Document Type : Research Paper


shahid beheshti university


Sediment yield caused by soil erosion process as the most important land degradation index is considered a main challenge in sustainable development and threats the ecosystems. It is therefore very important to estimate the reliable sediment discharge at watersheds outlets. The large river drainage basins and the lack of sediment gauges have led to apply regional analysis methods, to estimate suspended sediment load in the basins without gauges or the gauges with lack of data. The objective of this study was to estimate regional suspended sediment load using principal components regression in homogeneous regions of Sefidrood drainage basin with an area of 59273 km2as dependent variable and 18 physiographic and hydrologic factors in sediment load were recognized in each homogenous region based on principal components analysis (PCA). Finally, the relationship between suspended sediment load with different return periods and controlling factors were determined. The results showed that the stations located in the study area were clustered in two homogeneous groups. In the homogeneous region one, based on the PCA, 18 variables reduced into 5 factors accounting more than 87% of total variance and in the second homogenous region reduced into 3 factors accounting more than 92%. Using the principal component regression in the first homogeneous region, the first factor with the coefficient of determination of sediment discharge with 25- year return period, 0.67, and in the second homogeneous region, the first and second factors with coefficient of determination 0.32 were entered in model.


[1] Abbaspour Fard, M.H., Vandechali, M. and Rouhani, Abbas. (2018). Linear Regression Analysis Some of the important performance parameters of a conventional diesel engine in different working conditions. Journal of Mechanical Engineering, 5, 363- 373. (In Persian)
[2] Arab Ameri, A.R., Shirani, K. and Tazeh, M. (2017). Numerical Analysis of Factors Affecting Landslide Incidence and its Sensitivity Zoning Using Logistic Regression and Linear Multivariate Regression (A Case Study: Marbur Basin). Journal of range and watershed management, 1, 121- 161. (In Persian)
 [3] Arman, N. (2012). Regional modeling of soil erosion and sediment yield in Northern Alborz. PHD Thesis, Tehran University, Faculty of Natural Resources, 286 pp. (in Persian)
[4] Arman, N., Faghouri, Z., Faraji, M. and Khorsandi, Z. (2017). Determination of Effective Factors on Sediment Using Statistical Methods (A Case Study: Seyyed Abad Basin). Journal of Watershed Engineering, 2,190- 204. (In Persian)
[5] Armed Forces Organization, Topographic Maps of Guilan, Azarbayejan sharghi, Hamedan, Kordestan, Zanjan & Ghazvin Provinces, Scale 1:50000. (In Persian)
[6] Behmanesh, J., Mohammadpour, M. and bateni, M.M. (2017). Comparison of River Suspended Sediment Load Estimation, using Regression and GA Methods. Journal of Watershed Management Research, Vol. 8, No.16, 132- 141.  (In Persian)
[7] Dalir, P., R. Cash. and V. Gholami. (2015). The most important factors related Nvlyd forest roads in the forests of Northern Iran deposition. Journal of Environmental Degradation, 2, 12- 23.
[8] Esmaeeli, A., samiee, M., Bayat, R. and Lotfi, Z. (2011). Evaluation of hydrological homogenization methods in Basin Tehran Province. the Fourth Iranian Conference of Water Resources Management, Amir kabir University, 1- 9. (In Persian)
 [9] Emami, E. (2008). Investigating the regional model of sediment using artificial neural networks and comparing it with multivariate regression. Second National Conference on Hydroelectric Power Plants, 1- 7. (In Persian)
[10] Eldarmi, A. and Sheikhipour, A. (2016). Investigation changes of river morphology and its role on erosion and sedimentation with using HEC-RAS (Case study: Khorram Abad River, Duab Visian). Journal of Quantitative Geomorphological Research, 5(3), 146- 163. (In Persian)
[11] Geological Survey of the country, geological maps of Guilan, Azarbayejan sharghi, Hamedan, Kordestan, Zanjan & Ghazvin provinces, Scale 1:100000. (In Persian)
[12] Ghorbani. M. A., Fakherifar, A., Nemati, S. and Tolouee, S. (2011). Determining Homogeneous Regions of Spatial Distribution of Suspended Load in Aji-Chai River Basin. Journal of Water and Soil Science, 21 (2), 15- 24. (In Persian)
 [13] Groten, J. T. and Johnson, G. (2018). Report Comparability of River Suspended- Sediment Sampling and Laboratory Analysis Methods. U.S. Geological Survey, Reston, Virginia, 1- 34
[14] Heng, S. and Suetsugi, T. (2014). Development of a regional model for catchment-scale suspended sediment yield estimation in ungauged rivers of the Lower Mekong Basin. Geoderma, Vol. 235- 236, 334- 346.
