Flood Risk Pattern Recognition Analysis in Klang River Basin

This study constructs downscaling statistical model in analyzing the hydrological modeling in the study area which face the risk of flood occurrence as the impact of climate change. The combination of chemometric method and time series analysis in this study show that even during the monsoon season, rainfall and stream flow are not the major contribution towards the changing of water level in the study area. Based on Correlation Test, it shows that suspended solid and water level shows high correlation with p-value < 0.05. Factor Analysis being carried out to determine the major contribution to the changes of Water Level and the result shows that Suspended Solid shows a strong factor pattern with value 0.829. Based on Control Chat Builder for time series analysis, the Upper Control Limit for water level and suspended solid are 7.529 m and 1947.049 tons/day and the Lower Control Limit are 6.678 m and 178.135 tons/day. This shows that human development in the area gives high impact towards climate change and risk of flood in the study area which commonly face flood during monsoon season.

International Journal of Engineering & Technology, 2018

Flood is a major issue during monsoon season in Northern region of Malaysia especially in Muda River Basin. This study focused on the specific hydrology parameters that lead to the flood events in Muda River Basin, Kedah. There were 4 hydrologic parameters for thirty years of collected data from selected hydrology monitoring stations provided by Department of Irrigations and Drainage, Malaysia. The study applied Principal Component Analysis (PCA) and result shown that stream flow and suspended solid stand with highest correlation of coefficient variables with the changes of water level in the study area. Statistical Process Control (SPC) applied in this study was to determine the control limit for every selected parameter obtained from PCA. The Upper Control Limit value for water level reported from SPC analysis in the study area was 7.568m and starting from this level and above, the risk of flood is high to occur in the study area. This research proved that the flood risk model created in this study was accurate and flexible for flood early warning system at Muda River Basin.

Integrated Chemometric and Artificial Neural Network were being applied in this study to identify the main contributor for flood, predicting hydrological modelling and risk of flood occurrence at the Kuantan river basin. Based on the Correlation Test analysis, the relationship for Suspended Solid and Stream Flow with Water Level were very high with Pearson correlation of coefficient value more than 0.5. Factor Analysis had been carried out and based on the result, variables such as Stream Flow, Suspended Solid and Water Level turned out to be the major factors and had a strong factor pattern with the results of factor score with >0.7 respectively. Time series analysis was being employed and the limitation had been set up where the Upper Control Limit for Stream Flow, Suspended Solid and Water Level where at this level, it was predicted by using Artificial Neural Network (ANN) to be High Risk Class. The accuracy of prediction from this method stood at 97.8%.

International Journal of Engineering & Technology

The objective of this research is to determine the correlation of selected hydrological variables, to analyzed the significance factors influenced the occurrences of flood, to propose the flood control limit system and establish new flood risk index model in Lenggor River Basin based on secondary data derived from Department of Drainage and Irrigation (DID). Application of Chemometric technique such as Spearman’s Correlation Test, Principle Component Analysis, Statistical Process Control and Flood Risk Index created the most efficient results. Result shows water level has strong factor loading of 0.78 and significant for flood warning alert system application. The Upper Control Limit (UCL) for the water level in study area is 33.23m while the risk index for the water level set by the constructed formula of flood risk index consisting 0-100. The results show 20.6% classified as High Risk Class with index range from 70 and above. Thus, these findings are able to facilitate state gover...

This study looks into the downscaling of statistical model to produce and predict hydrological modelling in the study area based on secondary data derived from the Department of Drainage and Irrigation (DID) since 1982-2012. The combination of chemometric method and time series analysis in this study showed that the monsoon season and rainfall did not affect the water level, but the suspended solid, stream flow and water level that revealed high correlation in correlation test with p-value < 0.0001, which affected the water level. The Factor analysis for the variables of the stream flow, suspended solid and water level showed strong factor pattern with coefficient more than 0.7, and 0.987, 1.000 and 1.000, respectively. Based on the Statistical Process Control (SPC), the Upper Control Limit for water level, suspended solid and stream flow were 21.110 m3/s, 4624.553 tonnes/day, and 8.224 m/s, while the Lower Control Limit were 20.711 m, 2538.92 tonnes/day and 2.040 m/s. This shows that human development in the area gives high impact towards climate change and flood risk, and not the monsoon season. Prediction was carried out using the Artificial Neural Network (ANN) to classify risks into their own classes, and the rate of accuracy for the prediction was 97.1%. This meant that the points in the time series analysis which were located beyond Upper Control Limit were considered as High Risk class, and the probability for flood occurrence is very high. The other classes classified in this prediction are Caution Zone, Low Risk and No risk. This is important to set a trigger for warning system in the case of emergency response plan during flood.

