رویکردهای نوین در مهندسی آب و محیط‌زیست
نشریه علمی

Assessing Soft Computing Techniques for River Suspended Sediment Estimation

نوع مقاله : مقاله پژوهشی

نویسندگان

Amir Moradinejad 1
abbas parsaie 2
Seyed Ahmad Hosseini 3
Mahmoudreza Tabatabaei 4

1 Soil Conservation and Watershed Management Research Department, Markazi Agricultural and Natural Resources Research and Education Center, Arak, Agricultural Research Education & Extention Organization (AREEO). Arak, Iran.

2 Department of Hydraulic Structures, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

3 Department of River and Coastal Engineering, Soil Conservation and Watershed Management Institute, Agricultural Research Education & Extention Organization (AREEO), Tehran, Iran.

4 Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.

https://doi.org/10.22034/nawee.2025.514714.1147
چکیده
Objective: Statistical and regression models are commonly used for this purpose, but they often yield inaccurate results due to the linear assumptions inherent in these approaches. Hydraulic models, while useful, cannot always be fully trusted due to their requirement for extensive data, the potential unavailability of required data, and the possibility of human errors leading to inaccuracies in sediment simulation. To overcome these challenges, data-driven methods—specifically a subset of soft computing techniques—can be employed.
Material and Methods: This study aims to evaluate and compare five methods for estimating the sediment load at the Pol Doab station on the Qarachai River in Markazi Province, Iran. The methods considered include the Adaptive Network-based Fuzzy Inference System (ANFIS), Support Vector Machine (SVM), Gene Expression Programming (GEP), Multivariate Adaptive Regression Splines (MARS), and the Group Method of Data Handling (GMDH). The performance of these models in simulating the sediment load of rivers was assessed, and the results were compared both among the five methods and with those obtained from the sediment rating curve.
Results and Discussion: The statistical indicators showed the following results for each method at the station:
For the SVR model: R2=0.98R2 = 0.98R2=0.98, RMSE = 185, and MBE = -5.43. For the ANFIS model: R2=0.79R2 = 0.79R2=0.79, RMSE = 24.3, and MBE = 1.9. For the GEP model: R2=0.98R2 = 0.98R2=0.98, RMSE = 0.74, and MBE = 0.00047. In the next step, the best-performing patterns from the ANFIS, SVM, and GEP models were used as inputs for the GMDH model. The results indicated that the GMDH model demonstrated the highest performance, with R2=0.99, 0.91, 0.98 R2 = 0.99, 0.91, 0.98R2=0.99, 0.91, 0.98, RMSE = 83, 24, 73 (tons per day), and MBE = 3.2, 1.8, 1.2 respectively.
Conclusion: The findings suggest that the GEP model, with R2=0.98R2 = 0.98R2=0.98, RMSE = 0.74, and MBE = 0.00047, outperforms the SVM, ANFIS, and GMDH models to some extent. Moreover, the results demonstrate that all five data mining methods investigated in this study provide significantly better estimates than the traditional sediment rating curve.

کلیدواژه‌ها

suspended load
fuzzy neural network
sedimentation
gene expression programming
support vector machine. Gene
expression programming

عنوان مقاله English

Assessing Soft Computing Techniques for River Suspended Sediment Estimation

نویسندگان English

Amir Moradinejad 1
abbas parsaie 2
Seyed Ahmad Hosseini 3
Mahmoudreza Tabatabaei 4
1 Soil Conservation and Watershed Management Research Department, Markazi Agricultural and Natural Resources Research and Education Center, Arak, Agricultural Research Education & Extention Organization (AREEO). Arak, Iran.
2 Department of Hydraulic Structures, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
3 Department of River and Coastal Engineering, Soil Conservation and Watershed Management Institute, Agricultural Research Education & Extention Organization (AREEO), Tehran, Iran.
4 Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.
چکیده English

Sediment load along with river flow causes irreparable damage to water development projects. Estimation of river sediment load is an important and practical issue in the study and design of water and hydraulic projects. The purpose of this research is to evaluate and compare adaptive neural-fuzzy models (ANFIS), (SVM), (GEP), (GMDH) and (MARS) and compare with the (SRC) method in estimating sediment load of Pol Doab station of Qarachay River, Markazi Province. For this purpose, the performance of 5 types of data mining models in simulating river sediment load was investigated, then the results of the 5 methods were compared with each other and with the results of the scale curve method. The results indicate the acceptable performance of data mining models compared to the scale curve. The results also showed that the GEP model with R2=0.98, RMSE=0.74 and MBE=0.00047 has better performance than the SVM, ANFIS, MARS and GMDH models. The SRC method had the lowest R square value 0.61 and average RMSE 75 and MBE 20. In general, all five data mining methods showed better performance than the SRC sediment ranking curve.

