Single Order of Multiple Regression Model of Water Quality Index (WQI) in Manjung River and its Tributaries
A. H. Yahaya1, N. M. Salih2, M.K. Puteri Zarina3, W.M Dahalan4
1A. H. Yahaya, Universiti Kuala Lumpur – Malaysian Institute of Marine Engineering Technology (UNIKL-MIMET) ,Lumut, Perak, Malaysia
2N. M. Salih, Universiti Kuala Lumpur – Malaysian Institute of Marine Engineering Technology (UNIKL-MIMET) ,Lumut, Perak, Malaysia.
3M. K. Puteri Zarina, Universiti Kuala Lumpur – Malaysian Institute of Marine Engineering Technology (UNIKL-MIMET) ,Lumut, Perak, Malaysia
4W.M Dahalan, Universiti Kuala Lumpur – Malaysian Institute of Marine Engineering Technology (UNIKL-MIMET) ,Lumut, Perak, Malaysia.
Manuscript received on September 18, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 5572-5575 | Volume-8 Issue-12, October 2019. | Retrieval Number: L40131081219/2019©BEIESP | DOI: 10.35940/ijitee.L4013.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This research highlights a multi-variety technique to examine the relationship between dependent and independent variable in predicting the water quality index in Manjung Rivers and its affluents. The model building process been used to analyse and generate the data. There are 63 possible models for single order multiple regressions. The number of possible model started to reduce as we started to eliminate insignificant variable. This model then needs to run under eight selection criteria to identify the best model. The best model will be certified by using Mean absolute percentage error (MAPE) in order to measure the validity of the model.
Keywords: Multiple Regression Model, Water Quality Index, Single Order, Relationship
Scope of the Article: Regression and Prediction