Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River DOI Creative Commons
Manqi Wang, Chunyi Zhou, Jiaqi Shi

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(1), P. 119 - 119

Published: Jan. 2, 2025

The continuous and effective monitoring of the water quality small rural rivers is crucial for sustainable development. In this work, machine learning models were established to predict a typical river based on quantity measured data UAV hyperspectral images. Firstly, spectral preprocessed using fractional order derivation (FOD), standard normal variate (SNV), normalization (Norm) enhance response characteristics parameters. Second, method combining Pearson’s correlation coefficient variance inflation factor (PCC–VIF) was utilized decrease dimensionality features improve input data. Again, screened features, back-propagation neural network (BPNN) model optimized mixture genetic algorithm (GA) particle swarm optimization (PSO) as means estimating parameter concentrations. To intuitively evaluate performance hybrid algorithm, its prediction accuracy compared with that conventional algorithms (Random Forest, CatBoost, XGBoost, BPNN, GA–BPNN PSO–BPNN). results show GA–PSO–BPNN turbidity (TUB), ammonia nitrogen (NH3-N), total (TN), phosphorus (TP) exhibited optimal coefficients determination (R2) 0.770, 0.804, 0.754, 0.808, respectively. Meanwhile, also demonstrated good robustness generalization ability from different periods. addition, we used visualize parameters in study area. This work provides new approach refined rivers.

Language: Английский

Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River DOI Creative Commons
Manqi Wang, Chunyi Zhou, Jiaqi Shi

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(1), P. 119 - 119

Published: Jan. 2, 2025

The continuous and effective monitoring of the water quality small rural rivers is crucial for sustainable development. In this work, machine learning models were established to predict a typical river based on quantity measured data UAV hyperspectral images. Firstly, spectral preprocessed using fractional order derivation (FOD), standard normal variate (SNV), normalization (Norm) enhance response characteristics parameters. Second, method combining Pearson’s correlation coefficient variance inflation factor (PCC–VIF) was utilized decrease dimensionality features improve input data. Again, screened features, back-propagation neural network (BPNN) model optimized mixture genetic algorithm (GA) particle swarm optimization (PSO) as means estimating parameter concentrations. To intuitively evaluate performance hybrid algorithm, its prediction accuracy compared with that conventional algorithms (Random Forest, CatBoost, XGBoost, BPNN, GA–BPNN PSO–BPNN). results show GA–PSO–BPNN turbidity (TUB), ammonia nitrogen (NH3-N), total (TN), phosphorus (TP) exhibited optimal coefficients determination (R2) 0.770, 0.804, 0.754, 0.808, respectively. Meanwhile, also demonstrated good robustness generalization ability from different periods. addition, we used visualize parameters in study area. This work provides new approach refined rivers.

Language: Английский

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