Streamflow forecasting has an important role in water resource management (e.g. flood control, drought management, reservoir design, etc.). In this paper, the application of Adaptive Neuro Fuzzy Inference System (ANFIS) is used for long-term streamflow forecasting (monthly, seasonal) and moreover, cross-validation method (K-fold) is investigated to evaluate test-training data in the model.Then,...

this paper presents a model predictive control (mpc) approach for production and inventory control systems. model predictive control previously has been successfully applied to supply chain problems; however most systems that have been proposed so far possess no information on future demand. the incorporation of a forecasting methodology in an mpc framework can promote the efficiency of control...

abstract the aim of this study was to selecting the suitable model for forecast land, production and price of sugar beet in iran. for this purpose, models applied to forecast are arima, single and double exponential smoothing, harmonic, artificial neural network and arch for period 1993-2008. results of durbin-watson tests, land, production and price of sugar beet series were found non stochast...

In this study, we developed a model of combined streamflow forecasting based on cross entropy to solve the problems of streamflow complexity and random hydrological processes. First, we analyzed the streamflow data obtained from Wudaogou station on the Huifa River, which is the second tributary of the Songhua River, and found that the streamflow was characterized by fluctuations and periodicity...

CHIMERE is a chemical transport model widely used within the air quality scientific community. The actual version of CHIMERE is coupled with the MM5 mesoscale meteorological model. To facilitate model development, MM5 is replaced here with the Weather Research and Forecasting (WRF) model. With the accuracy of air quality models being highly dependent on the quality of meteorological drivers, th...

High frequency observations from a field campaign have been compared with time-step precipitation in the WRF regional climate model. Two microphysical schemes of different levels of sophistication have been tested. Both of the schemes generally reproduce the observed precipitation, yet the produced level of wetness is somewhat high, and the intensities rather low. Typical simulated intensities ...

This study empirically analyzes model accuracy, and applies grey forecasting to handle non-linear problems, insufficient data resources and forecasting involving small samples, and to construct the co-opetition diffusion model for the Lotka–Volterra (L.V.) system. Furthermore, this study examines historical data comprising revenue trends in the Taiwanese IC assembly industry during the past ten...

In a competitive and dynamic market, financial institutions must forecast the proportion of mortgages that will become delinquent, default or prepay. This paper develops a novel forecasting model with nonstationary Markov chain and Grey forecasting, capable of predicting the likelihood of delinquency, default and prepayment. Home mortgage data, obtained by a major Taiwan financial institution f...

In this paper we investigate the forecasting performance of the non-linear time series SETAR model by using Canadian GDP data from 1965 to 2000. Besides the with-insample fit, the forecasting performance of a standard linear ARIMA model for the same sample has also been generated for comparative purposes. Two forecasting methods, 1step-ahead and multi-step-ahead forecasting are compared for eac...