An investigation of trends in precipitation volume for the last three decades in different regions of Fars province, Iran   总被引：1，自引：1，他引：0  

Hossein Ahani  Mehrzad Kherad  Mohammad Reza Kousari  Mehdi Rezaeian-Zadeh  Mohammad Amin Karampour  Faezeh Ejraee  Saeedeh Kamali 《Theoretical and Applied Climatology》2012,109(3-4):361-382

Under condition of climate changes as global warming, monitoring and detecting trend of precipitation volume is essential and will be useful for agricultural sections. Considering the fact that there were not enough research related to precipitation volume, this study aimed to determine trends in precipitation volume, monthly and annually in different regions of Fars province for the last three decades (33?years period; 1978–2010). Fars province is located in arid and semi-arid regions of Iran, and it plays an important role in agricultural production. Inverse distance weighting interpolation method was used to provide precipitation data for all regions. To analyze the trends of precipitation volume, Mann–Kendall test, Sen’s slope estimator, and 10-year moving average low-pass filter (within time series) were used. The negative trends were identified by the Sen’s slope estimator as well as Mann–Kendall test. However, all the trends were insignificant at the surveyed confidence level (95%). With regards to the application of 10-year moving average low-pass filter, a considerable decreasing trend was observed after around year 1994. Since one of the most important restrictions in agricultural development of the Fars province is lack of sufficient water resources, any changes onward to lack of sufficient precipitation impose impressive pressure and stress on valuable resources and subsequently agricultural production.  相似文献   

MLP-based drought forecasting in different climatic regions   总被引：1，自引：0，他引：1  

Mehdi Rezaeian-Zadeh  Hossein Tabari 《Theoretical and Applied Climatology》2012,109(3-4):407-414

Water resources management is a complex task and is further compounded by droughts. This study applies a multilayer perceptron network optimized using Levenberg–Marquardt (MLP) training algorithm with a tangent sigmoid activation function to forecast quantitative values of standardized precipitation index (SPI) of drought at five synoptic stations in Iran. The study stations are located in different climatic regions based on De Martonne aridity index. In this study, running series of total precipitation corresponding to 3, 6, 9, 12, and 24?months were used and the corresponding SPIs were calculated: SPI3, SPI6, SPI9, SPI12, and SPI24. The multilayer perceptrons (MLPs) for SPIs with the 1-month lead time forecasting, were tested and validated. Four different input vectors were considered during network development. In the first model, MLP constructed by importing antecedent SPI with 1-, 2-, 3-, and 4-month time lags and antecedent precipitation with 1- and 2-month time lags (MLP1). Addition of antecedent North Atlantic Oscillation or antecedent Southern Oscillation Index with 1-month time lag or both of them to MLP1 led to MLP2, MLP3, and MLP4, respectively. The MLP models were evaluated using the root mean square error (RMSE) and the coefficient of determination (R ²). The results showed that MLP4 had a higher prediction efficiency than the other MLPs. The more satisfactory results of RMSE and R ² values of MLP4 for 1-month lead time for validation phase were equal to 0.35 and 0.92, respectively. Also, results indicated that MLPs can forecast SPI24 and SPI12 more accurately than the other SPIs.  相似文献   

Prediction of monthly discharge volume by different artificial neural network algorithms in semi-arid regions     

Rezaeian-Zadeh  Mehdi  Tabari  Hossein  Abghari  Hirad 《Arabian Journal of Geosciences》2013,6(7):2529-2537

Arabian Journal of Geosciences - Prediction of monthly discharge volume is important for reservoir management and evaluation of drinking-water supplies. Also, it is very essential in arid and...  相似文献   

Hourly air temperature driven using multi-layer perceptron and radial basis function networks in arid and semi-arid regions   总被引：2，自引：0，他引：2  

Mehdi Rezaeian-Zadeh  Shahrookh Zand-Parsa  Hirad Abghari  Masih Zolghadr  Vijay P. Singh 《Theoretical and Applied Climatology》2012,109(3-4):519-528

This study employed two artificial neural network (ANN) models, including multi-layer perceptron (MLP) and radial basis function (RBF), as data-driven methods of hourly air temperature at three meteorological stations in Fars province, Iran. MLP was optimized using the Levenberg–Marquardt (MLP_LM) training algorithm with a tangent sigmoid transfer function. Both time series (TS) and randomized (RZ) data were used for training and testing of ANNs. Daily maximum and minimum air temperatures (MM) and antecedent daily maximum and minimum air temperatures (AMM) constituted the input for ANNs. The ANN models were evaluated using the root mean square error (RMSE), the coefficient of determination (R ²) and the mean absolute error. The use of AMM led to a more accurate estimation of hourly temperature compared with the use of MM. The MLP-ANN seemed to have a higher estimation efficiency than the RBF ANN. Furthermore, the ANN testing using randomized data showed more accurate estimation. The RMSE values for MLP with RZ data using daily maximum and minimum air temperatures for testing phase were equal to 1.2°C, 1.8°C, and 1.7°C, respectively, at Arsanjan, Bajgah, and Kooshkak stations. The results of this study showed that hourly air temperature driven using ANNs (proposed models) had less error than the empirical equation.  相似文献