scholarly journals Flood Risk Index Assessment: Case Study in Lenggor River Basin, Johor, Malaysia

2018 ◽  
Vol 7 (4.34) ◽  
pp. 473
Author(s):  
Nurul Afiqa Adila Zakaria ◽  
Ahmad Shakir Mohd Saudi ◽  
Mohd Khairul Amri Kamarudin ◽  
Muhammad Hafiz Md Saad
Keyword(s):  
Water Level ◽  
River Basin ◽  
State Government ◽  
Flood Risk ◽  
Flood Control ◽  
Risk Index ◽  
Secondary Data ◽  
Control Limit ◽  
Statistical Process ◽  
Risk Class

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 government to come out with a comprehensive plan of action in strengthening the flood risk management at Lenggor River basin, Johor.  

scholarly journals Flood Risk Index Assessment: Case Study in Lenggor River Basin, Johor, Malaysia

2018 ◽  
Vol 7 (4.34) ◽  
pp. 473
Author(s):  
Nurul Afiqa Adila Zakaria ◽  
Ahmad Shakir Mohd Saudi ◽  
Mohd Khairul Amri Kamarudin ◽  
Muhammad Hafiz Md Saad
Keyword(s):  
Water Level ◽  
River Basin ◽  
State Government ◽  
Flood Risk ◽  
Flood Control ◽  
Risk Index ◽  
Secondary Data ◽  
Control Limit ◽  
Statistical Process ◽  
Risk Class

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 government to come out with a comprehensive plan of action in strengthening the flood risk management at Lenggor River basin, Johor.  

scholarly journals Flood Risk Index Assessment: Case Study in Lenggor River Basin, Johor, Malaysia

2018 ◽  
Vol 7 (4.34) ◽  
pp. 370
Author(s):  
Nurul Afiqa Adila Zakaria ◽  
Ahmad Shakir Mohd Saudi ◽  
Mohd Khairul Amri Kamarudin ◽  
Muhammad Hafiz Md Saad
Keyword(s):  
Water Level ◽  
River Basin ◽  
State Government ◽  
Flood Risk ◽  
Flood Control ◽  
Risk Index ◽  
Secondary Data ◽  
Control Limit ◽  
Statistical Process ◽  
Risk Class

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 government to come out with a comprehensive plan of action in strengthening the flood risk management at Lenggor River basin, Johor.  

scholarly journals Flood Risk Pattern Recognition Analysis in Klang River Basin

2018 ◽  
Vol 7 (4.34) ◽  
pp. 86 ◽  
Author(s):  
Puteri Nor Ain Kandari ◽  
Ahmad Shakir Mohd Saudi ◽  
Pang Jyh Chyang ◽  
Mohd Khairul Amri Kamarudin ◽  
Muhammad Hafiz Md Saad ◽  
...  
Keyword(s):  
Water Level ◽  
River Basin ◽  
Flood Risk ◽  
Stream Flow ◽  
Flood Control ◽  
Main Tool ◽  
Principal Component ◽  
Statistical Process ◽  
Pattern Recognition Analysis ◽  
Risk Pattern

This study was implemented to identify the specific factors that lead to major contribution of floods in Klang River Basin. A thirty-year (1987-2017) database obtained from Department of Irrigation and Drainage (DID), the selected data was analyzed by using integrated Chemometric techniques. The finding from Correlation Analysis revealed strong correlation between stream flow and water level is more than 0.5 (= 0.799). The finding from Principal Component Analysis proved that the selected parameters were significant with the result of R2 > 0.7was applied as a main tool for further analysis. Based on the result, it revealed that stream flow and water level were the most significant hydrological factor that influenced flood risk pattern in Klang River basin. Based on the result from Statistical Process control (SPC), the finding showed that the Upper Control Limit (UCL) for water level was 30.290m. The plotted data which is more than 30.290 m can cause flood to occur in Klang River Basin. Thus, it is very important to continuously monitor and maintain the mitigation measure of flood in the study area to avoid flood to occur. This study also helps to provide visualization of flood pattern and show the optimal rates for the maximum limit for flood control in Klang River Basin.  