Malaysian Journal of Analytical Sciences

This study is focusing on constructing the flood risk index in the Johor river basin. The application of statistical methods such as factor analysis (FA), statistical process control (SPC) and artificial neural network (ANN) had revealed the most efficient flood risk index. The result in FA was water level has correlation coefficient of 0.738 and the most practicable variable to be used for the warning alert system. The upper control limits (UCL) for the water level in the river basin Johor is 4.423m and the risk index for the water level has been set by this method consisting of 0-100.The accuracy of prediction has been evaluated by using ANN and the accuracy of the test result was R 2 = 0.96408 with RMSE= 2.5736. The future prediction for UCL in Johor river basin has been predicted and the value was 3.75m. This model can shows the current and future prediction for flood risk index in the Johor river basin and can help local authorities for flood control and prevention of the state of Johor.

This study is focusing on constructing the flood risk index in the Johor river basin. The application of statistical methods such as factor analysis (FA), statistical process control (SPC) and artificial neural network (ANN) had revealed the most efficient flood risk index. The result in FA was water level has correlation coefficient of 0.738 and the most practicable variable to be used for the warning alert system. The upper control limits (UCL) for the water level in the river basin Johor is 4.423m and the risk index for the water level has been set by this method consisting of 0-100.The accuracy of prediction has been evaluated by using ANN and the accuracy of the test result was R2 = 0.96408 with RMSE= 2.5736. The future prediction for UCL in Johor river basin has been predicted and the value was 3.75m. This model can shows the current and future prediction for flood risk index in the Johor river basin and can help local authorities for flood control and prevention of the state of Johor.

Flood is a major problem in Johor river basin, which normally happened during monsoon season. However in this study, it shows that rainfall did not have a strong relationship for the changes of water level compared to suspended solid and stream flow, where both variables have p-values of <0.0001 and these variables also became the main factors in contributing to the flood occurrence based on Factor Analysis result. Time Series Analysis was being carried out and based on Statistical Process Control, the limitation has been set up for mitigation in controlling flood. All data beyond the Upper Control Limit was predicted to have High Risk to face flood and Emergency Response Plan should be implemented to prevent complication and destruction because of flood. The prediction for the risk level was carried out using the application of Artificial Neural Network (ANN), where the accuracy of prediction was very high, with the result of 96% for the level of accuracy in the prediction of risk class.

The river is a natural detail that serves to drain the water from upstream to downstream. River flow is affected by several natural and man-made components. Flood phenomena a reason to know the value of the contribution of components of the discharge. Analysis of the main components of river discharge in the research conducted to address the causes of flooding that harm life and property. Data collection and processing streamflow, precipitation, volume of waste, drainage building dimensions, the volume of sediment and land use over a period of 10 years was obtained from the study sites. The research method is descriptive and quantitative percentage of principal component analysis is used to obtain the sequence of the components cause changes in flood discharge. The analysis showed precipitation components worth 50.702 % of the discharge, the volume of waste 23.776 %, 16.664 % drainage, sedimentation volume of 8.354 % and 0.503 % of land use. The order of the values is the biggest component causing flooding rainfall , waste management , drainage , sedimentation volume and land use at the sites. Information major components cause flooding can be used as an academic paper to enhance regulation and legislation as well as the preparation of programs and projects priority scale for flood prevention.

The city of Makassar is located in Maros, Tabaringan, Tallo, Jeneberang and Gowa-Takalar watersheds and Makassar non-watershed areas.This area is located along the coast of Makassar City whose stream flow is directly mainly to the sea or through small rivers (creek ). For the calculation of flood discharge, the study of watershed becomes crucial to know the position of study area to the influence of upstream river basin. Watershed area is very influential to flood discharge.In general, the larger the watershed the greater the amount of surface runoff is so that the greater the flow of surface or flood discharge. This study aims to identify the watershed and its effect towards floods in Makassar. By using an integrated spatial analysis method in Geographic Information Systems (SIG) version 10.5 application, the watershed of Makassar can be identified. The results show that watershed in Jeneberang region is the largest watershed area that empties into Makassar City. The watershed reaches 76,085.06 hectares while the Gowa-Takalar watershed is the smallest of 1.88 hectares. There are 2 watershed areas that have no effect on the risk of flood in Makassar City namely Maros watershed and Gowa-Takalar watershed. The research results can be utilized for water resource management needs such as primary and secondary flow planning, both by practitioners and the government of Makassar City.