Assessing Agricultural Reuse Potential of Treated Wastewater: A Hybrid Machine Learning Approach
Abstract
1. Introduction
1.1. The Role of Wastewater Reuse in Sustainable Irrigation
1.2. Artificial Intelligence for Wastewater Quality Prediction and Reuse
2. Materials and Methods
2.1. Data Source: Biological Wastewater Treatment Plant
2.2. Scenarios Based on Wastewater Quality Parameters
2.3. Artificial Intelligence Algorithms
2.3.1. Artificial Neural Networks
2.3.2. Adaptive Neuro-Fuzzy Inference System (ANFIS)
2.4. Combined Artificial Intelligence Algorithms
2.5. The Reuse Potential of Treated Wastewater of K-WWTP in Agriculture
3. Results
3.1. Performance of ANNs
3.2. Performance of ANFIS
3.3. Fuzzy-Based Assessment of Treated Wastewater Suitability for Irrigation
3.4. Quantitative Reuse Potential of K-WWTP Discharge in Agriculture
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameters | Statistical Data | ||||||||
---|---|---|---|---|---|---|---|---|---|
Ranges | Mean | SD | Skewness | Kurtosis | 95.0 % CI | Units | |||
Inlet | pH | 6.19 | 8.74 | 7.42 | 0.35 | −0.36 | 2.71 | (7.37, 7.47) | - |
EC | 998.00 | 1615.00 | 1107.90 | 95.22 | 2.22 | 7.02 | (1094.3, 1121.5) | μS/cm | |
Salinity | 0.32 | 0.81 | 0.52 | 0.07 | 1.11 | 3.92 | (0.51, 0.53) | % | |
DO | 0.01 | 5.87 | 1.47 | 1.22 | 1.34 | 1.67 | (1.30, 1.64) | mg/L | |
COD | 114.00 | 368.00 | 227.50 | 57.22 | 0.59 | −0.20 | (219.3, 235.7) | mg/L | |
Total N | 21.40 | 169.00 | 52.57 | 25.37 | 1.33 | 2.18 | (48.94, 56.20) | mg/L | |
NH4 | 12.60 | 73.10 | 37.42 | 11.13 | 0.52 | 0.69 | (35.83, 39.01) | mg/L | |
NO3 | 0.30 | 3.39 | 0.97 | 0.66 | 1.44 | 1.32 | (0.88, 1.06) | mg/L | |
Total P | 1.77 | 14.40 | 5.99 | 2.32 | 1.08 | 1.42 | (5.66, 6.32) | mg/L | |
TSS | 10.00 | 232.00 | 82.77 | 46.98 | 1.04 | 0.56 | (76.05, 89.49) | mg/L | |
Outlet | pH | 6.17 | 9.52 | 7.38 | 0.36 | 1.11 | 9.39 | (7.33, 7.43) | - |
EC | 717.00 | 1143.00 | 887.97 | 81.18 | 0.68 | 0.40 | (876.37, 899.57) | μS/cm | |
Salinity | 0.35 | 0.71 | 0.46 | 0.06 | 1.16 | 2.63 | (0.45, 0.47) | % | |
COD | 14.20 | 125.00 | 57.09 | 20.47 | 0.56 | 0.54 | (54.16, 60.02) | mg/L | |
Total N | 4.22 | 89.90 | 24.45 | 19.20 | 1.65 | 1.90 | (21.71, 27.19) | mg/L | |
Total P | 0.28 | 8.95 | 2.24 | 2.11 | 1.35 | 1.03 | (1.94, 2.54) | mg/L | |
TSS | 1 | 72.00 | 19.06 | 14.91 | 1.41 | 1.66 | (16.93, 21.19) | mg/L |
Output Parameters | Input Parameters | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
pH | EC | Salinity | DO | COD | Total N | Total P | TSS | NH4 | NO3 | |