scholarly journals Assessment on Regional Flood Risk Trend in Northern Region of Malaysia: Case Study in Muda River Basin, Kedah

2018 ◽  
Vol 7 (4.34) ◽  
pp. 75 ◽  
Author(s):  
Marhanisa Abd Halim ◽  
Ahmad Shakir Mohd Saudi ◽  
Mohd Khairul Amri Kamarudin ◽  
Muaz Mahmud ◽  
Arvind Bala Krishnan ◽  
...  
Keyword(s):  
Water Level ◽  
River Basin ◽  
Flood Risk ◽  
Risk Model ◽  
Monsoon Season ◽  
Warning System ◽  
Principal Component ◽  
Northern Region ◽  
Control Limit ◽  
Statistical Process

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.  

scholarly journals New Approach in Analyzing Risk Level of Flood in Tropical Region: A Case Study at Pahang River Basin, Malaysia

2018 ◽  
Vol 7 (4.34) ◽  
pp. 103
Author(s):  
Aqilah Ismail ◽  
Ahmad Shakir Mohd Saudi ◽  
Mohd Khairul Amri Kamarudin ◽  
Muhammad Hafiz Md Saad ◽  
Azman Azid ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Water Level ◽  
River Basin ◽  
Flood Risk ◽  
Stream Flow ◽  
Principal Component ◽  
Component Analysis ◽  
Suspended Solid ◽  
Control Limit ◽  
Risk Level

This study focuses on flood risk recognition factor that leads to major contribution of floods in Pahang River basin, identify the correlation between variables and determine factor that influence the flood risk pattern in Pahang. Four hydrological variables been applied. Chemometric technique of Principal Component Analysis (PCA) method and Statistical Process Control (SPC) method were being applied to identify the main contributor for flood, predicting hydrological modeling and risk of flood occurrence at Pahang river basin. Findings from Principal Component Analysis (PCA) confirmed that all selected variables were significant. The relationship between Suspended Solid and Stream Flow with Water Level were very high with correlation of coefficient value more than 0.7. SPC set up a new control limit for all variables. Data beyond the Upper Control Limit (UCL) value is considered as high risk for flood occurrence. Most of the trend pattern showed in year 2007 as high peak. Rapid development growth and anthropogenic activities caused the sediment of Suspended Solid triggered the Water Level and Stream Flow to arise than normal level. Thus, local authority should take earlier precaution for flood prevention and emergency responses plan at the study area for any development of land by takes obligatory action to the developers especially those development that arise along river channel. 

scholarly journals Flood risk index pattern assessment: case study in Langat River Basin

2018 ◽  
Vol 9 (2S) ◽  
pp. 12 ◽  
Author(s):  
A.S.M. Saudi ◽  
M.K.A. Kamarudin ◽  
I.S.D. Ridzuan ◽  
R Ishak ◽  
A Azid ◽  
...  
Keyword(s):  
River Basin ◽  
Flood Risk ◽  
Risk Index ◽  
Langat River

scholarly journals Analysis of Flood Risk of Urban Agglomeration Polders Using Multivariate Copula

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1470 ◽  
Author(s):  
Yuqin Gao ◽  
Dongdong Wang ◽  
Zhenxing Zhang ◽  
Zhenzhen Ma ◽  
Zichen Guo ◽  
...  
Keyword(s):  
Water Level ◽  
River Basin ◽  
Flood Risk ◽  
Eastern China ◽  
Urban Agglomeration ◽  
Dependence Structure ◽  
Flood Risks ◽  
The Impact ◽  
Flood Characteristics ◽  
Qinhuai River

