Comprehensive water quality evaluation based on kernel extreme learning machine optimized with the sparrow search algorithm in Luoyang River Basin, China

Water quality evaluation is crucial to water environmental quality management. Due to the low efficiency and rationality of the traditional automatic monitoring in water quality evaluation, a comprehensive water quality evaluation model based on kernel extreme learning machine (KELM) was proposed to...

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Vydané v:Environmental earth sciences Ročník 80; číslo 16; s. 521
Hlavní autori: Song, Chenguang, Yao, Leihua, Hua, Chengya, Ni, Qihang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2021
Springer Nature B.V
Predmet:
Algorithms
Artificial neural networks
Back propagation
Back propagation networks
basins
Biogeosciences
China
Earth and Environmental Science
Earth Sciences
Environmental management
Environmental protection
Environmental quality
Environmental Science and Engineering
Evaluation
Geochemistry
Geology
Heuristic methods
Hydrology/Water Resources
Kernels
Learning algorithms
Machine learning
Management planning
Mathematical models
model validation
Neural networks
Nonlinear systems
Nonlinearity
Optimization
Original Article
Parameters
Passeriformes
Performance enhancement
Quality assessment
Quality management
regression analysis
River basins
Rivers
Search algorithms
Support vector machines
Terrestrial Pollution
Water quality
watersheds
China
SSA
KELM
Water quality evaluation
Optimization algorithm
Hybrid model
ISSN:1866-6280, 1866-6299
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Shrnutí:Water quality evaluation is crucial to water environmental quality management. Due to the low efficiency and rationality of the traditional automatic monitoring in water quality evaluation, a comprehensive water quality evaluation model based on kernel extreme learning machine (KELM) was proposed to improve the performance of the model in Luoyang River Basin, China. Besides, a novel metaheuristic optimization algorithm, sparrow search algorithm (SSA), was implemented to compute the optimal parameter values for the KELM model. Extreme learning machine (ELM), KELM, support vector regression (SVR), and backpropagation neural network (BPNN) were considered as the benchmark models to validate the proposed hybrid model. Results showed that the water quality evaluation model based on KELM optimized with the SSA (SSA-KELM) outperformed other models. The proposed hybrid model can successfully overcome the nonstationarity, randomness, and nonlinearity of the water quality parameters data with a simple structure, fast learning speed, and good generalization performance, which is worthy of promotion and application. The research results can objectively and accurately determine the status of basin water quality and provide a scientific basis for basin water environment protection and management planning.
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ISSN:1866-6280
1866-6299
DOI:10.1007/s12665-021-09879-x