Predicting Aquaculture Water Quality Using Machine Learning Approaches
Abstract
:1. Introduction
2. Material and Methods
2.1. Selection of Water Quality Prediction Model
2.1.1. Back Propagation Neuron Network (BPNN)
2.1.2. Radial Basis Function Neuron Network (RBFNN)
2.1.3. Support Vector Regression Machine (SVM)
2.1.4. Least Squares Support Vector Machine (LSSVM)
2.2. Simulation and Prediction by Using the Empirical Data
2.2.1. Data Sources
2.2.2. Algorithm Implementation
2.2.3. Metric Evaluation Models
2.2.4. Sensitivity Analysis
3. Results and Discussion
3.1. Model Screening for Predicting Water Quality
3.2. Simulation and Prediction by Using Support Vector Machine
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Data | Measurement Methods |
---|---|
DO | DO sensor |
pH | pH meter |
NH3-N | Nessler’s reagent spectrophotometry |
NO3-N | Ultraviolet spectrophotometric method |
NO2-N | 1,2-diaminoethane dihydrochioride spectrophotometry |
Published Data | Aquaculture Water Quality Data in Industrial Aquaculture Systems | ||||||
---|---|---|---|---|---|---|---|
Water Quality Parameter | Model | Result | Water Quality Parameter | Model | Result | ||
MSE | R2 | MSE | R2 | ||||
DO | BPNN | 0.092 | 0.60 | DO | SVM | 0.001 | 0.99 |
RBFNN | 0.002 | 0.99 | |||||
SVM | 0.003 | 0.99 | |||||
LSSVM | 0.004 | 0.99 | |||||
pH | BPNN | 0.053 | 0.56 | pH | SVM | 0.0002 | 0.99 |
RBFNN | 0.002 | 0.84 | |||||
SVM | 0.002 | 0.99 | |||||
LSSVM | 0.052 | 0.57 | |||||
NH3-N | BPNN | 0.055 | 0.28 | NH3-N | SVM | 0.001 | 0.99 |
RBFNN | 0.001 | 0.88 | |||||
SVM | 0.004 | 0.99 | |||||
LSSVM | 0.056 | 0.25 | |||||
NO3-N | BPNN | 0.017 | 0.96 | NO3-N | SVM | 0.003 | 0.99 |
RBFNN | 0.002 | 0.87 | |||||
SVM | 0.006 | 0.99 | |||||
LSSVM | 0.031 | 0.87 | |||||
NO2-N | BPNN | 0.002 | 0.87 | NO2-N | SVM | 0.006 | 0.99 |
RBFNN | 0.351 | 0.08 | |||||
SVM | 0.001 | 0.99 | |||||
LSSVM | 0.064 | 0.75 |
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Li, T.; Lu, J.; Wu, J.; Zhang, Z.; Chen, L. Predicting Aquaculture Water Quality Using Machine Learning Approaches. Water 2022, 14, 2836. https://doi.org/10.3390/w14182836
Li T, Lu J, Wu J, Zhang Z, Chen L. Predicting Aquaculture Water Quality Using Machine Learning Approaches. Water. 2022; 14(18):2836. https://doi.org/10.3390/w14182836
Chicago/Turabian StyleLi, Tingting, Jian Lu, Jun Wu, Zhenhua Zhang, and Liwei Chen. 2022. "Predicting Aquaculture Water Quality Using Machine Learning Approaches" Water 14, no. 18: 2836. https://doi.org/10.3390/w14182836
APA StyleLi, T., Lu, J., Wu, J., Zhang, Z., & Chen, L. (2022). Predicting Aquaculture Water Quality Using Machine Learning Approaches. Water, 14(18), 2836. https://doi.org/10.3390/w14182836