Optimized Scenario for Estimating Suspended Sediment Yield Using an Artificial Neural Network Coupled with a Genetic Algorithm
Abstract
:1. Introduction
2. Study Area
3. Methodology and Data Used Description
4. Results and Discussion
4.1. T-Test of Data and Spatial Variation of Data
4.2. Hybrid ANN-GA Model for Estimation of SSY
4.3. Comparisons among ANN-GA, SRC, MLR and ANN Models
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data Set | t-Test | Water-Discharge | Rainfall | Temperature | Suspended Sediment Yield |
---|---|---|---|---|---|
Training and testing | p | 0.9013 | 0.5932 | 0.2339 | 0.2392 |
t | 0.1230 | 0.5328 | 1.1632 | −131.2 | |
Training and validation | p | 0.9630 | 0.8957 | 0.2863 | 0.0855 |
t | −0.0352 | −0.1311 | 1.0663 | 1.7205 | |
Validation and testing | p | 0.8838 | 0.5935 | 0.9259 | 0.5603 |
t | 0.1339 | 0.5326 | 0.0930 | −0.5827 |
Stations | Q-SSY(r1) | RF-SSY(r2) | T-SSY(r3) |
---|---|---|---|
Tikarapara | 0.9323 | 0.5787 | 0.1499 |
Simga | 0.8528 | 0.5736 | −0.0865 |
Andhiyarakhore | 0.8218 | 0.5847 | 0.1866 |
Sundargarh | 0.8913 | 0.7917 | 0.1459 |
Bamnidih | 0.7924 | 0.4963 | 0.1082 |
Jondhara | 0.8873 | 0.5711 | 0.1437 |
Kantamal | 0.8492 | 0.6643 | 0.1038 |
Kurubhata | 0.9031 | 0.7866 | 0.1734 |
Basantpur | 0.8935 | 0.6941 | 0.1516 |
Baronda | 0.8224 | 0.6467 | 0.0677 |
Rajim | 0.8413 | 0.6377 | 0.0624 |
ANN-GA | RMSE | MSE | MAE | Variance | r | Coefficient of Eficiency |
---|---|---|---|---|---|---|
Training | 0.0048 | 2.390 × 10−05 | 0.002 | 2.390 × 10−05 | 0.972 | 0.956 |
Validation | 0.014 | 2.000 × 10−04 | 0.004 | 1.000 × 10−03 | 0.752 | −0.081 |
Testing | 0.009 | 7.660 × 10−05 | 0.003 | 7.550 × 10−05 | 0.871 | 0.667 |
Tikarapara | 0.007 | 5.260 × 10−05 | 0.006 | 5.530 × 10−05 | 0.980 | 0.936 |
Simga | 0.008 | 5.890 × 10−05 | 0.001 | 5.820 × 10−06 | 0.921 | 0.251 |
Andhiyakore | 0.001 | 8.550 × 10−07 | 0.001 | 2.830 × 10−07 | 0.688 | −18.810 |
Sundargarh | 0.004 | 1.710 × 10−05 | 0.002 | 1.750 × 10−05 | 0.721 | 0.558 |
Bamnidih | 0.005 | 2.090 × 10−05 | 0.003 | 1.900 × 10−05 | 0.906 | −259 |
Jondhara | 0.005 | 2.950 × 10−05 | 0.003 | 2.920 × 10−05 | 0.830 | 0.627 |
Kantamal | 0.032 | 1.000 × 10−03 | 0.013 | 7.000 × 10−04 | 0.778 | 0.259 |
Kurubhata | 0.004 | 1.900 × 10−05 | 0.003 | 1.630 × 10−05 | 0.732 | −2.201 |
Basantpur | 0.007 | 5.120 × 10−05 | 0.005 | 3.970 × 10−05 | 0.917 | 0.772 |
Baronda | 0.001 | 1.770 × 10−06 | 0.001 | 1.220 × 10−06 | 0.890 | 0.555 |
Rajim | 0.003 | 6.330 × 10−06 | 0.002 | 6.550 × 10−06 | 0.752 | −0.356 |
Gauge Station | Time Index of Peaks | Q (m3/s) | RF (mm) | Sediment Yield (Tons/Month) | T (°C) |
---|---|---|---|---|---|
Sundargarh | 2 | 7756 | 351 | 582,190 | 27 |
4 | 11,857 | 242 | 732,762 | 29.5 | |
15 | 11,239 | 488 | 970,420 | 27.5 | |
26 | 8164 | 591 | 863,698 | 28.5 | |
29 | 6488 | 169 | 1,386,340 | 26 | |
Kurubhata | 2 | 4551 | 406 | 403,901 | 29.5 |
4 | 6463 | 207 | 371,259 | 31 | |
15 | 9845 | 529 | 578,737 | 29 | |
26 | 3130 | 566 | 90,388 | 26.5 | |
Jondhra | 2 | 42,770 | 404 | 2,225,625 | 30.5 |
16 | 19,207 | 152 | 1,660,837 | 30.75 | |
26 | 25,906 | 508 | 1,226,185 | 30.75 | |
Baronda | 3 | 12,363 | 245 | 550,873 | 29.25 |
15 | 13,228 | 267 | 330,656 | 28.75 | |
26 | 2668 | 592 | 57,911 | 29.25 | |
Baminidhi | 3 | 6138 | 428 | 53,056 | 29.5 |
15 | 5877 | 441 | 40,044 | 29 | |
26 | 3256 | 636 | 58,940 | 34 | |
Andhiyarakhore | 4 | 1244 | 233 | 67,731 | 28.5 |
15 | 325 | 139 | 8615 | 28 | |
26 | 220 | 334 | 19,228 | 31.5 |
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Yadav, A.; Hasan, M.K.; Joshi, D.; Kumar, V.; Aman, A.H.M.; Alhumyani, H.; Alzaidi, M.S.; Mishra, H. Optimized Scenario for Estimating Suspended Sediment Yield Using an Artificial Neural Network Coupled with a Genetic Algorithm. Water 2022, 14, 2815. https://doi.org/10.3390/w14182815
Yadav A, Hasan MK, Joshi D, Kumar V, Aman AHM, Alhumyani H, Alzaidi MS, Mishra H. Optimized Scenario for Estimating Suspended Sediment Yield Using an Artificial Neural Network Coupled with a Genetic Algorithm. Water. 2022; 14(18):2815. https://doi.org/10.3390/w14182815
Chicago/Turabian StyleYadav, Arvind, Mohammad Kamrul Hasan, Devendra Joshi, Vinod Kumar, Azana Hafizah Mohd Aman, Hesham Alhumyani, Mohammed S. Alzaidi, and Haripriya Mishra. 2022. "Optimized Scenario for Estimating Suspended Sediment Yield Using an Artificial Neural Network Coupled with a Genetic Algorithm" Water 14, no. 18: 2815. https://doi.org/10.3390/w14182815