[15] Imaizumi, M. and Kato, K. (2017). PCA-based estimation for functional linear regression with functional responses. Journal of Multivariate Analysis, Vol. 163, 15- 36.
[16] Johnson, R.A. and Wichern, D.W. (1992). Applied multivariate statistical analysisPrentice-Hall: Englewood Cliffs, Pearson Education, 6ed edition, 184- 188.
[17] Kalteh, A. M. (2002). Investigating the Factors Affecting the Sedimentation of North and South Alborz Basin, Master's thesis, Faculty of Natural Resources, Tehran University, 10- 92. (In Persian)
[18] Kannan, N., Osei, E.,  Cao. Y. and Saleh, A. (2017) . Estimating sediment and nutrient delivery ratios in the Big Sunflower Watershed using a multiple linear regression model. Journal of Soil and Water Conservation, 72 (6), 68- 83.
[19] Khalaji, S. (2014). Investigation of the factors affecting on erosion of gully in Robat-Tork-Delijan, Master's Thesis, Faculty of Geography, shahid beheshti University, 127 pp. (In Persian)
[20] Kheirfam, H. and Vafakhak, M. (2014). Evaluation of gamma test, cluster analysis, discriminant function analysis and andrews curves methods to separate homogeneous watersheds for regional analysis of suspended sediment. Journal of Water and Soil Conservation, 4(2), 65- 84. (In Persian)
[21] Kheirfam, H. and Vafakhah, M. (2015). Assessment of some homogeneous methods for the regional analysis of suspended sediment yield in the south and southeast of the Caspian Sea. Journal of Earth System Science. Vol. 124, Issue 6, 1243- 1267.
 [22] Kiyasheshaki, S. (2014). The role of factors affecting the avalanche occurrence and its zoning in the Meigoon - Shemshak axis, Master's thesis, Faculty of Geography, Shahid beheshti University, 158 pp. (In Persian)
[23] Lin, G.F. and Wang, Ch.M. (2006). Performing cluster analysis and discrimination analysis of hydrological factors in one step. Advances in Water Resources,  29, 1573- 1585.
 [24] Mahmoud Abadi, E., Karimi, A. R., Hagh Niya, GH.H. and Sepehr, A. (2017). Multivariate Regression Function, Artificial Neural Network and Gene Expression Planning in Determining Some Properties of Soil. Journal of Water and Soil Cnservation, 2, 23- 44.  (In Persian)
[25] Melesse, A.M., Ahmad, S., McClaina, M.E., Wang, X. and Limd, Y.H.‌ (2011). Suspended sediment load prediction of river systems: An artificial neural network approach. Agricultural Water‌Management, 98, 855- 866.
 [26] Mirdavoudi, H. R., Zahedi, H., shokoubi, M. and Torkan, J. (2006). Relationships between the most important ecological factors and rangeland vegetative using multivariate data analysis methods. (case study: South of Markazi province). Iranian Journal of Rangeland and Desert Research, 13 (3), 201- 211. (In Persian)
[27] Mirlatifi, M., Seifi, A. and Riyahii, H. (2010). Combined multiple regression modeling-component analysis and principal factors analysis (MLR-PCA) in prediction of reference evapotranspiration (case study: Kerman station). Journal of Water and Soil, 24 (6), 1186- 1196. (In Persian)
 [28] Mohammadi Khoshouee, M., Maleki Nejad, H. and Dastourani, M. T. (2017). Comparison of Regional Analysis Methods for Peak Flood Assessment (Case Study: Isfahan-Sirjan and Yazd-Ardakan Plains). Journal of Range and Watershed Management, 70 (2), 515- 529. (In Persian)
[29] Mohtadi, A., Hejazi, R., Hosseini, S.A. and Moemeni, M. (2018). Applying the "main component analysis" technique to the data of the variables affecting stock returns. Journal of Financial Accounting and Audit Research, 37, 25- 52. (In Persian)
[30] Nasri, M and Najafi, A. (2015). Determining Mathematical relationship between Sediment delivery ratio and Watershed Factors.  journal of Natural ecosystems of Iran, 6 (2), 1- 12. (In Persian)
[31] Neishabouri, M. R., Bayat, H. Rastgou, M. Mohammadi, K. Gregory, A. and Nariman zadeh, N. (2016). Parametric Estimation of Water Retention Using MGMDH Method and Principal Component Analysis. Journal of soil science, Vol. 50, 1- 29.
