Comparison of prediction model using spatial discriminant analysis for marine water quality index in mangrove estuarine zones

MARINE POLLUTION BULLETIN

卷:141

Article

Samsudin, Mohd Saiful^1,3  Azid, Azman¹  Khalit, Saiful Iskandar¹  Sani, Muhamad Shirwan Abdullah²  Lananan, Fathurrahman¹ 

[1] Univ Sultan Zainal Abidin UniSZA, Fac Bioresources & Food Ind, Besut Campus, Besut 22200, Terengganu, Malaysia

[2] Int Islamic Univ Malaysia, Int Inst Halal Res & Training, Selangor, Malaysia

[3] Dr FAS Technol, Block D1,2nd Floor UniSZA Digital Hub, Besut 222000, Terengganu, Malaysia

关键词: Marine water quality;    Discriminant analysis;    Artificial neural networks;    Multiple linear regression;    Mangrove estuarine zone;   

DOI  :  10.1016/j.marpolbul.2019.02.045

来源: Elsevier

【 摘 要 】

The prediction models of MWQI in mangrove and estuarine zones were constructed. The 2011-2015 data employed in this study entailed 13 parameters from six monitoring stations in West Malaysia. Spatial discriminant analysis (SDA) had recommended seven significant parameters to develop the MWQI which were DO, TSS, O&G, PO4, Cd, Cr and Zn. These selected parameters were then used to develop prediction models for the MWQI using artificial neural network (ANN) and multiple linear regressions (MLR). The SDA-ANN model had higher R-2 value for training (0.9044) and validation (0.7113) results than SDA-MLR model and was chosen as the best model in mangrove estuarine zone. The SDA-ANN model had also demonstrated lower RMSE (5.224) than the SDA-MLR (12.7755). In summary, this work suggested that ANN was an effective tool to compute the MWQ in mangrove estuarine zone and a powerful alternative prediction model as compared to the other modelling methods.

【 授权许可】

Free