Tabatabaei, M., Salehpour Jam, A. (2020). Simulation of the suspended sediment of the country rivers using the technology of intelligent models and open source GIS system, case study: Razin Hydrometric Station, Mozlanghan Watershed, Markazi Province. , 12(4), 1089-1101. doi: 10.22092/ijwmse.2020.341701.1766
Mahmoudreza Tabatabaei; Amin Salehpour Jam. "Simulation of the suspended sediment of the country rivers using the technology of intelligent models and open source GIS system, case study: Razin Hydrometric Station, Mozlanghan Watershed, Markazi Province". , 12, 4, 2020, 1089-1101. doi: 10.22092/ijwmse.2020.341701.1766
Tabatabaei, M., Salehpour Jam, A. (2020). 'Simulation of the suspended sediment of the country rivers using the technology of intelligent models and open source GIS system, case study: Razin Hydrometric Station, Mozlanghan Watershed, Markazi Province', , 12(4), pp. 1089-1101. doi: 10.22092/ijwmse.2020.341701.1766
Tabatabaei, M., Salehpour Jam, A. Simulation of the suspended sediment of the country rivers using the technology of intelligent models and open source GIS system, case study: Razin Hydrometric Station, Mozlanghan Watershed, Markazi Province. , 2020; 12(4): 1089-1101. doi: 10.22092/ijwmse.2020.341701.1766

Simulation of the suspended sediment of the country rivers using the technology of intelligent models and open source GIS system, case study: Razin Hydrometric Station, Mozlanghan Watershed, Markazi Province

مهندسی و مدیریت آبخیز
Article 17, Volume 12, Issue 4, January 2021, Pages 1089-1101    PDF (1.91 M)
Document Type: Research Paper
DOI: 10.22092/ijwmse.2020.341701.1766
Authors
Mahmoudreza Tabatabaei* ; Amin Salehpour Jam
Assistant Professor, Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran
Abstract
Relationships between river water quality parameters and physical, geochemical and biological processes carried between basin resources (soil, vegetation, geology, land use, etc.), meteorological variables (temperature, precipitation, snowmelt, etc.), River hydrological variables (flow discharge), as well as human interventions are often very complex, nonlinear and non–deterministic in a way that makes their complete understanding impossible. In this situation, the use of computational intelligence (such as artificial neural networks) is a useful tool in simulating and estimating river water quality variables such as suspended sediment load. In the present study, by combining open source GIS libraries and neural network models (with and without supervisor), an intelligent GIS system has been designed and coded that can estimate daily suspended sediment load under univariate or multivariate conditions. The results of applying this system to Mazaljan River Watershed at Razin hydrometric station showed that this system is able to simulate suspended sediment load with proper performance and validation  (with root mean square error of 1033 tonday-1, mean absolute error of 455 tonday-1 and Nash-Sutcliffe efficiency of 0.89 for the test data set). In general, this system can be used as a national infrastructure in the simulation and management of suspended sediment in all hydrometric stations in the country by relevant organizations.
Keywords
Artificial Neural Network; Data clustering; Prediction; Self-organizing map; Suspended sediment; Validation
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