pH | 0.03815 | 0.00505 | 0.00447 | 0.01142 | 0.00812 | 0.01041 | 0.00172 | 0.01176 | 0.00368 | 0.00170 |
EC | 0.00355 | 0.00321 | 0.07357 | 0.00875 | 0.01042 | 0.01476 | 0.00077 | 0.00526 | 0.00772 | 0.01075 |
Salinity | 0.00290 | 0.05163 | 0.01089 | 0.00320 | 0.00914 | 0.01936 | 0.00083 | 0.00751 | 0.00517 | 0.00054 |
COD | 0.00916 | 0.02405 | 0.01131 | 0.00916 | 0.02725 | 0.01138 | 0.00079 | 0.01020 | 0.00132 | 0.00649 |
Total N | 0.02291 | 0.02600 | 0.02118 | 0.00824 | 0.00681 | 0.05131 | 0.01928 | 0.00810 | 0.00245 | 0.00012 |
Total P | 0.00810 | 0.02290 | 0.02601 | 0.00245 | 0.02118 | 0.05131 | 0.00681 | 0.01928 | 0.00824 | 0.00012 |
TSS | 0.01079 | 0.01240 | 0.00916 | 0.00202 | 0.02716 | 0.00931 | 0.00368 | 0.01573 | 0.00125 | 0.00073 |
Output | Scenarios | Input Parameters | Train (%) | Validation (%) | Test (%) | MSE | R2 |
---|---|---|---|---|---|---|---|
pH | 1 | pH, TSS, DO, Total N, COD, EC, Salinity, NH4, Total P, NO3 | 80 | 5 | 15 | 0.01279 | 0.89 |
9 | pH, TSS | 75 | 10 | 15 | 0.01145 | 0.83 | |
EC | 1 | Salinity, Total N, NO3, COD, DO, NH4, TSS, pH, EC, Total P | 80 | - | 20 | 31.28646 | 0.97 |
9 | Salinity, Total N | 80 | - | 20 | 107.30676 | 0.96 | |
Salinity | 1 | EC, Total N, Salinity, COD, TSS, NH4, DO, pH, Total P, NO3 | 80 | 5 | 15 | 0.00006 | 0.96 |
6 | Conductivity, Total N, Salinity, COD, TSS | 85 | - | 15 | 0.00007 | 0.94 | |
COD | 1 | COD, EC, Total N, Salinity, TSS, pH, DO, NO3, NH4, Total P | 80 | 5 | 15 | 40.14093 | 0.86 |
5 | COD, Conductivity, Total N, Salinity, TSS, pH | 80 | 5 | 15 | 56.55725 | 0.80 | |
Total N | 1 | Total N, EC, pH, Salinity, Total P, DO, TSS, COD, NH4, NO3 | 85 | - | 15 | 1.82925 | 0.96 |
3 | Total N, EC, pH, Salinity, Total P, DO, TSS, COD | 85 | - | 15 | 9.83923 | 0.91 | |
Total P | 1 | Total N, Salinity, EC, COD, TSS, NH4, pH, Total P, DO, NO3 | 80 | 5 | 15 | 0.32555 | 0.90 |
6 | Total N, Salinity, Conductivity, COD, TSS | 80 | 5 | 15 | 0.67912 | 0.74 | |
TSS | 1 | COD, TSS, EC, pH, Total N, Salinity, Total P, DO, NH4, NO3 | 75 | 10 | 15 | 5.25323 | 0.92 |
5 | COD, TSS, Conductivity, pH, Total N, Salinity | 75 | 10 | 15 | 33.69808 | 0.85 |
Input Parameter | Rank of Categorized Parameters | Training (%) | Testing (%) | MF Type | Number of MFs | Training RMSE | Testing RMSE | R2 | ||
---|---|---|---|---|---|---|---|---|---|---|
pH | 6< | ≤6–8≥ | >8 | 80 | 20 | Trapmf | 3 | 0 | 0.0003 | 0.99 |
0 | 1 | 0 | ||||||||
EC | <700 | ≤700–900≥ | 900> | 80 | 20 | Trapmf | 3 | 0.0005 | 0.0007 | 0.97 |
