Water Quality Prediction and Classification based on Linear Discriminant Analysis and Light Gradient Boosting Machine Classifier Approach

Abstract

Estimating water quality has existed as one of the vital factors embarked on the planet in the present eons. This paper illustrates a water quality estimate based on the Linear Discriminant Analysis (LDA) technique. Weighted arithmetic index technique is used in the computation of the Water Quality Index (WQI). At that moment, the LDA is linked to the dataset, and the ultimate principal WQI dynamics have been determined. Subsequently after predicting the WQI, Light Gradient Boosted Machine (LGBM) classification is performed in the LDA. Lastly, the LGBM classifier is activated to label the water quality. This proposed LGBM with LDA technique is demonstrated and evaluated on a Gulshan Lake-related dataset. The results show 96% forecast accuracy for the LDA and 100% categorization accuracy for the Light Gradient Boosted Machine classifier system that indicate consistent interpretation linked over the futuristic prototypes. This innovative model LDA-LGBM is aimed at enhancing the prediction of water quality and its classification through AI - ML approach.

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“…On a dataset connected to Gulshan Lake, the suggested LGBM with LDA approach is shown and assessed. The results reveal 100% classification accuracy for the Light Gradient Boosted Machine classifier system and 96% prediction accuracy for the LDA, which indicates consistent interpretation connected over the futuristic models [15].…”