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The ensemble method has long been used to reduce the errors that are caused by initial conditions and/or parameterizations of models in forecasting problems. In this study, neural network (NN) simulations are applied to ensemble weather forecasting. Temperature forecasts averaged over 2 weeks from four different forecasts are used to develop the NN model. Additionally, an ensemble mean of bias-corrected data is used as the control experiment. Overall, ensemble forecasts weighted by NN with feed forward backpropagation algorithm gave better root mean square error, mean absolute error, and same sign percent skills compared to those of the control experiment in most stations and produced more accurate weather forecasts.  相似文献   
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The effectiveness of coagulation (at pH values of 6, 7.5, and 9), flocculation (at pH 9), and coagulation plus flocculation (at pH 9) on turbidity removal from natural stone (travertine) processing wastewaters (NSPW) were examined by applying classical sedimentation tests. FeCl3·6 H2O, AlCl3, and Al2(SO4)3·16 H2O were used as coagulants and a polyacrylamide based anionic polymer was used as the flocculant. In this way, it was found that the coagulation method alone was not sufficient to purify NSPW, whereas flocculation and coagulation plus flocculation methods provided superior purification. Among the coagulants used, AlCl3 gave the best result in terms of turbidity removal by coagulation from NSPW at pH 6 and 9, whereas the turbidity removal performances of the three coagulants were almost identical at pH 7.5. In addition, relatively low pH (i. e., pH 6) improved the purification performance of all coagulants. During coagulation of NSPW at pH 6, a charge neutralization mechanism played a decisive role in turbidity removal. However, in neutral (pH 7.5) and slightly basic (pH 9) media, a sweep coagulation mechanism was predominant. For flocculation of NSPW, the basic mechanism comprised of polymer bridging.  相似文献   
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