Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration
<p>Location of the Marvdash watershed in Fars province, Iran.</p> "> Figure 2
<p>Methodological flow chart.</p> "> Figure 3
<p>Groundwater vulnerability to NO<sub>3</sub> factors: (<b>a</b>), elevation; (<b>b</b>), slope; (<b>c</b>), plan curvature; (<b>d</b>), profile curvature; (<b>e</b>), rainfall; (<b>f</b>), piezometric depth; (<b>g</b>), distance from the river; (<b>h</b>), distance from residential; (<b>i</b>), Sodium (Na); (<b>j</b>), Potassium (K); (<b>k</b>), TWI.</p> "> Figure 3 Cont.
<p>Groundwater vulnerability to NO<sub>3</sub> factors: (<b>a</b>), elevation; (<b>b</b>), slope; (<b>c</b>), plan curvature; (<b>d</b>), profile curvature; (<b>e</b>), rainfall; (<b>f</b>), piezometric depth; (<b>g</b>), distance from the river; (<b>h</b>), distance from residential; (<b>i</b>), Sodium (Na); (<b>j</b>), Potassium (K); (<b>k</b>), TWI.</p> "> Figure 4
<p>Exploratory data analyses.</p> "> Figure 5
<p>Correlation analyses parameters based on Spearman.</p> "> Figure 6
<p>Scatter plot Bayesian ANN, SVM, cubist, and RF models for groundwater nitrate concentration in the validation stage.</p> "> Figure 7
<p>Result of Bayesian ANN, SVM, cubist, and RF models for groundwater nitrate concentration in the validation stage.</p> "> Figure 8
<p>Taylor diagram of observed and simulated groundwater nitrate concentration susceptibility values by Cubist, SVM, RF, and Bayesian ANN models.</p> "> Figure 9
<p>Spatial groundwater nitrate concentration susceptibility using (<b>a</b>) Cubist, (<b>b</b>) SVM, (<b>c</b>) RF, and (<b>d</b>) Bayesian ANN models.</p> "> Figure 9 Cont.
<p>Spatial groundwater nitrate concentration susceptibility using (<b>a</b>) Cubist, (<b>b</b>) SVM, (<b>c</b>) RF, and (<b>d</b>) Bayesian ANN models.</p> "> Figure 10
<p>NO<sub>3</sub> partial dependence plot for importance variable: (<b>a</b>) Altitude and Rainfall, (<b>b</b>) K, and Na.</p> "> Figure 10 Cont.
<p>NO<sub>3</sub> partial dependence plot for importance variable: (<b>a</b>) Altitude and Rainfall, (<b>b</b>) K, and Na.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Description of the Study Area
2.2. Methodology
2.3. Dataset Preparation
2.4. VIF
2.5. Machine Learning Methods
2.5.1. Cubist
2.5.2. Support Vector Machine (SVM)
2.5.3. Random Forest (RF)
2.5.4. Bayesian Artificial Neural Network (Bayesian ANN)
2.6. Validation and Accuracy Assessment
3. Results
3.1. Exploratory Data Analysis and Data Statistic Analysis
3.2. Correlation Analysis
3.3. Multi-Collinearity Analysis
3.4. Validation of the Models
3.5. Spatial Groundwater Nitrate Susceptibility
3.6. Importance Value
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Number of Wells | Mean | Minimum | Maximum | Standard Deviation |
---|---|---|---|---|
67 | 20.029 | 2.23 | 56.74 | 15.50 |
Variables | Train Data | Test Data | ||||||
---|---|---|---|---|---|---|---|---|
Mean | SD | Min | Max | Mean | SD | Min | Max | |
