Cop Abstract: Event sediment transport and yield were studied for 45 events in the upstream part of the 260 km agricultural Koga catchment that drains to an irrigation reservoir. Discharge and turbidity data were collected over a period of more than a year, accompanied by grab sampling. Turbidity was very well correlated with the sediment concentrations from the samples (r = 0.99), which allowe...

Demand forecasting is one of the important activities in a supply chain which provides all the supply chain planning processes with market information crucial for efficient supply chain management. Its performance is measured by forecasting error, which is defined using the difference between forecast and actual sales. In this paper, we classify the forecasting error types based on the cases th...

China’s wind power has developed rapidly in the past few years, the large-scale penetration of which will bring big influence on power systems. The wind speed forecasting research is quite important because it can alleviate the negative impacts. This paper reviews the current wind speed forecasting techniques in China. The literature (written in Chinese) sources and classification were firstly ...

A quantitative understanding of both natural and anthropogenic sediment sources is needed to accurately assess and predict the potentially adverse effects of land development on aquatic ecosystems. The main objective of this study was to quantify sediment production and delivery rates in a dry tropical environment on the island of St. John in the eastern Caribbean. One to three years of measure...

Despite several individual forecasting models that have been proposed in the literature, accurate forecasting is yet one of the major challenging problems facing decision makers in various fields, especially financial markets. This is the main reason that numerous researchers have been devoted to develop strategies to improve forecasting accuracy. One of the most well established and widely use...

In this paper, we propose a new residual analysis method using Fourier series transform into fuzzy time series model for improving the forecasting performance. This hybrid model takes advantage of the high predictable power of fuzzy time series model and Fourier series transform to fit the estimated residuals into frequency spectra, select the low-frequency terms, filter out high-frequency term...

Short term load forecasting (STLF) plays an important role in the economic and reliable operation ofpower systems. Electric load demand has a complex profile with many multivariable and nonlineardependencies. In this study, recurrent neural network (RNN) architecture is presented for STLF. Theproposed model is capable of forecasting next 24-hour load profile. The main feature in this networkis ...