Transfer Learning Artificial Neural Network-based Ensemble Voting of Water Quality Classification for Different Types of Farming
Received: 16 May 2024 | Revised: 25 May 2024 | Accepted: 1 June 2024 | Online: 5 June 2024
Corresponding author: Lap Poomhiran
Abstract
This study aims to develop a model for characterizing water quality in seawater-influenced areas for salt farming, fish farming, and crop farming. The water quality classification model was based on transfer learning trained by the Multi-Layer Perceptron Neural Network (MLPNN) and then classified by conventional Machine Learning (ML) methods, such as Decision Tree (DT), K-Nearest Neighbors (KNN), Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM). The results of each ML classification were ensemble voted together, comparing the efficiency between hard and soft voting. The collected imbalanced dataset had a difference ratio between the majority and minority classes of 1:0.0138. However, after 900% resampling by applying the k-mean SMOTE technique, the data ratio between the majority and minority classes was 1:0.9778. The results show that the proposed ensemble approach improved accuracy by up to 2.15% in classifying water quality for salt farming, fish farming, and crop farming in seawater-influenced areas.
Keywords:
water quality classification, artificial neural networks, transfer learning, ensemble votingDownloads
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Copyright (c) 2024 Sumitra Nuanmeesri, Chaisri Tharasawatpipat, Lap Poomhiran
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