Improved the Prediction of Multiple Linear Regression Model Performance Using the Hybrid Approach: A Case Study of Chlorophyll-a at the Offshore Kuala Terengganu, Terengganu

作     者：Muhamad Safiih Lola Mohd Noor Afiq Ramlee G. Sugan Gunalan Nurul Hila Zainuddin Razak Zakariya MdSuffian Idris Idham Khalil Muhamad Safiih Lola;Mohd Noor Afiq Ramlee;G. Sugan Gunalan;Nurul Hila Zainuddin;Razak Zakariya;MdSuffian Idris;Idham Khalil

作者机构：School of Informatics and Applied Mathematics University Malaysia Terengganu Kuala Terengganu Malaysia School of Science Marin University Malaysia Terengganu Kuala Terengganu Malaysia 

出 版 物：《Open Journal of Statistics》 (统计学期刊（英文）)

年 卷 期：2016年第6卷第5期

页      面：789-804页

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Multi Linear Regression Artificial Neural Network ANN-MLR Chlorophyll-a Correlation 

摘      要：Efficiency and precision in prediction of Chlorophyll-a using this model is still a pandemic among researchers, due to the natural conditions in ocean water systems itself, which involved chemical, biological and physical processes and interaction among them may affect the model performance drastically. Thus, to overcome this problem as well as to improve the strength of MLR, we proposed a hybrid approach, i.e., an Artificial Neural Network to the MLR coins as Artificial Neural Network-Multiple Linear Regression (ANN-MLR). To investigate the performance of the proposed model, we compared Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and proposed hybrid Artificial Neural Network and Multiple Linear Regression (ANN-MLR) in the prediction of chlorophyll-a (chl-a) concentration by statistical measurement which are MSE and MAE. Achieving our objectives of study, we used 4 parameters, i.e. temperature (°C), pH, salinity (ppt), DO (ppm) at the Offshore Kuala Terengganu, Terengganu, Malaysia. The results showed that our proposed model can improve the performance of the model as compared to ANN and MLR due to small errors generated, error reduced, and increased the correlation coefficient for all parameters in both MSE and MAE, respectively. Thus, this result indicated that our proposed model is efficient, precise and almost perfect correlation as compared to ANN and MLR.