Based on the comparison between several model outputs from CMIP5 （Coupled Model Intercomparison Project Phase-5） and the satellite rainfall mapping data of GSMaP （global satellite mapping of precipitation）, This p...Based on the comparison between several model outputs from CMIP5 （Coupled Model Intercomparison Project Phase-5） and the satellite rainfall mapping data of GSMaP （global satellite mapping of precipitation）, This paper selected MIROC4h as a future projection of rainfall in the Sittaung River basin, Myanmar, with the fine spatial resolution of 0.5°. At first, MIROC4h projection towards 2035 was corrected by using the error trend （GSMaP-MIROC4h） for nine years over-rapping of both outputs from 2006 to 2014. Assuming the seasonal autoregressive processes, future error trend at each grid point was estimated by the time series forecast of SARMAP processes using the nine years training data. Then future projection correction was done by M1ROC4h output plus error trend at each grid point to obtain the corrected MIROC4h precipitation. As a historical analysis, using the corrected precipitation in the Sittaung River basin and observed river discharge at the outlet of the river, the hydrological model （HSPF （Hydrological Simulation Program Fortran）） calibration was carried out with consideration of the water utilization data for darn/reservoir and irrigation. As a projection analysis, future simulation of hourly discharge at the outlet of Sittaung River from 2015 to 2035 was conducted by using the corrected MIROC4h precipitation. The results of projection analysis show that high risks of flood will appear in 2023 and 2028 and the risks of draught will be expected in 2019-2021.展开更多
Neural network and genetic algorithms are complementary technologies in the design of adaptive intelligent system. Neural network learns from scratch by adjusting the interconnections betweens layers. Genetic algorith...Neural network and genetic algorithms are complementary technologies in the design of adaptive intelligent system. Neural network learns from scratch by adjusting the interconnections betweens layers. Genetic algorithms are a popular computing framework that uses principals from natural population genetics to evolve solutions to problems. Various forecasting methods have been developed on the basis of neural network, but accuracy has been matter of concern in these forecasts. In neural network methods forecasted values depend to the choose of neural predictor structure, the number of the input, the lag. To remedy to these problem, in this paper, the authors are investing the applicability of an automatic design of a neural predictor realized by real Genetic Algorithms to predict the future value of a time series. The prediction method is tested by using meteorology time series that are daily and weekly mean temperatures in Melbourne, Australia, 1980-1990.展开更多
By analyzing the recent 15 years＇ statistical data of Zhejiang tourism human resources, this paper analyzes the status of Zhejiang tourism talents. ARIMA （Autoregressive Integrated Moving Average） model is a method...By analyzing the recent 15 years＇ statistical data of Zhejiang tourism human resources, this paper analyzes the status of Zhejiang tourism talents. ARIMA （Autoregressive Integrated Moving Average） model is a method of time series prediction. This paper predicts the trends of the next three years＇ demands of Zhejiang tourism talents based on ARIMA model in order to promote the tourism in Zhejiang Province. According to the demands forecasting, the number of the employees required by the hotels is 10 times of travel agencies in 2015. At last, some solutions and suggestions are provided such as strengthening the talents training establishing tourism talents mobility mechanism and improving tourism talents excitation mechanism展开更多
文摘Based on the comparison between several model outputs from CMIP5 （Coupled Model Intercomparison Project Phase-5） and the satellite rainfall mapping data of GSMaP （global satellite mapping of precipitation）, This paper selected MIROC4h as a future projection of rainfall in the Sittaung River basin, Myanmar, with the fine spatial resolution of 0.5°. At first, MIROC4h projection towards 2035 was corrected by using the error trend （GSMaP-MIROC4h） for nine years over-rapping of both outputs from 2006 to 2014. Assuming the seasonal autoregressive processes, future error trend at each grid point was estimated by the time series forecast of SARMAP processes using the nine years training data. Then future projection correction was done by M1ROC4h output plus error trend at each grid point to obtain the corrected MIROC4h precipitation. As a historical analysis, using the corrected precipitation in the Sittaung River basin and observed river discharge at the outlet of the river, the hydrological model （HSPF （Hydrological Simulation Program Fortran）） calibration was carried out with consideration of the water utilization data for darn/reservoir and irrigation. As a projection analysis, future simulation of hourly discharge at the outlet of Sittaung River from 2015 to 2035 was conducted by using the corrected MIROC4h precipitation. The results of projection analysis show that high risks of flood will appear in 2023 and 2028 and the risks of draught will be expected in 2019-2021.
文摘Neural network and genetic algorithms are complementary technologies in the design of adaptive intelligent system. Neural network learns from scratch by adjusting the interconnections betweens layers. Genetic algorithms are a popular computing framework that uses principals from natural population genetics to evolve solutions to problems. Various forecasting methods have been developed on the basis of neural network, but accuracy has been matter of concern in these forecasts. In neural network methods forecasted values depend to the choose of neural predictor structure, the number of the input, the lag. To remedy to these problem, in this paper, the authors are investing the applicability of an automatic design of a neural predictor realized by real Genetic Algorithms to predict the future value of a time series. The prediction method is tested by using meteorology time series that are daily and weekly mean temperatures in Melbourne, Australia, 1980-1990.
文摘By analyzing the recent 15 years＇ statistical data of Zhejiang tourism human resources, this paper analyzes the status of Zhejiang tourism talents. ARIMA （Autoregressive Integrated Moving Average） model is a method of time series prediction. This paper predicts the trends of the next three years＇ demands of Zhejiang tourism talents based on ARIMA model in order to promote the tourism in Zhejiang Province. According to the demands forecasting, the number of the employees required by the hotels is 10 times of travel agencies in 2015. At last, some solutions and suggestions are provided such as strengthening the talents training establishing tourism talents mobility mechanism and improving tourism talents excitation mechanism
