Flood damage assessment is important in flood risk management for the assessment of flood vulnerability, development of flood risk map and flood management financial appraisal. In Malaysia, there is a lack of studies on flood damages estimation. In addition, the needed data for the assessment of flood damages is scarce. This review identified the approaches and problems in flood damage assessment. For Malaysia, the combination of four elements namely; flood characteristics (flood depth and flood duration), characteristic of exposed elements, value of exposed element and flood damage function curve are recommended. The scarcity of data for developing flood damage curve could partly be overcome by applying synthetic method to generate additional data from the existing flood damage data.
Developing water level forecasting models is essential in water resources management and flood prediction. Accurate water level forecasting helps achieve efficient and optimum use of water resources and minimize flooding damages. The artificial neural network (ANN) is a computing model that has been successfully tested in many forecasting studies, including river flow. Improving the ANN computational approach could help produce accurate forecasting results. Most studies conducted to date have used a sigmoid function in a multi-layer perceptron neural network as the basis of the ANN; however, they have not considered the effect of sigmoid steepness on the forecasting results. In this study, the effectiveness of the steepness coefficient (SC) in the sigmoid function of an ANN model designed to test the accuracy of 1-day water level forecasts was investigated. The performance of data training and data validation were evaluated using the statistical index efficiency coefficient and root mean square error. The weight initialization was fixed at 0.5 in the ANN so that even comparisons could be made between models. Three hundred rounds of data training were conducted using five ANN architectures, six datasets and 10 steepness coefficients. The results showed that the optimal SC improved the forecasting accuracy of the ANN data training and data validation when compared with the standard SC. Importantly, the performance of ANN data training improved significantly with utilization of the optimal SC.
Flood damage estimation is an essential element in the assessment of flood risk. However, the assessment of flood damage in developing countries is challenging due to the scarcity of historical data. An attempt has been made to assess the flood damages of 2013 Kuantan flood and to develop a flood damage function model based on the socioeconomic and property characteristics of the study area. A field survey was conducted to gather damage data and information regarding the flood event using face to face interview technique. Age, household income, educational background, occupation and the distance from river have been identified as the most significant variables that influence the residential flood damages. A damage model to aid in the estimation of the structural and content damage have been developed. The preliminary results can be used in the future flood damage assessment works, especially in the development of flood damage function curve.
Flood forecasting models are a necessity, as they help in planning for flood events, and thus help prevent loss of lives and minimize damage. At present, artificial neural networks (ANN) have been successfully applied in river flow and water level forecasting studies. ANN requires historical data to develop a forecasting model. However, long-term historical water level data, such as hourly data, poses two crucial problems in data training. First is that the high volume of data slows the computation process. Second is that data training reaches its optimal performance within a few cycles of data training, due to there being a high volume of normal water level data in the data training, while the forecasting performance for high water level events is still poor. In this study, the zoning matching approach (ZMA) is used in ANN to accurately monitor flood events in real time by focusing the development of the forecasting model on high water level zones. ZMA is a trial and error approach, where several training datasets using high water level data are tested to find the best training dataset for forecasting high water level events. The advantage of ZMA is that relevant knowledge of water level patterns in historical records is used. Importantly, the forecasting model developed based on ZMA successfully achieves high accuracy forecasting results at 1 to 3 h ahead and satisfactory performance results at 6 h. Seven performance measures are adopted in this study to describe the accuracy and reliability of the forecasting model developed
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