نشریه علمی هیدرولیک

نشریه علمی هیدرولیک

A New Machine Learning Model for Predicting Suspended Sediment Load

نوع مقاله : مقاله کامل (پژوهشی)

نویسنده
گروه مهندسی آب دانشگاه شهرکرد
چکیده
Predicting suspended sediment load is important for water resource management, water quality protection, erosion control, infrastructure planning, flood management, ecological conservation, pollution control, and environmental impact assessments. As a novel aspect of this study, the ANFIS-M5T model is introduced and used to predict suspended sediment loads. Our study combines the adaptive neuro-fuzzy inference system (ANFIS) and the M5T model to create a hybrid model for predicting suspended sediment load (SSL). The lagged rainfall, discharge, and SSL values were used to predict SSL. The results showed that the introduced ANFIS-M5T model performs better than other models so that it had the lowest mean absolute error (MAE: 525), the highest Nash Sutcliffe efficiency (0.98) and the lowest Percent bias (4).The ANFIS model had the second lowest MAE of 576, followed by MLP (586), RFN (682), and M5T (981). Thus the ANFIS-M5T was a reliable tool for predicting SSL. By tackling the obstacles, assessing various methods, and showcasing the ANFIS-M5T model's efficiency, our research contributes to the continuous improvement and advancement of sediment measurement techniques and tools.
کلیدواژه‌ها

موضوعات


Achite, M., Yaseen, Z.M., Heddam, S., Malik, A. & Kisi, O. (2022). Advanced machine learning models development for suspended sediment prediction: comparative analysis study. Geocarto International. 37(21), https://doi.org/10.1080/10106049.2021. 1933210.
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(3), 383-398.
AlDahoul, N., Essam, Y., Kumar, P., Ahmed, A.N., Sherif, M., Sefelnasr, A. & Elshafie, A. (2021). Suspended sediment load prediction using long short-term memory neural network. Scientific Reports, 11, 7826, https://doi.org/10.1038/s41598-021-87415-4.
Bainbridge, Z., Lewis, S., Bartley, R., Fabricius, K., Collier, C., Waterhouse, J., Garzon-Garcia, A., Robson, B., Burton, J., Wenger, A. & Brodie, J. (2018). Fine sediment and particulate organic matter: A review and case study on ridge-to-reef transport, transformations, fates, and impacts on marine ecosystems. Marine Pollution Bulletin135, 1205-1220. 
Bartley, R., Bainbridge, Z.T., Lewis, S.E., Kroon, F.J., Wilkinson, S.N., Brodie, J.E. & Silburn, D.M. (2014). Relating sediment impacts on coral reefs to watershed sources, processes and management: A review. Science of the Total Environment468, 1138-1153.
Darabi, H., Mohamadi, S., Karimidastenaei, Z., Kisi, O., Ehteram, M., ELShafie, A. & Torabi Haghighi, A. (2021). Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models. Soft Computing, 25, 7609-7626.
de Almeida, W.S., Seitz, S., de Oliveira, L.F.C. & de Carvalho, D.F. (2021). Duration and intensity of rainfall events with the same erosivity change sediment yield and runoff rates. International Soil and Water Conservation Research9(1), 69-75.
Downs, P.W. & Soar, P.J. (2021). Beyond stationarity: influence of flow history and sediment supply on coarse bedload transport. Water Resources Research57(2), e2020WR027774,  https://doi.org/10.1029/2020WR027774.
Duarte, V.B.R., Viola, M.R., Giongo, M., Uliana, E. M. & de Mello, C.R. (2022). Streamflow forecasting in Tocantins river basins using machine learning. Water Supply, 7, 6230-6244.
Ehteram, M., Ahmed, A.N., Latif, S.D., Huang, Y. F., Alizamir, M., Kisi, O., ... & El-Shafie, A. (2021). Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction. Environmental Science and Pollution Research, 28, 1596-1611.
Fath, A.H., Madanifar, F. & Abbasi, M. (2020). Implementation of multilayer perceptron (MLP) and radial basis function (RBF) neural networks to predict solution gas-oil ratio of crude oil systems. Petroleum, 6(1), 80-91.
Ghanbari-Adivi, E., Ehteram, M., Farrokhi, A. & Sheikh Khozani, Z. (2022). Combining radial basis function neural network models and inclusive multiple models for predicting suspended sediment loads. Water Resources Management36(11), 4313-4342
Ghenai, C., Al-Mufti, O.A.A., Al-Isawi, O.A.M., Amirah, L.H.L. & Merabet, A. (2022). Short-term building electrical load forecasting using adaptive neuro-fuzzy inference system (ANFIS). Journal of Building Engineering, 52, https://doi.org/10.1016 /j.jobe.2022.104323.
Goyal, M.K. (2014). Modeling of Sediment Yield Prediction Using M5 Model Tree Algorithm and Wavelet Regression. Water Resources Management, 7, 1991-2003.
Gupta, D., Hazarika, B.B., Berlin, M., Sharma, U. M. & Mishra, K. (2021). Artificial intelligence for suspended sediment load prediction: a review. Environmental Earth Sciences, 80, 346. https://doi.org/10.1007/s12665-021-09625-3.
Heddam, S. & Kisi, O. (2018). Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree. Journal of Hydrology, 559, 499-509.
Jia, Y., Wang, H., Li, P., Su, Y., Wang, F. & Huo, S. (2023). Particle swarm optimization algorithm with Gaussian exponential model to predict daily and monthly global solar radiation in Northeast China. Environmental Science and Pollution Research, 30(5), 12769-12784.
McMillan, H., Freer, J., Pappenberger, F., Krueger, T. & Clark, M. (2010). Impacts of uncertain river flow data on rainfall‐runoff model calibration and discharge predictions. Hydrological Processes: An International Journal24(10), 1270-1284.  
Mohammadi, B., Guan, Y., Moazenzadeh, R. & Safari, M.J.S. (2021). Implementation of hybrid particle swarm optimization-differential evolution algorithms coupled with multi-layer perceptron for suspended sediment load estimation. Catena, 198, 105024, https://doi.org/10.1016/j.catena.2020. 105024.
Nhu, V.H., Khosravi, K., Cooper, J.R., Karimi, M., Kisi, O., Pham, B.T. & Lyu, Z. (2020). Monthly suspended sediment load prediction using artificial intelligence: testing of a new random subspace method. Hydrological Sciences Journal, 65(12), 2116-2127.
Rahgoshay, M., Feiznia, S., Arian, M. & Hashemi, S.A.A. (2019). Simulation of daily suspended sediment load using an improved model of support vector machine and genetic algorithms and particle swarm. Arabian Journal of Geosciences, 12, 277, https://doi.org/10.1007/s12517-019-4444-7.
Rahul, A.K., Shivhare, N., Kumar, S., Dwivedi, S. B. & Dikshit, P.K.S. (2022). Modelling suspended sediment concentration and discharge relationship using neural network and adaptive neuro-fuzzy inference system. Arabian Journal of Geosciences, 15, 493, https://doi.org/10.1007/s12517-022-09744-6.
Salih, S.Q., Sharafati, A., Khosravi, K., Faris, H., Kisi, O., Tao, H., Ali, M. & Yaseen, Z.M. (2020). River suspended sediment load prediction based on river discharge information: application of newly developed data mining models. Hydrological Sciences Journal, 65(4), 624-637.

  • تاریخ دریافت 01 اردیبهشت 1403
  • تاریخ بازنگری 27 مرداد 1403
  • تاریخ پذیرش 01 مهر 1403