Proceedings of International Conference on Applied Innovation in IT
2026/03/31, Volume 14, Issue 1, pp.115-121

Multivariate Partial Linear Modeling for Groundwater Quality Anaiysis


Huda Yahay Ahmed and Emad Hazim Aboudi


Abstract: This research aims to estimate the multivariate partial linear model using two methods: Profile Least Squares and Profile Likelihood, with the goal of identifying the factors affecting the decline in groundwater quality in two geographical locations within Baghdad city (Abu Ghraib and Mahmudiya). The main problem lies in the presence of several response variables that are correlated with each other within the same model, which complicates the process of accurate estimation of the parameters. Additionally, there are correlations between the response variables and the explanatory variables, further challenging the estimation process. The model includes both linear and nonlinear variables, adding to the complexity of the analysis. Total dissolved solids and ion ratios were used as response variables, while the independent variables included sodium, chloride, and sulfate. The geographical location was incorporated as a nonparametric component in the analysis. The two methods were compared based on the Mean Squared Error (MSE) criterion to determine the preferred method for model estimation. Real data were used to verify the effectiveness of the methods in practical applications, and the results indicated the superiority of the Profile Least Squares method in providing more accurate and reliable estimates of the model.

Keywords: Profile Least Squares, Profile Likelihood, Multivariate Partial Linear, Kernel Smoother.

DOI: Under indexing

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