1 | 0.5 | 0 | ||||||||
Salinity | <0.7 | ≤0.7–1.5≥ | 1.5> | 80 | 20 | Trapmf | 3 | 0 | 0 | 1 |
1 | 0.5 | 0 | ||||||||
COD | <50 | ≤50–100≥ | 100> | 80 | 20 | Trapmf | 3 | 0 | 0 | 1 |
1 | 0.5 | 0 | ||||||||
Total N | <7.5 | ≤7.5–15≥ | 15> | 80 | 20 | Trapmf | 3 | 0.090 | 0.091 | 0.92 |
1 | 0.5 | 0 | ||||||||
Total P | <1 | ≤1–2≥ | 2> | 80 | 20 | Trapmf | 3 | 0.036 | 0.036 | 0.96 |
1 | 0.5 | 0 | ||||||||
TSS | <15 | ≤15–30≥ | 30> | 80 | 20 | Trapmf | 3 | 0.045 | 0.045 | 0.90 |
1 | 0.5 | 1 |
Crops | Area | Area of Centre | Production | Yield | Total ETc | Total Reff | Cropping Pattern | Growing Days | q | Irrigable Area |
---|---|---|---|---|---|---|---|---|---|---|
(ha) | (ha) | (t) | (t/ha) | (mm) | (mm) | (%) | (day) | (L/s/ha) | (ha) | |
Sunflower | 74,051 | 17,661.1 | 210,930 | 2.85 | 558 | 188.8 | 33.6 | 150 | 1.3784 | 145 |
Maize | 10,765 | 2431.5 | 503,525 | 46.77 | 717 | 231.8 | 4.9 | 160 | 1.8115 | 110 |
Canola | 3700 | 46.2 | 13,875 | 3.75 | 407 | 391.2 | 1.7 | 250 | 0.0589 | 3394 |
Rice | 2109 | 4.4 | 17,745 | 8.41 | 689 | 231.8 | 1.0 | 170 | 1.7069 | 117 |
Clover | 1725 | 500.0 | 33,182 | 19.24 | 777 | 277.4 | 0.8 | 210 | 1.8652 | 107 |
Sugar Beat | 1412 | 52.0 | 67,334 | 47.68 | 736 | 296.6 | 0.6 | 190 | 1.6405 | 122 |
Oat | 787 | 34.4 | 2754 | 3.50 | 524 | 189 | 0.4 | 260 | 1.2507 | 160 |
Triticale | 982 | 180.0 | 4041 | 4.12 | 513 | 493 | 0.4 | 270 | 0.0746 | 2681 |
Chickpea | 88 | 8.4 | 119 | 1.35 | 357 | 189 | 0.1 | 120 | 0.6272 | 319 |
Potato | 75 | 7.7 | 1532 | 20.32 | 555 | 188.8 | 0.1 | 150 | 1.3672 | 146 |
Total | 95,694 | 20,925.7 | 11.7811 | 7301 |
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Köksal, D.D.; Ahi, Y.; Todorovic, M. Assessing Agricultural Reuse Potential of Treated Wastewater: A Hybrid Machine Learning Approach. Agronomy 2025, 15, 703. https://doi.org/10.3390/agronomy15030703
Köksal DD, Ahi Y, Todorovic M. Assessing Agricultural Reuse Potential of Treated Wastewater: A Hybrid Machine Learning Approach. Agronomy. 2025; 15(3):703. https://doi.org/10.3390/agronomy15030703
Chicago/Turabian StyleKöksal, Daniyal Durmuş, Yeşim Ahi, and Mladen Todorovic. 2025. "Assessing Agricultural Reuse Potential of Treated Wastewater: A Hybrid Machine Learning Approach" Agronomy 15, no. 3: 703. https://doi.org/10.3390/agronomy15030703
APA StyleKöksal, D. D., Ahi, Y., & Todorovic, M. (2025). Assessing Agricultural Reuse Potential of Treated Wastewater: A Hybrid Machine Learning Approach. Agronomy, 15(3), 703. https://doi.org/10.3390/agronomy15030703