Application and Comparison of Different Models for Quantifying the Aquatic Community in a Dam-Controlled River
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data Collection
2.2. Methodology
2.2.1. Output Variables
2.2.2. Input Variables
2.2.3. MLE Model Construction
2.2.4. GA-BP Model Construction
2.2.5. Statistical Analyses
3. Results
3.1. Model Structure and Parameter Setting
3.1.1. MLE Model
3.1.2. GA-BP Model
3.2. Model Calibration and Validation
3.2.1. MLE Model
3.2.2. GA-BP Model
3.3. Model Comparison
4. Discussion
4.1. Variability of Habitat Quality
4.2. Aquatic Community Characteristics
4.3. Model Constraints
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Input Variables | Output Variables |
---|---|
Q, DO, TP, and TN | fP-SWI |
DO, Q, and TN | fZ-SWI |
DO, Q, and CODMn | fB-SWI |
Model | R2 | Significance Test | |
---|---|---|---|
F | p | ||
fP-SWI | 0.561 | 10.538 | 0.000 * |
fZ-SWI | 0.678 | 23.903 | 0.000 * |
fB-SWI | 0.621 | 19.155 | 0.000 * |
Model | Function Item | Parameter | Significance Test | ||
---|---|---|---|---|---|
Name | Value | t | p | ||
fP-SWI | constant | αp | 2.1809 | 4.725 | 0.000 * |
xDO | βp1 | 0.1228 | 3.317 | 0.002 * | |
xQ | βp2 | 0.0043 | 1.421 | 0.165 | |
xTP | βp3 | −0.7810 | −0.927 | 0.361 | |
xTN | βp4 | −0.0598 | −1.293 | 0.205 | |
fZ-SWI | constant | αz | −0.0553 | −0.1650 | 0.870 |
xDO | βz1 | 0.1894 | 7.0842 | 0.000 * | |
xQ | βz2 | 0.0062 | 2.6388 | 0.013 | |
xTP | βz3 | −0.0048 | −0.1433 | 0.887 | |
fB-SWI | constant | αb | 0.5772 | 1.0376 | 0.307 |
xDO | βb1 | 0.1275 | 3.2575 | 0.003 * | |
xQ | βb2 | 0.0138 | 4.3158 | 0.000 * | |
xCODMn | βb3 | −0.2294 | −2.8501 | 0.007 * |
Model | GA-BP | |||
---|---|---|---|---|
P-SWI | Z-SWI | B-SWI | ||
variables | input | DO, Q, TN, TP | Q, DO, TN | Q, DO, CODMn |
output | fP-SWI | fZ-SWI | fB-SWI | |
layer nodes | input layer neurons, m1 | 4 | 3 | 3 |
hidden layer neurons, m2 | 9 | 3 | 4 | |
output layer neurons, m3 | 1 | 1 | 1 | |
parameters | transfer function | tansig and purelin | ||
training function | trainlm | |||
learning rate, v | 0.1 | 0.1 | 0.1 | |
training times, epochs | 100 | 100 | 50 | |
population, Mp | 30 | 20 | 20 | |
iteration times, Ts | 80 | 100 | 50 | |
crossover probability, pc | 0.3 | 0.3 | 0.3 | |
mutation probability, pm | 0.1 | 0.1 | 0.1 |
Model | The Mean Value of Outputs | Calibration Stage | Validation Stage | |||||
---|---|---|---|---|---|---|---|---|
fP-SWI | fZ-SWI | fB-SWI | fP-SWI | fZ-SWI | fB-SWI | |||
MLE | Simulation | 3.144 | 1.923 | 1.442 | 3.241 | 2.151 | 1.377 | |
Observation | 3.055 | 1.916 | 1.440 | 2.880 | 1.881 | 1.586 | ||
t-test (2-tailed) | H0 | mean simulation = mean observation | ||||||
Z-score | 0.55 | 0.10 | −0.22 | 1.59 | 0.99 | −0.19 | ||
results | no significant difference at the 0.05 level | |||||||
GA-BP | Simulation | 3.082 | 1.908 | 1.442 | 2.971 | 2.195 | 1.377 | |
Observation | 3.055 | 1.916 | 1.440 | 2.880 | 1.881 | 1.586 | ||
t-test (2-tailed) | H0 | mean simulation = mean observation | ||||||
Z-score | 0.14 | −0.05 | 0.01 | 0.29 | 1.26 | −0.58 | ||
results | no significant difference at the 0.05 level |
Model | Model Equation/Structure | Calibration | Validation | ||||
---|---|---|---|---|---|---|---|
MRE | MAE | R2 | MRE | MAE | R2 | ||
GA-BP | 1.6% | 0.0338 | 0.990 | 20% | 0.4833 | 0.332 | |
13% | 0.3075 | 0.834 | 28.2% | 0.4217 | 0.320 | ||
18.5% | 0.2862 | 0.801 | 28.1% | 0.4523 | 0.479 | ||
MLE | 20.7% | 0.4323 | 0.561 | 22.5% | 0.4979 | 0.072 | |
17% | 0.3551 | 0.678 | 33.7% | 0.5394 | 0.260 | ||
41.5% | 0.5246 | 0.621 | 37.5% | 0.5985 | 0.356 | ||
MNLE | 9.1% | 0.2309 | 0.811 | 22% | 0.5136 | 0.357 | |
15.1% | 0.3443 | 0.723 | 34.5% | 0.4587 | 0.305 | ||
42% | 0.4262 | 0.657 | 29.5% | 0.5179 | 0.407 |
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Liu, J.; Zang, C.; Zuo, Q.; Han, C.; Krause, S. Application and Comparison of Different Models for Quantifying the Aquatic Community in a Dam-Controlled River. Int. J. Environ. Res. Public Health 2023, 20, 4148. https://doi.org/10.3390/ijerph20054148
Liu J, Zang C, Zuo Q, Han C, Krause S. Application and Comparison of Different Models for Quantifying the Aquatic Community in a Dam-Controlled River. International Journal of Environmental Research and Public Health. 2023; 20(5):4148. https://doi.org/10.3390/ijerph20054148
Chicago/Turabian StyleLiu, Jing, Chao Zang, Qiting Zuo, Chunhui Han, and Stefan Krause. 2023. "Application and Comparison of Different Models for Quantifying the Aquatic Community in a Dam-Controlled River" International Journal of Environmental Research and Public Health 20, no. 5: 4148. https://doi.org/10.3390/ijerph20054148