[32] Nosrati, K., Ahmadi, M., Sarvati, M. R. and Mezbani, M. (2013). Determination of the Effective Factors in Flooding Potential of Darrehshahr Drainage Basin Based on Hydrological Homogeneous Area. Journal of Geogrphical Planning of Space Quarterly, 3(8), 119- 136. (In Persian)
[33] Nosrati, K. (2013). Applied Methods in Scientific Research, First edition, Shahid Beheshti University Jahad Publishing, 204 pp. (In Persian)
[34] Nosrati, K., Laaha, G., Sharifnia, S.A. and Rahimi, M. (2015). Regional low flow analysis in Sefidrood Drainage Basin, Iran using principal component regression. Journal of Hydrology Research, 46 (1), 121- 136.
[35] Noori, R., Karbassi, A.R., Moghaddamnia, A., Han, D., Zokaei-Ashtiani, M.H., Farokhnia, A. and Ghafari G.M. (2011). Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction. Journal of Hydrology, 401, 177- 189. (In Persian)
[36]. Pektaş, A. O. (2015). Determining the essential parameters of bed load and suspended sediment load. Global Warming, Vol. 8 (3), 359- 335.
[37] Rafik, B. and Kamel, B. (2016). Prediction of permeability and porosity from well log data using the nonparametric regression with multivariate analysis and neural network, Hassi R’Mel Field, Algeria. Egyptian Petroleum research institute, 26 (3), 778- 763.
[38] Rezazadeh Joodi, A., Sattari, M. T., Safdari, F. and Ghahremanzadeh, F. (2016). Evaluation of the performance of M5 tree modeling and regression models in suspended sediment modeling of the river. Journal of Water and Soil Conservation, 6(2), 109- 124. (In Persian)
 [39] Roman, D. C., Vogel, R. M. and Schwarz, G. E. (2012). Regional regression models of watershed suspended-sediment discharge for the eastern United States. Journal of Hydrology, Vol. 472, 53- 62.
[40] Ramos, M.C. (2001). Divisive and hierarchical clustering techniques to analyze variability of rainfall distribution patterns in a Mediterranean region. Journal of Hydrology, 57(2), 132- 138.
[41] Sadeghi, S.H.R. and Singh, J.K. (2005). Development of a synthetic sediment graph using hydrological data. Journal of Agricultural Science and Technology, 2 (1-2), 69- 77. (In Persian)
[42] Sadeghi, S.H.R., Mizuyama, T., Miyata, S., Gomi, T., Kosugi, K., Fukushima, T., Mizugaki, S. and Onda, Y. (2008). Determinant factors of sediment graphs and rating loops in a reforested watershed. Journal of Hydrology, 356 (3- 4), 271- 282. (In Persian)
 [43] Samadzadeh, R., Khayam, M and Fazeli, A. (2013). Modeling Regional Estimation of Suspended Sediment in Darreh Rood Basin in Ardebil. Journal of Geography and Environmental Planning, 51(3), 153- 179. (In Persian)
[44] Tabatabaee, M. R. and Soleimani, K. (2013). Estimation of Fluvial Suspended Sediment Concentration Using MODIS Sensor (Case Study: Hydrometry Station of Mollasani), Journal of Irrigation Science and Engineering, 36(2), 83- 95. (In Persian)
 [45] Tananaev, N. I. (2017). Applying Regression Analysis to Calculating Suspended Sediment Runoff: Specific Features of the Metho.  journal of Water Resources, Vol. 40, No. 6, 585- 592.
[46] Tryon, R.C. (1939). Cluster analysis. New York: McGraw-Hill.
[47] Uca., Toriman, E., Jaafar, Othman., Maru, Rosmini, Amal, Arfan. and Ansari, Saleh Ahmar. (2018). Daily Suspended Sediment Discharge Prediction Using Multiple Linear Regression and Artificial Neural Network. Journal of Physics Conference Series, Ser. 95401203, 1- 20.
[48] Wuttichaikitcharoen, P. and Babel, M. S. (2014). Principal Component and Multiple Regression Analyses for the Estimation of Suspended Sediment Yield in Ungauged Basins of Northern Thailand. Journal of Water, 6(8), 2847- 2848.
 [49] Varvani, J., Ebrahimi, N. Gh., Yousefi, M., Shadmani, A. and Nikche Farahani. Sh. (2015). Investigation of the efficiency of nero fuzzy and tree regression methods in estimating suspended sediment load of the river. Journal of Watershed management Research, 4(109), 105- 115. (In Persian)
[50] Water Resources Organization (TAMAB), Sediment and Rain Data, (1961- 2011). (In Persian)
[51] Zhong, X. and Enke, D. (2017). Forecasting Daily Stock Market Return Using Dimensionality Reduction. Expert Systems with Applications, 67, 139- 126.