Urban agglomeration polders (UAPs) are often used to control flooding in eastern China. The impacts of UAPs on individual flood events have been extensively examined, but how flood risks are influenced by UAPs is much less examined. This study aimed to explore a three-dimensional joint distribution of annual flood volume, peak flow and water level to examine UAPs’ impact on flood risks based on hydrological simulations. The dependence between pairwise hydrological characteristics are measured by rank correlation coefficients and graphs. An Archimedean Copula is applied to model the dependence structure. This approach is applied to the Qinhuai River Basin where UAPs are used proactively for flood control. The result shows that the Frank Copula can better represent the dependence structure in the Qinhuai River Basin. UAPs increase risks of individual flood characteristics and integrated risks. UAPs have a relatively greater impact on water level than the other two flood characteristics. It is noted that the impact on flood risk levels off for greater floods.

scholarly journals Artificial Neural Network Model for Estimation of Suspended Sediment Load in Krishna River Basin, India

2020 ◽  
Vol 9 (3) ◽  
pp. 3701-3705
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Suspended Sediment ◽  
River Basin ◽  
Flood Control ◽  
Sediment Load ◽  
Water Discharge ◽  
Suspended Sediment Load ◽  
Artificial Neural

The correct assessment of amount of sediment during design, management and operation of water resources projects is very important. Efficiency of dam has been reduced due to sedimentation which is built for flood control, irrigation, power generation etc. There are traditional methods for the estimation of sediment are available but these cannot provide the accurate results because of involvement of very complex variables and processes. One of the best suitable artificial intelligence technique for modeling this phenomenon is artificial neural network (ANN). In the current study ANN techniques used for simulation monthly suspended sediment load at Vijayawada gauging station in Krishna river basin, Andhra Pradesh, India. Trial & error method were used during the optimization of parameters that are involved in this model. Estimation of suspended sediment load (SSL) is done using water discharge and water level data as inputs. The water discharge, water level and sediment load is collected from January 1966 to December 2005. This approach is used for modelled the SSL. By considering the results, ANN has the satisfactory performance and more accurate results in the simulation of monthly SSL for the study location.

scholarly journals Changes in stage–flow relation of the East River, the Pearl River basin: causes and implications

2012 ◽  
Vol 44 (4) ◽  
pp. 737-746 ◽  
Author(s):  
Qiang Zhang ◽  
Kun Li ◽  
Vijay P. Singh ◽  
Xiaohong Chen ◽  
Jianfeng Li
Keyword(s):  
Water Level ◽  
River Basin ◽  
Flood Control ◽  
Morphological Changes ◽  
River Channel ◽  
Pearl River Basin ◽  
Abrupt Decrease ◽  
East River ◽  
The Pearl River ◽  
High Level

Water level and streamflow extracted from 891 hydrological episodes from both dry and flood seasons covering a period of 1954–2009 were analyzed to investigate stage–flow relations. Results indicate the following. (1) Since the early 1990s the low/high flow is increasing/decreasing. The water level, particularly the high level, is consistently decreasing. An abrupt decrease of water level is observed since the early 1990s at the lower East River. (2) Stage–streamflow relation is usually stable in the river reach with no significant bedform morphological changes. Changes in the geometric shape of the river channel are the major cause of the change in the stage–streamflow relation. (3) An abrupt decrease of water level at the Boluo station is mainly the result of abnormally rapid downcutting of the riverbed due to extensive sand dredging within the channel which caused serious headwater erosion. This human-induced modification by downcutting of the river channel may lead to significant hydrological alterations and may have critical implications for flood control, conservation of eco-environment, and also for basin-wide water resources management in the lower East River basin.

Flood Risk Pattern Recognition Using Chemometric Technique: A Case Study In Kuantan River Basin

2014 ◽  
Vol 72 (1) ◽  
Author(s):  
Ahmad Shakir Mohd Saudi ◽  
Hafizan Juahir ◽  
Azman Azid ◽  
Mohd Khairul Amri Kamarudin ◽  
Mohd Fadhil Kasim ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
River Basin ◽  
Stream Flow ◽  
Pearson Correlation ◽  
Suspended Solid ◽  
Test Analysis ◽  
Risk Class ◽  
Artificial Neural

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%.

Sign in / Sign up

Export Citation Format

Share Document