کلیدواژه‌ها English

suspended load
fuzzy neural network
sedimentation
gene expression programming
support vector machine. Gene
expression programming
Abraham, A., Steinberg, D., Philip, N. S. 2001. Rainfall forecasting using soft computing models
and multivariate adaptive regression splines IEEE SMC Transactions, Special Issue on Fusion of Soft Computing and Hard.
Adamowski, J., Chan, H. F., Prasher, S. O., Sharda, V.N. 2012. Comparison of multivariate
adaptive regression splines with coupled wavelet transform artificial neural networks for runoff
forecasting in Himalayan micro-watersheds with limited data. Journal of Hydroinformatics 14(3): 731-744.
Adnan, R.M., Yaseen, Z.M., Heddam, S., Shahid, S., Sadeghi-Niaraki, A., Kisi, O. 2022. Predictability; performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy system model. International; Journal of Sediment Research. 37(10), 383–398. https://doi.org/10.1016/j.ijsrc.2021.10.001.
Alizamir, M., Kisi, O., Adnan, R.M., Kuriqi, A. 2020. Modeling reference evapotranspiration by combining neuro-fuzzy and evolutionary strategies. Acta; Geophys. 68, 1113–1126.
Aytek, A., Kisi, O. 2008. A Genetic Programming Approach to Suspended Sediment Modeling. Journal; of Hydrology, 351: 288-298.
Azamathulla, H.M., Caun, Y.C., Aminudin, A., Chang, C. K. 2013. Suspended; sediment load prediction of river systems: GEP; approach. Arabian; Journal of Geoscience, 6: 3469-480.
Biranvand, N., Sepahvand, A. and Haqizadeh, A. 2022. Modeling of suspended sediment using machine learning algorithms in periods of low and high water (case study: Kashkan watershed). Soil; and water modeling and management, (3)2: DOI: 10.22098/mmws.2022.11262.1115
Duan, W.L.; He, B.; Takara, K.; Luo, P.P., Nover, D., Hu, M.C. 2015. Modeling suspended sediment sources and transport in the Ishikari River basin, Japan, using SPARROW, Hydraulic Earth Systems Sciences, 19: 1293-1306.
Eder, A.P., Strauss, T., Krueger, B., 1and, J.N., Quinton, B. 2010. A Comparative calculation of suspended sediment loads with respect to hysteresis effects (in the Petzenkirchen catchment), Austria, Journal of Hydrology, 389: 168-176.
Emamgolizadeh s., Bateni S. M., Shahsavani D., Ashrafi T., Ghorban H. 2015. Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS) Journal of Hydrology, 529 (3), pp 1590-1600.
Ferreira C. 2001. Algorithms; for solving gene expression programming: a new adaptive problems. Complex; Systems, 13(2): 87-129.
Ikram, R.M.A., Mostafa, R.R., Chen, Z., Parmar, K.S., Kisi, O., Zounemat-Kermani, M. 2023. Water Temperature Prediction Using Improved Deep Learning Methods through Reptile Search Algorithm and Weighted Mean of Vectors Optimizer; J. Mar. Sci. Eng. 11, 259. https://doi.org/10.3390/jmse11020259.
Ivakhnenko, A.G. 1976. The Group Method of Data Handling in Prediction Problems, Soviet
Automatic Control of Avtomotika, 9: 21-30.
Keshtegar, B., Piri, J., Hussan, W.U., Ikram, K., Yaseen, M., Kisi, O., Adnan, R.M., Adnan, M., Waseem, M. 2023. Prediction of Sediment Yields Using a Data-Driven Radial M5 Tree Model. Journal; of Water 2023, 15, 1437. https://doi.org/10.3390/w15071437;
Kisi, O., Ozkan, C., Bahriye, A. 2012. Modeling; Discharge-Sediment Relationship Using Neural Networks with Artificial Bee Colony Algorithm, Journal of Hydrology, 428-429: 94-103.
