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Flood forecasting is one of the most important and demanding operational responsibilities carried out by meteorological services all over the world. This task is complicated in the field of meteorology because all decisions have to consider in the visage of physiographical uncertainty factors such as the land coverage and vegetation, type of soil and topology of the catchment area [1][2]. This paper...
Floods occurrences are the most hated environmental hazard around the world. This is due to floods threaten human life and furthermore affected the economy of the involved country. In Malaysia, floods are usually due to the season monsoon and heavy rains that causing flash floods usually in urban area. Therefore, it is a must for researchers around the world to find a solution in solving this problem...
Nowadays flood water level predictions have become one of the most popular subject matter among researcher because this natural disaster damages people's life and property. In addition, flood is also one of the natural disasters that occur frequently around the world. However, since the dynamic of the flood itself is highly nonlinear, it is a very difficult task to predict the flood water level ahead...
Predicting flood disasters are good potential research areas due to its impact to publics and economics of the affected country. With rapid economic growth and urbanization, flash floods in cities are frequent and annoying to publics. Thus, accurate and reliable prediction model of respective rivers that causing flood to highly dense populated area is needed so that the public can be warn of the possible...
Flood prediction modelling is one of the most popular research areas among researcher around the world. This is due to negative impacts to the economy and society that were caused by flood. The dynamic behaviours of river water level that causing flood were commonly modelled by researcher either by equations using physic theories or by black-box model. River water level prediction model that could...
Global warming is the cause of climate change effected to the severe flood disaster. Improvements of water level prediction model are needed. The accuracy of prediction model can reduce flood damage. This research aims to extend the water level prediction model with back propagation neural network. The proposed model tested the important factors in order to predict the water levels. The input of the...
Yangtze River is the world's busiest inland waterway. Ships need to be guided when passing through controlled waterways based on their trajectory predictions. Inaccurate predicted trajectories lead to non-optimal traffic signalling which may cause significant traffic jam. For the existing intelligent traffic signalling systems (ITSSs), ships are supposed to travel exactly along the mid-line of the...
Flood is an overflowing of a large amount of water beyond its normal confines, especially over what is normally called dry land. Therefore, flood prediction system is crucial in order to notify the public about the incoming flood and an important task to achieve. The flood prediction may be very useful especially in the east cost of Malaysia. Artificial Neural Network (ANN) is well-known as an effective...
Flood is defined as an overflow of large amount of water beyond its normal limits. Therefore, it has become threat to people's life and can cause damages to properties. However, in Malaysia, the only existing flood warning system are the alarming system which only notify residents nearby flood location to evacuate only when flood occur. Thus, flood water level prediction is very much needed in order...
Recently, the applications of Artificial Neural Network (ANN) in various hydrologic problems have becoming popular. This is due to ability of ANN models to estimate nonlinear functions and hence become important tools to solve diverse water resources problems. Particularly, ANN models have been used in hydrological fields such as river flow forecasting, rainfall-runoff estimation, flood prediction...
This paper presents a review on flood modeling and rainfall-runoff relationships. It was found many methods have been used by previous researches to solve the problem in flood modeling. The suitable techniques were applied based on flood modeling parameters and watershed intensity. Understanding on the rainfall-runoff relationships is one of the requirement and necessity in hydrometeorology. Thus,...
Rainfall and river flow are one of the most difficult elements of hydrological cycle to predict. This is due to tremendous range of variability it displays over a wide range of scale both in terms of space and time. The situation is further aggravated by the fact that rainfall-runoff is also very difficult to measure at scales of interest to hydrology and climatologic. Computational intelligence techniques...
Recently, ANN models have been successfully applied in flood water level prediction system. However, most of publication on flood prediction only focusing on flood modelling and no element of prediction time was mentioned. Therefore, flood water level prediction is a new avenue to embark on in order to give early warning for evacuation purposes. This paper proposeda 4 hours ahead flood water level...
Flood is one of natural disaster that has becomes major threat around the world. Flood disaster may damages people’s life and property. Therefore, an accurate flood water level prediction is very important in flood modelling because it can give ample time to residents nearby flood location for evacuation purposes. However, due to the dynamics of flood water level itself is highly nonlinear, Artificial...
Through analyzing main affecting factors of the artificial neural network model, the optimization model is established and the optimization parameters is obtain based on the method of momentum and self-adaptive of learn rate, This optimize artificial neural network is not only set up with the limited training samples, but also can improve the operating speed and study efficiency. The optimization...
Considering seasonal feature of the flood events, a nonlinear perturbation model based on Artificial Neural Network is developed. The model structure is similar to that of the Linear Perturbation Model. The deference is that ANN, instead of linear response function, was used to simulate the unknown relationship between the input perturbing terms and the output perturbing terms. The reach from Huayuankou...
Previously, it is not easy to solve problems like having a lot of Water Factors, being hard to express the whole process of change when use deterministic model to predict the water quality. In order to solve the problem, the essay founded and used BP and RBF, the predict model of combined Multiple Neural Networks to check and simulate the data of water quality monitoring of Fengman Reservoir on the...
This paper researches on Elman neural network and wavelet analysis based on the existing literatures and proposes the Elman wavelet neural network model. This paper also provides Elman neural network training process to apply it to groundwater Level Prediction of Naoli River Basin. This paper proposes groundwater level prediction model of Naoli River basin based on Elman wavelet neural networks. It...
In this paper, through combining information diffusion principle and BP neural network theory, a new prediction model of drought disaster assessment is established. First, the original data are fuzzily processed based on information diffusion method, then a new training sample is formed; second, the new sample is used to design and train BP neural network; finally, the trained fuzzy neural network...
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