Rainfall plays a significant role in managing the water level reservoir. The unpredictable amount of rainfall due to climate change can cause either overflow or dry Many individuals, especially those agricultural sector, rely on rain forecasts. Forecasting is challenging because changing nature weather. area Jimma southwest Oromia, Ethiopia subject this research, which aims develop forecasting ...

Rainfall measurement has an extensive historical precedent. Attempts have been made to standardise measurement procedures. This has never been successfully achieved. There are many sources of measurement error, some of which are compounded by poor rain gauge siting and a variation in gauge height. By far the worst cause of measurement inaccuracy is due to windinduced undercatching. Some solutio...

Characterization of the error associated with satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving spaceborne passive and active microwave measurements for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. The authors focus here on the error structure of NASA’s Tropical Rainf...

One of the principal issues related to hydrologic models for prediction of runoff is the estimation of extreme values (floods). It is well understood that unless the models capture the dynamics of rainfall-runoff process, the improvement in prediction of such extremes is far from reality. In this paper, it is proposed to develop a dual (combined and paralleled) artificial neural network (D-ANN)...

Most neural network hydrological modelling has used split-sample validation to ensure good out-of-sample generalisation and thus safeguard each potential solution against the danger of overfitting. However, given that each sub-set is required to provide a comprehensive and sufficient representation of both environmental inputs and hydrological processes, then to partition the data could create ...

In this study three data-driven water level forecasting models are presented and discussed. One is based on the artificial neural networks approach, while the other two are based on the Mamdani and the Takagi-Sugeno fuzzy logic approaches, respectively. All of them are parameterised with reference to flood events alone, where water levels are higher than a selected threshold. The analysis of th...

In this work an adaptive linear filter model in a autoregressive moving average (ARMA) topology for forecasting time series is presented. The time series are composed by observations of the accumulative rainfall every month during several years. The learning rule used to adjust the filter coefficients is mainly based on the gradient-descendent method. In function of the long and short term stoc...

Climate change adversely affects Ethiopian economy due to heavy dependence of the agricultural sector on rainfall. A decrease of rainfall and rise in temperature has been increasing the exposure of the country to frequent drought. The study was conducted in central Tigray, Adiha tabia, to examine the perception of farmers on trends of climate changes and existing coping strategies. Farmers’ kno...