Flood Risk Pattern Recognition Using Integrated Chemometric Method and Artificial Neural Network: A Case Study in the Johor River Basin

2015 ◽  
Vol 74 (1) ◽  
Author(s):  
Ahmad Shakir Mohd Saudi ◽  
Azman Azid ◽  
Hafizan Juahir ◽  
Mohd Ekhwan Toriman ◽  
Mohammad Azizi Amran ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
River Basin ◽  
Monsoon Season ◽  
Strong Relationship ◽  
Suspended Solid ◽  
Risk Level ◽  
Statistical Process ◽  
Risk Class ◽  
Artificial Neural

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.

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%.

Paraconsistent Artificial Neural Network for Structuring Statistical Process Control in Electrical Engineering

2016 ◽  
pp. 77-102
Author(s):  
João Inácio da Silva Filho ◽  
Clovis Misseno da Cruz ◽  
Alexandre Rocco ◽  
Dorotéa Vilanova Garcia ◽  
Luís Fernando P. Ferrara ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Control ◽  
Statistical Process Control ◽  
Electrical Engineering ◽  
Statistical Process ◽  
Artificial Neural

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 Monthly Suspended Sediment Load Estimation using Artificial Neural Network and Traditional Models in Krishna River Basin, India

2020 ◽  
Vol 9 (3) ◽  
pp. 613-618
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Suspended Sediment ◽  
River Basin ◽  
Sediment Yield ◽  
Sediment Load ◽  
Water Discharge ◽  
Tectonic Activity ◽  
Precipitation Seasonality ◽  
Artificial Neural

The measurement of sediment yield is essential for getting the information of the mass balance between sea and land. It is difficult to directly measure the suspended sediment because it takes more time and money. One of the most common pollutants in the aquatic environment is suspended sediments. The sediment loads in rivers are controlled by variables like canal slope, basin volume, precipitation seasonality and tectonic activity. Water discharge and water level are the major controlling factor for estimate the sediment load in the Krishna River. Artificial neural network (ANN) is used for sediment yield modeling in the Krishna River basin, India. The comparative results show that the ANN is the easiest model for the suspended sediment yield estimates and provides a satisfactory prediction for very high, medium and low values. It is also noted that the Multiple Linear Regressions (MLR) model predicted an many number of negative sediment outputs at lower values. This is entirely unreality because the suspended sediment result can not be negative in nature. The ANN is provided better results than traditional models. The proposed ANN model will be helpful where the sediment measures are not available.

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