Earticle

초록

영어

This study evaluates the use of Multi-Layer Perceptron (MLP) neural network models to forecast water levels of a gauging station located at the Kuala Lumpur city centre in Malaysia using records of upstream multiple stations. Cross correlation analysis of water level data was performed to determine the input vectors which include the current and antecedent water level data of the upstream stations to ensure that of the data available, the most influential values corresponding to different lags are selected for analysis. Twelve well recorded storm events were used to train, test and validate the MLP models. The best performance based on MSE, MAE and R² was achieved with a model of 15 input vectors of upstream current and antecedent water levels, 7 hidden nodes and an output vector for the station at Kuala Lumpur centre. The R² values for training, testing and validation datasets are 0.81,0.85 and 0.85 respectively.

목차

Abstract
 1. Introduction
 2. Materials and Methods
  2.1. The Study Area
  2.2. Water Level Data
  2.3. Input Dataset Preparation
  2.4 Methodology
 3.0 Results and Discussion
 4.0 Summary and Conclusions
 References

키워드

저자

• Jer Lang Hong [ Taylor’s University, Hong and Associates ]
• KeeAn Hong [ Taylor’s University, Hong and Associates ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605