performance of the model. The general results of this study showed that the WANN model with appropriate rainfall delays on a monthly scale can be utilized to predict monthly precipitation of selected stations in Ardabil province, including Ardabil, Khalkhal, Meshginshahr and Parsabad with acceptable accuracy.

In the present study, we used 27 precipitation average monthly data from synoptic, climatologic, rain-guage and evaporative stations located in Zayandeh-Rud river basin for the period of 1970-2014. Before interpolating, the missing data in the time series of each station was reconstructed by the normal ratio method. Also, for the data quality control, the Dickey-Fuller and Shapiro-Wilk tests were used to check the data stationarity and normality. Then, these data were interpolated by six interpolation methods including   Inverse Distance Weighting, Natural Neighbor, Tension Spline, Regularized Spline, Ordinary Kriging and Universal Kriging; then each method was evaluated using the cross-validation technique with MAE, MBE and RMSE indices. The results showed that among the spatial interpolation methods, Natural Neighbor method with MAE of 0.24 had the best performance for interpolating precipitation among all of the methods. Also, among Ordinary Kriging, Universal Kriging, Spline and Inverse Distance Weighting methods, respectively, Exponential Kriging with MAE 0.54, Quadratic Drift Kriging with MAE of 0.5, Tension Spline with the MAE of 0.54 and Inverse Distance Weighting with the power of 4 with MAE of 0.57 had the least error compared to other IDW methods.

Precipitation is one of the important climatic parameters in hydrological models. Therefore, the accurate estimation of its amount and spatial distribution in a watershed is of great importance. In recent years, due to the limited number of raingage stations especially in mountainous areas, the use of satellite precipitation data as an effective tool for predicting regional distribution of rainfall has gained lots of attention by the researchers. In the present study, the accuracy of TRMM 3B43 data, which is one of the TRMM products, was evaluated in 40 raingage stations and 9 synoptic stations in Hormozgan province in a monthly scale. Comparison of satellite data with the observation data was performed for the time period 1998-2012. To assess the agreement between TRMM rainfall data with observation data, the statistical criteria including Spearman correlation coefficients (Rs), Pearson correlation coefficient (Rp), mean absolute error (MAE), root mean square error (RMSE) and mean square error (MSE), as well as probability of detection (POD), false alarm ratio (FAR), true skill statistics (TSS) and critical success index (CSI) were used. Based on the results, the highest value of POD (1) was observed in August and the lowest POD (0.92) was obtained in May. Moreover, the highest FAR (0.91) and the lowest CSI (0.08) were observed in May, and the lowest FAR (0.16) and the highest CSI (0.83) was obtained in January. In addition, the highest Rp (0.64) and Rs (0.76) were seen in December while the lowest Rp and Rs were occurred in April, May and July. These results indicate that the TRMM satellite has the highest accuracy of predicting rainfall in winter and spring while it has the lowest performance in summer. In other words, the TRMM satellite is able to predict rainfall in cold months better than in warm months of the year. The results also showed that TRMM overestimates rainfall in most months of the year, however the results were significantly improved after calibration especially in August and December as seen in spatial distribution maps.

Incomplete rainfall datasets with missing gaps is a major challenge in climatology and water resource studies. In the present study, two intelligent models, namely Genetic Programing (GP) and Support Vector Machines (SVM) were used to reconstruct the monthly rainfall data of four rain-gauges located in Hamedan province, Iran during the period of 1992 to 2011. The incomplete rainfall data was reconstructed first by using the data of one, two and three stations respectively. The results showed that increasing the memory and the number of stations involved in the training phase, will improve the performance of the models. In reconstruction of monthly precipitation data of Sarabi and Maryanj stations, the Support Vector Machine method showed better performance with RMSE of 12.9 mm and 11.4 mm, and correlation coefficients (r) of 0.93 and 0.95, respectively. The corresponding values of RMSE for GP approach were 13 mm and 12.21 mm, which indicated the superior performance of SVM.

