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Prediction of daily suspended sediment load using the Genetic Expression Programming and Artificial Neural Network models | ||
| Environmental Resources Research | ||
| دوره 10، شماره 1، فروردین 2022، صفحه 115-132 اصل مقاله (2.16 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22069/ijerr.2023.19045.1338 | ||
| نویسندگان | ||
| Adele Alijanpour Shalmani* 1؛ Ali Reza Vaezi2؛ Mahmoud Reza Tabatabaei3 | ||
| 1Ph.D., Soil Science. Department, University of Zanjan, Zanjan, Iran. | ||
| 2Professor, Soil Science. Department, University of Zanjan, Zanjan, Iran. | ||
| 3Associate Professor, Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran. | ||
| چکیده | ||
| Because of the quantitative and qualitative problems of Daily Suspended Sediment Load (SSL) data with direct measurement, it is important to use methods for predicting it in watersheds. In this research, two methods consisting of the artificial neural network (ANN) and Genetic Expression Programming (GEP) were used to predict SSL. The studied area was a watershed in north of Iran. Input data included instantaneous flow discharge (Q), average daily flow discharge (Qi), average daily precipitation (Pi) and the output was SSL. A clustering method was used to homogenize data for the self-organizing map (SOM) method and then, all data were divided into three groups including 70, 15 and 15% for training, validating and testing, respectively. Also, the gamma test method was used to determine the best combination of input variables. In all combinations of inputs to the ANN and GEP models, the ANN model with tangent sigmoid activation function and input variables combination including Q, Qi, Qi-2, Qi-3, Pi, Pi-2, Pi-3 was the best for estimating SSL in the area with a root mean square error of 1995.3 (ton day -1) and the Nash-Sutcliff efficiency of 0.96. In general, the results of this study showed that intelligent models are capable of accurately estimating the SSL value. Also, using SOM preprocessing techniques and gamma tests increased the generalization power of the models. We also found that choosing the most influential variables and their best combination increased the modeling power and accuracy of SSL estimation, respectively. | ||
| کلیدواژهها | ||
| Daily discharge؛ Daily precipitation؛ Clustering؛ Gamma test؛ Self-organizing map | ||
| مراجع | ||
|
Abbaspour, B., and Haghiabi, A.H. 2015. Comparing the estimation of suspended load using two methods of sediments rating curve and artificial neural network (A Case Study: Cham Anjir Station, Lorestan Province). J. Environ. Treat. Tech. 3(4), 215-222.
Baartman, J.E.M., Masselink, R., Keesstra, S.D., and Temme, A.J.A.M. 2013. Linking landscape morphological complexity and sediment connectivity. Earth Surf. Process. Landf. 38(12), 1457–1471.
Baylar, A., Unsal, M., and Ozkan, F. 2011. GEP modeling of oxygen transfer efficiency prediction in aeration cascades. KSCE Journal of Civil Engineering. 15(5), 799–804.
Bolboaca, S.D., Jantschi, L. 2006. Pearson versus Spearman, Kendall's tau correlation analysis on structure-activity relationships of biologic active compounds. Leonardo Journal of Sciences. 5(9), 179-200.
Bowden, G.J., Maier, H.R., and Dandy, G.C. 2002. Optimal division of data for neural network models in water resources applications. Water Resources Research. 38(2), 1-2.
Cevik, A. 2007. A new formulation for longitudinally stiffened webs subjected to patch loading. Journal of Constructional Steel Research. 63(10), 1328–1340.
Chaudhary, V., Bhatia, R.S., and Ahlawat, A. 2014. The self-organizing map learning algorithm with inactive and relative winning frequency of active neurons. Journal HKIE Transactions. 21(1), 62–67.
Chen, I.T., Chang, L.C., and Chang, F.J. 2017. Exploring the spatio-temporal interrelation between groundwater and surface water by using the Self-Organizing Maps. Journal of Hydrology. 556, 131-142.
