Mahmoudreza Tabatabaei; Amin Salehpour Jam; Jamal Mosaffaie
Abstract
In watershed areas, monitoring and assessing erosion and sedimentation processes are crucial, as these processes directly impact the quality and quantity of water resources. The design and construction of advanced systems, such as a specialized geographic information system for the country’s hydrometric ...
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In watershed areas, monitoring and assessing erosion and sedimentation processes are crucial, as these processes directly impact the quality and quantity of water resources. The design and construction of advanced systems, such as a specialized geographic information system for the country’s hydrometric stations, can significantly enhance watershed management research.This study developed a specialized geographic system for managing and analyzing hydrological data using the C# programming language and open-source spatial libraries. The system uses SQLite as a data storage platform and employs Entity Framework 6 (EF6) and LINQ to facilitate data management and extraction. It can perform various spatial and descriptive queries and analyses, as well as statistical analyses and summaries from sedimentation data.The results can be summarized in two sections: the design and construction of the system, and the statistical analysis of sedimentation data from the Aras basin. The statistical analysis of sedimentation data (26,156 recorded data points until 2017) indicates that at the watershed scale, the average daily suspended sediment discharge is 11,814.95 tons per day, the average suspended sediment concentration is 4,185.68 mg per liter, and the average instantaneous flow discharge is 13.16 cubic meters per second.At the study unit scale, the average maximum and minimum suspended sediment discharge correspond to the Jolfa-Duzal unit (code 1105) with 10,312.33 tons per day and the Qareh Ziyaldin unit (code 1108) with 991.96 tons per day, respectively. Additionally, at the hydrometric station scale, the average maximum and minimum daily suspended sediment discharge correspond to the Jolfa station (code 807-19) with 571,697.82 tons per day and the Naur-Exit Neur station (code 0195-19) with 2.82 tons per day.This research aims to develop a national software infrastructure for managing sedimentation data and flow discharge from the country’s hydrometric stations.
Seyed Ahmad Hosseini; Ahmad Tabatabaei
Abstract
Introduction
Simulating suspended sediment in hydrological systems has always been challenging due to inherent complexities and uncertainties. This issue has led to the use of intelligent models such as Artificial Neural Networks (ANNs) as a suitable approach for predicting suspended sediment load. ...
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Introduction
Simulating suspended sediment in hydrological systems has always been challenging due to inherent complexities and uncertainties. This issue has led to the use of intelligent models such as Artificial Neural Networks (ANNs) as a suitable approach for predicting suspended sediment load. Therefore, the use of intelligent models like ANNs has expanded in this field. However, determining the optimal network structure (including the number of neurons, layers, weights, and biases) is usually done through trial and error, which is both time-consuming and inefficient. In this study, a multilayer perceptron neural network was used to simulate the daily suspended sediment load in the Qarasu Sarab watershed (Quri Chay and Hir Chai rivers) located in Ardabil province, Iran.
Materials and methods
In this research, an Artificial Neural Network (ANN) of the Multilayer Perceptron (MLP) type was utilized to simulate the daily suspended sediment load in the Sarab Qareh Su watershed (including the Quri Chay and Hir Chay rivers) in Ardabil province. The neural network models were trained not only whit the conventional backpropagation algorithm but also using the Particle Swarm Optimization (PSO) algorithm to optimize the weights and biases of the neurons. Furthermore, to increase the models' generalization capability, a Self-Organizing Map (SOM) clustering was employed. In addition to the backpropagation algorithm, the Particle Swarm Optimization (PSO) algorithm was also employed to optimize the network weights and biases. Furthermore, to enhance the model's generalization power, SOM clustering was used. The use of evolutionary algorithms such as PSO in training neural networks is an effective approach to improve the accuracy of intelligent models, especially in simulating river suspended sediment and applications related to water resources and watershed management structures.
Results and discussion
Using SOM clustering and the Davies-Bouldin index, the optimal number of clusters was determined as 12 for Koozeh Toupraqi station and 15 for Hir Chai station. Statistical analysis and the Kolmogorov-Smirnov (KS) test showed that data distributions across training, validation, and testing sets were similar, which enhances the generalization capability of the models. Training the neural network models with PSO yielded better performance and lower prediction errors compared to backpropagation. The ANN-PSO-Sig and ANN-PSO-Tan models achieved the best results at Koozeh Toupraqi and Hir Chai stations, respectively. Bias analysis further confirmed that PSO-trained models had lower errors in total sediment load estimation. Overall, results showed that PSO-based training outperforms pure backpropagation training. At Koozeh Toupraqi station, the hybrid ANN-PSO model with sigmoid activation function (ANN-PSO-Sig), and at Hir-Hirchai Topraghi station, the hybrid model with hyperbolic tangent activation function (ANN-PSO-Tan) were selected as optimal models, showing biases of +5.25% and -19.2% and RMSE values of 86.28 and 89.2 tons per day, respectively. These findings demonstrate the improvement in suspended sediment load prediction accuracy by using PSO in neural network training.
Conclusion
The use of the PSO metaheuristic algorithm in training neural network models improved their performance in simulating suspended sediment load. This method outperformed gradient-based error algorithms and provided more accurate weight optimization. The improved bias accuracy in PSO-trained models is crucial for designing hydraulic structures and water resource management. Furthermore, SOM clustering helped select homogeneous and representative datasets for model training, enhancing model generalizability. Overall, considering the complexities and uncertainties in hydrological systems, employing intelligent models combined with evolutionary optimization algorithms like PSO is an effective approach for simulating and monitoring suspended sediment loads. The obtained results can be applied in planning and implementing watershed engineering measures and water resource management.
jamal mosaffaie; Ataollah Ebrahimi; Mahmood Arabkhedri; Parviz Garshasbi; Amin Salehpour Jam; Mahmoudreza Tabatabaei; Hamidreza Peyrowan; Mohammadreza Gharibreza; Mehran Zand; Bagher Ghermez Cheshme
Abstract
Extended abstractIntroductionIn recent years, the country's watersheds have been exposed to man-made problems and sufferings such as erosion, floods, and droughts, leading to great yearly losses. To effectively manage watersheds, it is necessary to conduct appropriate and practical research, which in ...