The complete time series of meteorological data with enough length is one of the most fundamental issues in environmental and hydrological studies. However, weather stations often have statistical gaps due to lack of measured data or technical problems (Tardio and Bertie, 2012). Precipitation data are so important for climate studies and frequency analysis of hydrologic events such as floods, droughts and also water resources planning and management. Estimation of missing values has been a subject of interest to meteorologists, hydrologists and environmental experts. Recently, artificial intelligence models support vector machine (SVM) and Artificial Neural Network (ANN) has been used to reconstruct the rainfall data. SVM is a supervised learning method that can be used for classification and regression. This method, developed by Vapnik (1998), is based on the theory of statistical learning. The other method which is widely used to predict the characteristics of non-linear and complex phenomena is artificial neural network. Artificial intelligence and neural network models were proposed by McCulloch and Pitts (1943), for the first time, to predict precipitation. Golabi et al. (2013) compared the performance of different algorithms of artificial neural networks for modeling seasonal rainfall in Khuzestan province. In this study, MLP and RBF networks with applying some changes in the middle layers, neurons, and training algorithms MOM, LM and CG were used to predict seasonal rainfall. The main purpose of this study was to compare the performance of artificial neural network (ANN) and Support Vector Machine (SVM) in reconstructing of precipitation in rain gauge stations of Hamedan Province. For this purpose, the monthly precipitation data of four rain gauge stations, including Aghajanbolaghi, Sarabi, Aghkahriz and Maryanej, were used during the period of 1991 to 2010.
SVM regression model is depended on a functional dependence y to a set of independent variables x estimated. It is assumed that other issues such as regression, the relationship between the dependent and independent variables by a certain function f noise, plus an additional amount are determined. In this study, SVM model was used to reconstruct the missing data by using non-linear kernel function of RBF Statistica10 and with more than 100 replicates. The Kernel function were used because higher accuracy in the reconstruction of the monthly precipitation data. Optimal characteristics of SVM model include Ɛ, C and ɣ should be determined. For this purpose, characteristics C and Ɛ by was calculated by the network search optimization algorithm and variable ɣ by trial and error. An artificial neural network is composed of neurons. The smallest units of information-processing are neurons or nodes which form the basis of the performance of neural networks (Minhaj, 2005). Each neuron receives input and then processing them, generate an output signal. Thus, the neurons in the network act as a center for processing and distribution of information, its own input and output (Sadorsky, 2006). The study of artificial neural networks Multilayer Perceptron (MLP) is used by the propagation algorithm. The precipitation data of four stations including Aghajanbolaghi, Sarabi, Aghkahriz and Maryanej, were used in this study. These stations were chosen based on their close distance and high correlation in monthly precipitation data. To determine the best input pattern for the network, the various factors that may affect the phenomenon should be considered. To reconstruct rainfall data in neural network, programming in Matlab software is used. In order to evaluate the performance of Support Vector Machine (SVM) and artificial neural networks (ANN) in reconstructing the missing precipitation values, the Coefficient Determination (R2) and root mean square error (RMSE) were calculated for observed and estimated precipitation values.
In this study, the MLP artificial neural networks and RBF Support Vector Machine models were used for reconstruction of monthly precipitation data of rain gauge station of Sarabi in Hamedan province, during the years 1991 to 2010. Three modes were intended as training models. Models for training in the first case used only one station data (Aghajanblaghy), the second used two stations data (Aghajanblaghy and AqhKahriz) and for the third training data from three stations were used (Aghajanblaghy, Aqhkahriz and Maryanaj). Also, Sarabi was considered as reconstruction station. The results of this study indicated that when the data for one and two stations were used for training in modeling, the Support Vector Machine model showed better performance than SVM. While, using the data from three stations in the models, the artificial neural network model was better than the Support Vector machine with a little difference. Reconstructing of monthly precipitation, the best performance was obtained by using data from three stations for training ANN and SVM. Coefficient Determination and RMSE for the models were 0.88 and 12.33 mm for ANN and 0.87 and 12.89 mm for SVM. Reconstructing of seasonal precipitation, the best results was gained by using data from three stations for training ANN and SVM models. Coefficient of determination and mean squares for these models were 0.95 and 26.62 mm for ANN and 0.94 and 18.82 mm for SVM. Although both models in three different educational models performed almost the same in the reconstruction of monthly data (Sarabi), the results showed that when the number of stations in learning more models increased, the models performance improved.

Precipitation with multiple time scales is considered as one of the most important parameters in water resources studies. Precipitation with different time scales can be expressed with fractal nature. One of the standard tools in the fractal investigation of hydrological processes is the power spectrum method. In this method, the power spectrum is calculated with transmitted observation from time space to frequency space. When all or a part of the spectral follow the power functions, the data in this power range will have fractal properties. In this study, the power spectrum of monthly precipitation of the 33 rain gauge stations in Iran was analyzed and scaling regimes with spectral exponent value are shown for each station. The results indicate that 81 percent of the stations have fractal nature and scaling properties in a period less than one year. In addition to the first scaling regime, 17 stations have second scaling regime and 3 stations have the third scaling regime. None of the stations in its second scaling regime have fractal nature and only one station in the third scaling regime has fractal nature.Precipitation with multiple time scales is considered as one of the most important parameters in water resources studies. Precipitation with different time scales can be expressed with fractal nature. One of the standard tools in the fractal investigation of hydrological processes is the power spectrum method. In this method, the power spectrum is calculated with transmitted observation from time space to frequency space. When all or a part of the spectral follow the power functions, the data in this power range will have fractal properties. In this study, the power spectrum of monthly precipitation of the 33 rain gauge stations in Iran was analyzed and scaling regimes with spectral exponent value are shown for each station. The results indicate that 81 percent of the stations have fractal nature and scaling properties in a period less than one year. In addition to the first scaling regime, 17 stations have second scaling regime and 3 stations have the third scaling regime. None of the stations in its second scaling regime have fractal nature and only one station in the third scaling regime has fractal nature.Precipitation with multiple time scales is considered as one of the most important parameters in water resources studies. Precipitation with different time scales can be expressed with fractal nature. One of the standard tools in the fractal investigation of hydrological processes is the power spectrum method. In this method, the power spectrum is calculated with transmitted observation from time space to frequency space. When all or a part of the spectral follow the power functions, the data in this power range will have fractal properties. In this study, the power spectrum of monthly precipitation of the 33 rain gauge stations in Iran was analyzed and scaling regimes with spectral exponent value are shown for each station. The results indicate that 81 percent of the stations have fractal nature and scaling properties in a period less than one year. In addition to the first scaling regime, 17 stations have second scaling regime and 3 stations have the third scaling regime. None of the stations in its second scaling regime have fractal nature and only one station in the third scaling regime has fractal nature.Precipitation with multiple time scales is considered as one of the most important parameters in water resources studies. Precipitation with different time scales can be expressed with fractal nature. One of the standard tools in the fractal investigation of hydrological processes is the power spectrum method. In this method, the power spectrum is calculated with transmitted observation from time space to frequency space. When all or a part of the spectral follow the power functions, the data in this power range will have fractal properties. In this study, the power spectrum of monthly precipitation of the 33 rain gauge stations in Iran was analyzed and scaling regimes with spectral exponent value are shown for each station. The results indicate that 81 percent of the stations have fractal nature and scaling properties in a period less than one year. In addition to the first scaling regime, 17 stations have second scaling regime and 3 stations have the third scaling regime. None of the stations in its second scaling regime have fractal nature and only one station in the third scaling regime has fractal nature.