Mahmoudreza Tabatabaei; Amin Salehpour Jam; Jamal Mosaffaie
Abstract
IntroductionThe cycle of soil erosion (including removal, transport and deposition) that controls the sedimentation of watersheds, includes a set of complex and highly nonlinear processes. On the other hand, the factors influencing sedimentation in watersheds are very diverse, and according to the specific ...
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IntroductionThe cycle of soil erosion (including removal, transport and deposition) that controls the sedimentation of watersheds, includes a set of complex and highly nonlinear processes. On the other hand, the factors influencing sedimentation in watersheds are very diverse, and according to the specific conditions of climate, soil, vegetation, geology, topography, etc., in each basin, the weight and role of each of the mentioned factors in sediment production is very different. Accurately determining and measuring these factors and making mathematical relationships between them are often difficult, expensive, time-consuming and error-prone, and this is the case with the use of models based on computational intelligence and the use of a limited number of basin dynamic variables, it is possible to simulate the behavior of the watershed in sediment production. Regardless of the type of intelligent models, in most of the conducted research (especially in internal research), the simulation of suspended sediment is mainly based on the discharge variable and the role of variables such as precipitation (especially precipitation obtained from satellite images), which are effective in the sedimentation of basins, have received less attention. In addition to precipitation, the skewness of sediment measurement data is also one of the issues that lack of recognition and attention will reduce the efficiency of estimator models. In the present study, the role of variable daily rainfall (taken from CHIRPS satellite) in the simulation of suspended sediment of Qarachai River has been investigated. Materials and methodsMulti-layer perceptron artificial neural network was used in order to simulate the daily suspended sediment concentration of Qarachai River (at the Ramian hydrometer station in Golestan province). In this regard, the variables of discharge and previous discarge (in instantaneous and daily scales) as well as the average daily and previous rainfall of the basin (taken from CHIRPS satellite) for a statistical period of 37 years (1980-2017) as variables model input was used. In order to increase the generalization power of the models, self-organized mapping neural network (for data clustering) and gamma test was used to find the best combination of input variables. In order to improve the efficiency of network training, a variety of activation and loss functions as well as the overfitting prevention algorithm were used. In order to investigate the effect of using activation and loss functions in suspended sediment estimation, different scenarios were considered, which led to the construction of 9 models. After that, using validation indicators, the effectiveness of the models in simulating suspended sediment was investigated and compared, and then the best model was selected. Results and discussionThe results obtained from the present research showed that among the different models, the neural network model with Huber's activation function and ReLU loss function, having the average absolute value of the error equal to 368 mg/l, the root mean square error equal to 597 mg per liter, the Nash-Sutcliffe coefficient of 0.87 and the percent bias -2.2% were selected as the best model. The results also showed that the use of the rainfall variable (as one of the important factors in causing erosion and sediment transfer in the basin) has improved the efficiency of the models, therefore, considering the ease of using CHIRPS satellite rainfall data, it is suggested in order to simulate the suspended sediment of rivers, this data is also used along with other predictive variables. ConclusionIn the simulation of suspended sediment, discharge variable is often used as the only predicting variable of suspended sediment, while in basins with rainy, or rainy-snow regimes, the role of precipitation in the production of surface runoff and soil erosion is very important and plays an important role in the production and transport of sediment in the basin. In this regard, although the use of rainfall data obtained from ground rain gauge stations has played an effective role in increasing the efficiency of data-based models in estimating suspended sediment, however, the preparation of hundreds of spatial distribution layers of daily rainfall from the data point data of ground stations, the use of this variable in the simulation of the suspended sediment of the basin has been faced with many problems (such as the lack or inappropriateness of the spatial distribution of rain gauge stations, statistical deficiencies, the use of inappropriate interpolation methods and time-consuming calculations). Therefore, in practice, the variable of river flow is often used as a predictor of sediment, and precipitation is used less often. One of the solutions to the problem mentioned in the present study is the use of CHIRPS satellite data, which was investigated for the first time in this study. These data, available since 1981, can easily be used to simulate suspended sediment or other applications related to watersheds. Another important point that needs to be taken into account in the simulation of suspended sediment is the presence of high skewness in sediment measurement data (both suspended sediment and flow rate), which lack of attention in the process of training (or recalibration) and testing the models leads to It will lead to the construction of weak models in terms of efficiency and the existence of uncertainty in the accuracy of their results. In this regard, it is necessary to use logarithmic transformations or suitable functions of activation and loss in the training process, which in this research, two functions, ReLU and Huber, were proposed respectively. Another important point is to pay attention to the generalization power of data-based models, which is largely dependent on the data used in their calibration or training process. These data should be selected in such a way that while they are representative of the data in the entire statistical period, they are similar and have the same distribution with other data sets (such as cross-validation or test sets). According to the results obtained from the present research and in order to increase the efficiency of artificial neural network models in estimating the suspended sediment of watershed hydrometric stations, it is suggested to use the experiences obtained in this research in other sediment measuring stations of the country.