Emamgholizadeh, S., and Karimi Demneh, R. 2018. The comparison of artificial intelligence models for the estimation of daily suspended sediment load: a case study on Telar and Kasilian Rivers in Iran. Water Science and Technology: Water Supply. 1-14.
Ferreira, C. 2001. Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems. 13(2), 87-129.
Fort, J.C. 2006. SOM’s mathematics. Neural Networks. 19, 812–816.
Ghabaei Sough, M., Mosaedi, A., Hesam, M., and Hezarjaribi, A. 2010. Evaluation effect of input parameters preprocessing in artificial neural networks (ANNs) by using stepwise regression and Gamma Test techniques for fast estimation of daily evapotranspiration. Journal of Water and Soil. 24(3), 610-624.
He, Z., Wen, X., Liu, H., and Du, J. 2014. A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountainous region. Journal of Hydrology. 509(4), 379–386.
Kakaei Lafdani, E., Moghaddam Nia, A., and Ahmadi, A. 2013. Daily suspended sediment load prediction using artificial neural networks and support vector machines. Journal of Hydrology. 478(4), 25-50.
Kaufman, L., and Rousseeuw, P.J. 2009. Finding Groups in Data: An Introduction to Cluster Analysis. Hoboken, New Jersey: John Wiley and Sons.
Kaveh, K., Duc Bui, M., and Rutschmann, P. 2017. A comparative study of three different learning algorithms applied to ANFIS for predicting daily suspended sediment concentration. International Journal of Sediment Research, 32(3), 340-350.
Kayadelen, C., Gunaydin, O., Fener, M., Demir, A., and Ozvan, A. 2009. Modeling of the angle of shearing resistance of soils using soft computing systems. Expert Systems with Applications. 36(9), 11814–11826.
Kişi, O. 2012. Modeling discharge-suspended sediment relationship using least square support vector machine. Journal of Hydrology. 456(5), 110–120.
Kisi, O., and Ozkan, C. 2017. A new approach for modeling sediment-discharge relationship: Local weighted linear regression. Water Resources Management. 30(2), 1-23.
Kumar Singh, V., Kumar, D., Kashyap, P.S., and Kisi, O. 2018. Simulation of suspended sediment based on gamma test, heuristic, and regression-based techniques. Environmental Earth Sciences. 77, 708.
Li, X., Nour, M.H., Smith, D.W., and Prepasc, A.A. 2010. Neural networks modeling of nitrogen export: model development and application to unmonitored boreal forest watersheds. Environmental Technology. 31(5), 495–510.
Li, C. and YANG, S. 2010. Is chemical index of alteration (CIA) a reliable proxy for chemical weathering in global drainage basins?. American Journal of Science. 310(10), 111–127.
Malik, A., Kumar, A., and Piri, J. 2017. Daily suspended sediment concentration simulation using hydrological data of Pranhita River Basin, India. Computers and Electronics in Agriculture. 138, 20–28.
Marchamalo, M., Hooke, J.M., and Sandercock, P.J. 2016. Flow and sediment connectivity in semi-arid landscapes in SE Spain: patterns and controls. Land Degrad. Dev. 27(4), 1032–1044.
Masselink, R.J.H., Keesstra, S.D., Temme, A.J.A.M., Seeger, M., Giménez, R., and Casalí, J., 2016. Modelling discharge and sediment yield at catchment scale using connectivity components. Land Degradation and development. 27(4), 933–945.
May, R.J., Maier, H.R., and Dandy, G.C. 2010. Data splitting for artificial neural networks using SOM-based stratified sampling. Neural Networks. 23(2), 283-294.
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. Agricultural Water Management. 98(5), 855-866.
Moghaddamnia, A., Ghafari Gousheh, M., Piri, J., Amin, S., and Han, D. 2009. Evaporation estimation using artificial neural networks and adaptive neurofuzzy inference system techniques. Advance Water Res. 32, 88-97.
