Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error, and thus the MAE would be a better metric for that purpose. While some concerns over using RMSE raised by W...

parameter estimation of the nonlinear muskingum model is a highly nonlinear optimization problem. although various techniques have been applied to optimize the coefficients of the nonlinear muskingum flood routing models, but an efficient method for this purpose in the calibration process is still lacking. the accuracy of artificial bee colony (abc) algorithm is investigated in this paper to op...

BACKGROUND Cases of hemorrhagic fever with renal syndrome (HFRS) are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS. METHODS Two hybrid models, one composed of...

This paper proposes a fast block-matching algorithm that uses three fast matching error measures, besides the conventional mean-absolute error (MAE) or mean-square error (MSE). An incoming reference block in the current frame is compared to candidate blocks within the search window using multiple matching criteria. These three fast matching error measures are established on the integral project...

The paper employs Artificial Neural Network (ANN) to forecast foreign exchange rate in India during 1992-2009. We used two types of data set (daily and monthly) for US dollar, British pound, euro and Japanese yen. The performance of forecasting is quantified by using various loss functions namely root mean square error (RMSE), mean absolute error (MAE), mean absolute deviation (MAD) and mean ab...

background: water is considered as the main source of life but water resources are limited and nonrenewable. different factors have caused groundwater to decrease. therefore, modeling and predicting groundwater level is of great importance. methods: monthly groundwater level data of about 20 years (october 1991 to february 2012) from the hamadan-bahar plain, west of iran were used based on peiz...

accurate prediction of municipal solid waste’s quality and quantity is crucial for designing and programming municipal solid waste management system. but predicting the amount of generated waste is difficult task because various parameters affect it and its fluctuation is high. in this research with application of feed forward artificial neural network, an appropriate model for predicting the...

PURPOSE To compare the accuracy of four different intraocular lens (IOL) power calculation formulas for eyes with mean keratometry values greater than 46 diopters (D). METHODS Forty five eyes from 45 patients who were candidates for senile cataract surgery with mean keratometry values greater than 46 D were included. Calculation of the IOL power was performed by the Lenstar. The implanted IOL...

The problem of fuzzy time series forecasting plays an important role in many scientific areas such as statistics and neural networks. While forecasting fuzzy time series, most of forecasting applications use the same length of intervals. The determination of length of intervals is significant and critical in fuzzy time series forecasting. The usage of convenient performance measure may also hav...