Surface water quality classification and prediction model based on multiple machine learning algorithms.

ABSTRACT: In the context of worsening global water scarcity and prominent water pollution issues, precise water quality assessment and prediction are crucial for water resource management. Addressing the shortcomings of traditional methods in multi-parameter collaborative analysis, class imbalance handling, and nonlinear fitting, this study focuses on Heilongjiang Province, China, to construct a multi-parameter watershed water quality level prediction system comprising "data preprocessing-feature dimensionality reduction-Model Optimization" multi-parameter watershed water quality level prediction system, combining Principal Component Analysis (PCA), C4.5 decision tree, Backpropagation (BP) neural network, Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) networks for the study. T

SUBMITTER: Man G 

PROVIDER: S-EPMC12618629 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature