Long term prediction of maximum water levels and combined sewer overflow (CSO) in drainage systems are associated with large uncertainties. Especially on rainfall inputs, parameters, and assessment of return periods. This paper proposes a Monte Carlo based methodology for stochastic prediction of both maximum water levels as well as CSO volumes based on operations of the urban drainage model MO...

Accurate forecast of rainfall has been one of the most important issues in hydrological research. Due to rainfall forecasting involves a rather complex nonlinear data pattern; there are lots of novel forecasting approaches to improve the forecasting accuracy. In this paper, a new approach using the Modular Radial Basis Function Neural Network (M–RBF–NN) technique is presented to improve rainfal...

Attenuation due to rain is a primary cause of communication impairment on satellite-earth paths, especially above 10GHz. Rainfall is a serious source of attenuation at such a frequency. This paper presents the characteristics of rain distribution in USM based on measured one minute rain rate. For attenuation of rain, predictions models like ITU and VIHT (Variable Isotherm Height Technique) mode...

r a 200 .007 : +886 2 tu.edu.t Summary We investigated the effectiveness of combining gauge observations and satellite-derived precipitation on flood forecasting. Two data merging processes were proposed: the first one assumes that the individual precipitation measurement is non-bias, while the second process assumes that each precipitation source is biased and both weighting factor and bias pa...

Accurate and timely weather forecasting is a major challenge for the scientific community in hydrological research such as river training works and design of flood warning systems. Neural Network (NN) is a popular regression method in rainfall predictive modeling. This paper investigates the effectiveness of the hybrid Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) evolved neural ...

The coupling of hydrological distributed models to numerical weather prediction outputs is an important issue for hydrological applications such as forecasting of flood events. Downscaling meteorological predictions to the hydrological scales requires the resolution of two fundamental issues regarding precipitation, namely, (1) understanding the statistical properties and scaling laws of rainfa...

Indian summer monsoon rainfall is a process which is dependent on number of environmental and geological parameter. This makes it very hard to precisely predict the monsoon rainfall. As India is agriculture based country, a long range monsoon rainfall prediction is crucial for proper planning and organization of agriculture policy. Severe hydrological events, such as droughts, may result in dec...

Catchments that do not behave the way the hydrologist expects, expose the frailties of hydrological science, particularly its unduly simplistic treatment of input and model uncertainty. A conceptual rainfall– runoff model represents a highly simplified hypothesis of the transformation of rainfall into runoff. Sub-grid variability and mis-specification of processes introduce an irreducible model...

This paper presents a new and alternative method (in the context of urban drainage) for probabilistic hydrodynamical analysis of drainage systems in general and especially prediction of combined sewer overflow. Using a probabilistic shell it is possible to implement both input and parameter uncertainties on an application of the commercial urban drainage model MOUSE combined with the probabilis...

A good short-period forecast of heavy rainfall is essential for many meteorological and hydrological applications. Traditional deterministic and stochastic nowcasting methodologies have been inadequate in their characterization of pixelwise rainfall reflectivity propagation, intensity, and uncertainty. The methodology presented herein uses an approach that efficiently parameterizes spatio-tempo...