Mohammad Jafar Soltani; Baharak Motamedvaziri; aliakbar noroozi; Hassan Ahmadi; Jamal Mosaffaei
Abstract
Extended abstractIntroductionDust event is one of the natural events that occur widely in the world, especially in dry areas. One of the main and effective factors in the occurrence of this phenomenon is the geographical location and climatic conditions of the regions affected by this phenomenon. The ...
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Extended abstractIntroductionDust event is one of the natural events that occur widely in the world, especially in dry areas. One of the main and effective factors in the occurrence of this phenomenon is the geographical location and climatic conditions of the regions affected by this phenomenon. The purpose of this research is to analyze the cause-and-effect relationships of dust events in Hendijan region with the approach of the Driver-Pressure-Situation-Effect-Response (DPSIR) framework. Materials and methodsThe DPSIR process is an analysis based on the "cause-disability" relationship of factors for policy-making and management planning. The DPSIR framework is a systems thinking framework that assumes cause-and-effect relationships between environmental and socio-economic systems. This conceptual framework uses a cycle of causes and results for the proper integration of basic economic, social and environmental data and information, specifies the relationship between environmental processes and human factors, and also leads to an understanding of the relationship between policy levels and environmental studies. The trend of each component of DPSIR was also evaluated by applying quantitative criteria for the time period of 2007-2019. Results and discussionThe results of the research showed that the total index of all DPSIR components has an upward trend for the studied period. The slope of the trend related to D, P, S, I and R components was equal to 0.06, 0.03, 0.02, 0.05 and 0.02, respectively. Although some responses were made to reduce the influence of others. The components of DPSIR have been adopted to improve the dust situation, but the research results and the process of changes showed that they were not sufficient and integrated. In this research, a variety of answers related to the components of the driving force, pressure, situation, and effects were identified. The results showed that dust concentration and dusty days in the region increased during the study period and more attention was paid to reactive responses and less focus on preventive responses. Also, paying attention to the response of increasing the efficiency of irrigation due to the high correlation between the state of dust concentration and pressure factors such as the amount of rainfall, soil moisture, and exploitation of water resources in the research area, special attention to the development of water extraction systems as one of the most important responses. Management issues due to the existence of negative and high correlation with the dust situation during the years of research, adequate and sustainable supply of water resources by reducing and minimizing the diversion dams downstream of the Kowsar and Ask dams due to the increase in dust concentration with the increase of flood diversion operations and construction Reservoir and diversion dams since 2009, Considering the water rights of wetlands in the region with the aim of preventing wetlands from drying up and creating a dust center, especially in the west of Hendijan city, and carefully choosing the appropriate plant species, as well as carrying out desertification operations (planting saplings, mulching, building windbreaks). With the ecological conditions of the region, due to the negative correlation between the desertification operation and the reduction of dust concentration since 2014, it can help to improve the dust situation in the research area. ConclusionIn this regard, it is recommended to respond to driving forces (D) and pressures (P) so that while improving the conditions and adverse effects caused by it, the drivers and pressures that create the current situation can also be controlled. Based on this, the incomplete implementation of the answers is another reason for not achieving management goals and making the situation and the number of dusty days more unfavorable. It is also suggested to implement the integrated watershed management program in Hendijan city through the development of a joint water, agriculture, and natural resources program upstream of the watershed in order to clarify the effective measures of the water production, distribution, and consumption chain in the upstream lands.
