Alijanpour Shalmani, A., Vaezi, A., Tabatabaei, M. (2020). Optimal combinations of hydrological variables for modeling of daily suspended sediment load in Karaj Watershed. , 12(1), 228-243. doi: 10.22092/ijwmse.2019.121319.1463
Adele Alijanpour Shalmani; Alireza Vaezi; Mahmoudreza Tabatabaei. "Optimal combinations of hydrological variables for modeling of daily suspended sediment load in Karaj Watershed". , 12, 1, 2020, 228-243. doi: 10.22092/ijwmse.2019.121319.1463
Alijanpour Shalmani, A., Vaezi, A., Tabatabaei, M. (2020). 'Optimal combinations of hydrological variables for modeling of daily suspended sediment load in Karaj Watershed', , 12(1), pp. 228-243. doi: 10.22092/ijwmse.2019.121319.1463
Alijanpour Shalmani, A., Vaezi, A., Tabatabaei, M. Optimal combinations of hydrological variables for modeling of daily suspended sediment load in Karaj Watershed. , 2020; 12(1): 228-243. doi: 10.22092/ijwmse.2019.121319.1463

Optimal combinations of hydrological variables for modeling of daily suspended sediment load in Karaj Watershed

مهندسی و مدیریت آبخیز
Article 17, Volume 12, Issue 1, April 2020, Pages 228-243    PDF (1.13 M)
Document Type: Research Paper
DOI: 10.22092/ijwmse.2019.121319.1463
Authors
Adele Alijanpour Shalmani* 1; Alireza Vaezi2; Mahmoudreza Tabatabaei3
1PhD Student, Soil Science. Department, University of Zanjan, Iran
2Professor Soil Science Department, University of Zanjan, Iran
33Assistant Professor, Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran
Abstract
Analysis of suspended sediment load data in rivers is the basis for understanding the trend of erosion and sediment in the management and planning of soil and water resources. Due to lack of access to daily suspended sediment loading data with direct measurement, it is important to use methods for modeling and estimating it in watersheds. One of the best methods used in this field is the use of artificial neural networks. To evaluate daily suspended sediment load, Sira hydrometric station was studied in Karaj River watershed. The number of data used in this study included 624 information records of 31 years (1981–2011) statistical period .Input data to the artificial neural network models included instantaneous flow discharge, average daily flow discharge, average daily flow discharge with a delay of three days, average daily precipitation and average daily precipitation with a delay of three days. Output data to models was daily suspended sediment load. In this research, gamma test and genetic algorithm were used to obtain optimal variables and best combination of variables for entering the model. Then, these combinations with some combination of test and error variables were entered to artificial neural network models. The self-organizing map neural network was used for data clustering and all data were divided into three homogeneous groups: 70 percentage training data, 15 percentage validation data and 15 percentage test data. Then, the combination of variables entered to neural network models with activation functions log sigmoid and tangent sigmoid. The results showed that the neural networks using the optimal variable combinations in comparison with manual combinations have a more accurate estimate for suspended sediment load. In all combinations of inputs to neural network models, a model with tangent sigmoid activation function, with input variables combination including, instantaneous flow discharge (Q), average daily flow discharge (Qi), average daily flow discharge for two day ago (Qi-2), average daily flow discharge for three day ago (Qi-3), average daily precipitation (Pi), average daily precipitation for two day ago (Pi-2) and average daily precipitation for three day ago (Pi-3), was the best model for estimating daily suspended sediment load. This model has the lowest of error (MAE=500.05 (ton/day), RMSE=1995.33(ton/day) and Erel=7%), the highest accuracy (R2=0.96), the highest performance model (NSE=0.96) and has the lowest general standard deviation (GSD=0.97) compared to other models. Also, this model is the best combination with the most influential input variables derived from gamma test and genetic algorithm for estimating SSL.
Keywords
Artificial neural network; Clustering; Gamma test; Self-organizing map; Tangent sigmoid
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