Altitude (m) | 1616.13 | 33.28 | 1568.00 | 1694.00 | 1615.15 | 26.36 | 1567.00 | 1663.00 |
K (mg/lit) | 0.03 | 0.04 | 0.01 | 0.11 | 0.02 | 0.03 | 0.01 | 0.10 |
Na (mg/lit) | 0.44 | 0.29 | 0.10 | 1.30 | 0.39 | 0.25 | 0.10 | 1.10 |
Plan curvature | −0.03 | 0.30 | −1.13 | 0.64 | −0.02 | 0.35 | −0.85 | 0.82 |
Profile curvature | 0.05 | 0.31 | −0.58 | 1.09 | −0.06 | 0.31 | −0.73 | 0.56 |
Pizometric depth (m) | 55.04 | 33.95 | 12.58 | 171.04 | 57.72 | 29.31 | 6.84 | 110.34 |
Rainfall (mm) | 381.97 | 81.26 | 254.10 | 503.02 | 383.03 | 73.45 | 267.03 | 498.84 |
Distance from residential (m) | 1025.03 | 710.90 | 30.00 | 3777.74 | 956.73 | 675.58 | 42.43 | 3606.24 |
Distance from river (m) | 1568.35 | 1399.89 | 0.00 | 5193.12 | 1559.50 | 1555.95 | 84.85 | 5730.08 |
Slope (%) | 6.36 | 4.27 | 1.32 | 21.29 | 6.91 | 4.92 | 1.32 | 18.49 |
TWI | 6.90 | 2.40 | 3.84 | 15.64 | 6.95 | 1.30 | 4.75 | 9.69 |
NO3 (mg/lit) | 20.99 | 16.62 | 2.23 | 56.74 | 18.23 | 12.23 | 4.82 | 49.83 |
Row | Variables | VIF | Tolerance |
---|---|---|---|
1 | Altitude | 3.72 | 0.27 |
2 | Slope | 1.12 | 0.89 |
3 | Plan curvature | 1.95 | 0.51 |
4 | Profile curvature | 2.01 | 0.49 |
5 | Rainfall | 4.44 | 0.22 |
6 | Piezometric depth | 1.39 | 0.72 |
7 | Distance from residential | 1.18 | 0.84 |
8 | Distance from river | 1.22 | 0.82 |
9 | K | 2.58 | 0.39 |
10 | Na | 2.24 | 0.45 |
11 | TWI | 1.25 | 0.67 |
Models | Stage | Parameters | |||
---|---|---|---|---|---|
R2 | RMSE | MAE | NSE | ||
Cubist | Training | 0.96 | 3.52 | 2.52 | 0.95 |
Validation | 0.87 | 5.18 | 4.06 | 0.81 | |
SVM | Training | 0.94 | 4.24 | 2.73 | 0.94 |
Validation | 0.74 | 6.07 | 5.07 | 0.74 | |
RF | Training | 0.96 | 3.66 | 2.72 | 0.95 |
Validation | 0.89 | 4.24 | 3.55 | 0.87 | |
Bayesian ANN | Training | 0.88 | 5.89 | 4.56 | 0.88 |
Validation | 0.79 | 5.91 | 4.67 | 0.75 |
Row | Variables | Importance Value |
---|---|---|
1 | Altitude | 2.35 |
2 | Slope | 0.91 |
3 | Plan curvature | 0.74 |
4 | Profile curvature | 0.67 |
5 | Rainfall | 3.15 |
6 | Piezometric depth | 1.09 |
7 | Distance from residential | 0.86 |
8 | Distance from river | 0.98 |
9 | K | 6.09 |
10 | Na | 1.84 |
11 | TWI | 1.01 |
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Band, S.S.; Janizadeh, S.; Pal, S.C.; Chowdhuri, I.; Siabi, Z.; Norouzi, A.; Melesse, A.M.; Shokri, M.; Mosavi, A. Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration. Sensors 2020, 20, 5763. https://doi.org/10.3390/s20205763
Band SS, Janizadeh S, Pal SC, Chowdhuri I, Siabi Z, Norouzi A, Melesse AM, Shokri M, Mosavi A. Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration. Sensors. 2020; 20(20):5763. https://doi.org/10.3390/s20205763
Chicago/Turabian StyleBand, Shahab S., Saeid Janizadeh, Subodh Chandra Pal, Indrajit Chowdhuri, Zhaleh Siabi, Akbar Norouzi, Assefa M. Melesse, Manouchehr Shokri, and Amirhosein Mosavi. 2020. "Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration" Sensors 20, no. 20: 5763. https://doi.org/10.3390/s20205763