Mehrizi Haeri, AA. 2012. Data mining: concepts, methods, and applications. Master's; thesis in economic and social statistics, Faculty of Economics, Allameh Tabatabai University.
Melesse, A. M., Ahmad, S., McClain, M. E., Wang, X. and Lim, Y.H. 2011. Suspended; sediment load prediction of river systems: an artificial neural network approach. Agriculture;
Water Management, 98: 855–866.
Moradinejad, A. 2024. Suspended load modeling of river using soft computing techniques. Water Resources Management. Advance online publication. https://doi.org/10.1007/s11269-023-03722-7.
Moradinejad, A., Khosrobeigi, S., Akbari, M., Hosseini, SA. 2024. Investigating soft calculation methods in
river suspended sediment estimation (Hassan Abad station of Tirah river). Water and Soil Management and Modeling, 4(2),
241-260. DOI: 10.22098/mmws.2023.12620.1258
Mostafa, R.R., Kisi, O., Adnan, R.M., Sadeghifar, T., Kuriqi, A. 2023. Modeling Potential Evapotranspiration by Improved Machine Learning Methods Using Limited Climatic Data. Water; 15, 486. https://doi.org/10.3390/w15030486;
Satari, M.T., Rezazadeh Jodi, A., Safdari, F., Kahramanzadeh, F. 2016. Performance evaluation of M5 tree model and support vector regression methods in river suspended sediment modeling. Protection; of water and soil resources, (1) 6, https://civilica.com/doc/1297623.
Tayfur, G. 2012. Soft; computing in water resources engineering: Artificial neural networks, fuzzy logic and genetic algorithms. WIT; Press; Dorset.
Walling, D. E., Webb, B. W. 1988. The reliability of rating curve estimates of suspended sediment yield: some further comments. Sediment; Budgets (Proceedings of the Porto Alegre Symposium), (174), 337–350.
Yeganeh-Bakhtiary, A., Eyvazoghli, H., Shabakhty, N., Kamranzad, B., Abolfathi, S. 2022. Machine; Learning as a Downscaling Approach for Prediction of Wind Characteristics under Future Climate Change Scenarios. Complexity; 13, 8451812. https://doi.org/10.1155/2022/8451812.Abolfathi, S., Yeganeh
Yilmaz, B., E. Aras, Nacar, S., Kankal, M. 2018. Estimating suspended sediment load with
multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models. Sci Total Environ 639:826–840. https://doi.org/10.1016/j.scitotenv.2018.05.153.
Zhang, W., Goh, A.T.C.  2016. Multivariate adaptive regression splines and neural network models for prediction of pile drivability. Geosci Front 7:45–52. https://doi.org/10.1016/j.gsf.2014.10.003
Sadegh Safar, M. J. 2020. Hybridization of multivariate adaptive regression splines and random
forest models with an empirical equation for sediment deposition prediction in open channel flow. Journal Pre-proofs, S0022-1694(20)30852-0, https://doi.org/10.1016/j.jhydrol.2020.125392.
García-Nietoa, P.J., E. García-Gonzaloa, J.R. Alonso Fernándezb, C. Díaz Muñizb. 2019. Modeling algal atypical proliferation using the hybrid DE–MARS–basedapproach and M5 model tree in LaBarca reservoir: A case study in northernSpain. Journal homepage.
Sultani, W., Chen, C., Shah, M. 2018. Real-World Anomaly Detection in Surveillance Videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
Koza, J. R. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press.
Russel, SO., Campbell, PF. 1996. Reservoir operating rules with fuzzy programming. J Water Resour Plan
Manag 122(3):165–170.
 
رویکردهای نوین در مهندسی آب و محیط‌زیست
دوره 4، شماره 2
مهر 1404
صفحه 188-212

صفحه اصلی

راهنمای نویسندگان

ارسال مقاله

داوران

تماس با ما