Mustafa, M.R., Rezaur, R.B., Saiedi, S., and Isa, M.H. 2012. River suspended sediment prediction using various multilayer perceptron Neural Network training Algorithms—A case study in Malaysia. Water Resources Management. 26(7), 1879-1897.
Norouzi, A., Pajouhesh, M., Abdollahi, K., and Esmali Ouri, A. 2021. Estimating the Suspended Sediment Load and Evaluating the Homogeneity and Heterogeneity of Water and Sediment (Case Study: Kasilian watershed, Mazandaran). Iranian Journal of Echo Hydrology. 7(4), 1099-1112. (In Persian).
Nour, M., Smith, D., El-Din, M., and Prepas, E. 2006. Neural networks modeling of stream flow, phosphorus, and suspended solids: application to the Canadian Boreal forest. Water Science and Technology. 53(10), 91-99.
Rashidi, S., Vafakhah, M., Kakaei Lafdani, E. and Javadi, M.R. 2016. Evaluating the support vector machine for suspended sediment load forecasting based on gamma test. Arabian Journal of Geosciences. 9(11), 583.
Shamim, M.A., Hassan, M., Ahmad, S., and Zeeshan, M. 2016. A comparison of Artificial Neural Networks (ANN) and Local Linear Regression (LLR) techniques for predicting monthly reservoir levels. KSCE Journal of Civil Engineering. 20(2), 971–977.
Sheikhalipour, Z., and Hassanpour, F. 2013. Estimation of suspended sediment load using genetic expression programming. Journal of Civil Engineering and Urbanism. 3(2), 292-299.
Soil survey staff. 2010. Keys to soil Taxonomy USDA-NRCS. 327 pages.
Tabatabaei, M. and Salehpour Jam, A. and Hosseini S.A. 2019. Suspended Sediment Load Prediction Using Non-Dominated Sorting Genetic Algorithm II. International Soil and Water Conservation Research. 7(2), 119-129.
Tabatabaei, M., and Salehpour Jam, A. 2017. Optimization of sediment rating curve coefficients using evolutionary algorithms and unsupervised artificial neural network. Caspian Journal of Environmental Sciences. 15(4), 387-401.
Talebi, A., Mahjoobi, J., Dastorani, M.T., and Moosavi, V. 2016. Estimation of suspended sediment load using regression trees and model trees approaches (Case study: Hyderabad drainage basin in Iran). Journal of Hydraulic Engineering. 21(3), 1-8.
Tayfur, G. 2012. Soft computing in water resources engineering - Artifical Neural Networks, Fuzzy Logic and Genetic Algorithms. WIT Press, Southampton, England, UK.
Teodorescu, L., and Sherwood, D. 2008. High energy physics event selection with gene expression programming. Computer Physics Communications. 178(6), 409–419.
Tfwala, S.S., and Wang, Y.M. 2016. Estimating sediment discharge using Sediment Rating Curves and Artificial Neural Networks in the Shiwen River, Taiwan. Water. 8(53), 1-15.
Tokar, A., and Johnson, P. 1994. Rainfall-runoff modeling using Artificial Neural Networks. Journal of Hydrologic Engineering. 4(3), 232–239.
Ulke, A., Tayfur, G., and Ozku, S. 2009. Predicting suspended sediment loads and missing data for Gediz River, Turkey. Journal of Hydrologic Engineering. 14(9), 954-965.
Wolfs, V., and Willems, P. 2014. Development of discharge-stage curves affected by hysteresis using time varying models, model trees and neural networks. Environmental Modelling and Software. 55(4), 107-119.
Zhu, Y.M., Lua, X.X., and Zhoub, Y. 2007. Suspended sediment flux modeling with Artificial Neural Network: An example of the Longchuanjiang River in the Upper Yangtze Catchment, China. Geomorphology. 84(2), 111–125.
Zounemat-Kermani, M., Kişi, O., Adamowski, J., and Ramezani-Charmahineh, A. 2016. Evaluation of data driven models for river suspended sediment concentration modeling. Journal of Hydrology. 16(2), 1-40. | ||
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