Amin Salehpour Jam; Hamid Reza Peyrowan; Mahmoud Reza Tabatabaei; Amir Sarreshtehdari; Jamal Mosaffaie
Abstract
The desertification process, by reducing the biological production potential, leads to the destruction of ecosystems. In this research, to assess the role of edaphic factors on desertification in rangelands surrounding Eshtehard, Halgh-e-Darreh highlands, first, the map of units was created by crossing ...
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The desertification process, by reducing the biological production potential, leads to the destruction of ecosystems. In this research, to assess the role of edaphic factors on desertification in rangelands surrounding Eshtehard, Halgh-e-Darreh highlands, first, the map of units was created by crossing maps of slope classes, land uses, and geology using ArcGIS 10.3 software. Three indices of erodibility, salinity and permeability for each land unit were considered and classified. 185 and 179 samples were taken during 2018 and 2019 for indices of salinity and permeability for zoning of the study area, respectively. Then, weights of indices and consistency ratio were calculated by the AHP method. Method of multicriteria optimization and compromise solution, VIKOR method was used to prioritize the alternatives. After calculating the weighted normalized values, priority was given to desertification potential of the units. Also, the results of AHP showed that from the experts' point of view, salinity is the most important factor in desertification. Other factors such as susceptibility to erosion and permeability coefficient are in the next rank order, respectively. The AHP-VIKOR model has very high degree of adaptation to the corresponding classes in the control map. The percentage of compliance and non-compliance of the classes of the potential of desertification risk were 92.91% and 7.09%, respectively. The range of changes of Vikor index based on AHP-VIKOR method varies from 0.443 to 0.967. Accordingly, the study area has three classes of potential or severity of desertification areally 0.5% moderate, 7.06% high and 92.43% very high class respectively.
Mahmoudreza Tabatabaei; Amin Salehpour Jam; Jamal Mosaffaie
Abstract
The proper estimation of the amount of suspended sediment in rivers has an important role in erosion and sediment studies, hydrology and management of watersheds. The simulation of suspended sediment in hydrological systems that has a lot of complexity and at the same time our understanding of the components ...
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The proper estimation of the amount of suspended sediment in rivers has an important role in erosion and sediment studies, hydrology and management of watersheds. The simulation of suspended sediment in hydrological systems that has a lot of complexity and at the same time our understanding of the components and processes within them is always uncertain led to the use of many intelligent models, including artificial neural networks (ANNs). However, the use of these smart models also faces challenges. Determining the proper structure of the network requires optimization of the parameters used (such as the optimal number of neurons and layers, weight and bias, and the type of activation functions), which their proper calibration, using test and error, leads to a lot of time spent in low efficiency. In this study, a multilayer perceptron (MLP) was used to simulate the daily sediment load of the Nirchai River at the site of the Nair hydrometric station in Ardebil province. In order to train the models, in addition to the error back propagation (BP) algorithm, Particle Swarm Optimization (PSO) algorithm was used to optimize the weight and bias of ANNs. The fuzzy clustering method was also used to increase the power of generalization of the models. The results showed that training of ANN models with PSO algorithm with decreasing estimation error (decreasing the PBIAS of estimation and root mean square error up to 0.3% and 10.4 tons per day respectively) is more effective than ANN models that use only error BP techniques. Due to insufficient recorded sediment data in most hydrometric stations of the country on the one hand and the need to train ANNs with sufficient data on the other hand, the use of evolutionary algorithms (e.g. PSO algorithm) can be a good solution for improving the efficiency of intelligent models.