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Extended abstractIntroductionIn recent years, the country's watersheds have been exposed to man-made problems and sufferings such as erosion, floods, and droughts, leading to great yearly losses. To effectively manage watersheds, it is necessary to conduct appropriate and practical research, which in turn requires solving the problems related to this area. To identify and determine the problems of the research department, getting the opinions of experts, researchers, and elites and thinking together with them is considered a suitable solution, which can later solve problems and problems. This article intends to analyze their problems in soil conservation and watershed management based on common thinking with the research and education centers of agriculture and natural resources of the country. Methods and materialsFor this purpose, these problems were collected based on the request of the SCWMRI from the agricultural and natural resources research and training centers of the province. Then, the announcements were categorized in two organizational and thematic ways (general and partial). In the organizational classification, each of the issues raised by the provinces was assigned to one of the departments according to their relationship with each of the organizational departments of the SCWMRI. In the subject classification, in the first stage, each of the issues raised by the provinces was assigned to three sub-sectors including research or technological, structural, and support, according to the nature of the subject. In the second stage, the issues raised by the provinces were assigned to more detailed categories according to the nature of the subject. Results and discussionThe results indicate that in this survey, 25 out of 32 provincial centers (78% participation) have announced 182 cases as problems in the research department in soil conservation and watershed management. Thematic classification of the announced instances showed that the subjects of knowledge-based productivity of watershed resources, watershed monitoring and evaluation, and solving the gap in the research department with implementation have the most importance with 28, 26, and 25 cases (15%, 14%, and 13%), respectively. The results of organizational classification also indicate that the departments of the directorate and research departments of watershed management and hydrology and water resources development have the most importance with 34, 28, and 24 cases (19%, 15%, and 14%), respectively. ConclusionBased on the results of this research, the managers and decision-makers of the research and implementation departments of soil and watershed protection in the country will be able to have better targeting for their future policies, strategies, and actions.
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.
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
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 ...
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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.
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.
Mohammad Gheitury; Mosayeb Heshmati; Yahya Parvizi; Mahmoh Arabkhedri; Mahmod Tabatabaei; Khosroo Shahbazi
Abstract
Now a day, carbon sequestration is an important issue due to its serious role on global warming. The aim of this research was to evaluate mechanical measure of check dams on vegetation cover and soil carbon storage in watersheds of Kermanshah Province, Iran. These check dams were constructed in the drainage ...
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Now a day, carbon sequestration is an important issue due to its serious role on global warming. The aim of this research was to evaluate mechanical measure of check dams on vegetation cover and soil carbon storage in watersheds of Kermanshah Province, Iran. These check dams were constructed in the drainage systems to reduce surface runoff velocity and optimize channel slope. Small sedimentary dams are made by gabions and dry structures. The soil and vegetation characteristics of the areas under mechanical operation and its control (severe grazing and grazing management) by field survey in selected sites of Gilan Ghab, Kangavar and Sarfirozabad. The plant biomass including canopy cover and plant root as well as plant litter were samplled along transect path using the quadrat plot. 36 soil sampls were collected from 0-20 cm of soil depths and were air dried and sieved through two milimeter mesh and analyzed in the soil laboratory. Soil organic carbon was measured by the Walkley and Black method and statistical analysis was carried out using SPSS software (version 19). Results showed that both mechanical (check dams) contributed to store 49.28 tonha-1 of carbon which was significantly lower than biological measures. It was concluded that vegetation cover has the most effects on carbon sequestration of the rangelands compared to mechanical methods.
Adele Alijanpour Shalmani; Alireza Vaezi; Mahmoudreza Tabatabaei
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 ...
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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.
Amin Salehpour Jam; Mahmoudreza Tabatabaei; Amir Sarreshtehdari; Jamal Mosaffaie
Abstract
Investigation of drought event has a great importance in the natural resources management and planning water resources management. In this research, the drought characteristics in the selected synoptic stations in northwest of Iran, including Ardebil, Khoy, Oroomieh, Tabriz, Zanjan, Sanandaj and Saghez ...
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Investigation of drought event has a great importance in the natural resources management and planning water resources management. In this research, the drought characteristics in the selected synoptic stations in northwest of Iran, including Ardebil, Khoy, Oroomieh, Tabriz, Zanjan, Sanandaj and Saghez were investigated using the Deciles Index. First, after rebuilding monthly missing data in the period of 1977-2014, time series of precipitation in each station normalized using Box-Cox Transformation. Then, the Deciles Index in different time scales of monthly, seasonal and yearly was calculated based on the normalized monthly precipitation of stations in the period of 1977-2014 by MATLAB and DIP softwares. Then, the drought characteristics, including intensity, duration and frequency were calculated in each synoptic station. In this research, to find the trend of precipitation in the 38 years, 1977-2014, First, trends of the selected stations were determined using Mann-Kendall trend test at the different time scales of the 19 year period, 1977-1995, then compared with trend of the 38 years and finally, determined the changes of the mean precipitation values in two subsequent periods, 1977-1995 and 1996-2014. Obtained results show that the mean annual precipitation at the all stations was decreased in the second period than base period. The results also show that the drought occurrence with different intensity, duration and frequency occurred in selected synoptic stations in northwest of Iran. The results also indicate that there is the decreasing trend of precipitation at the synoptic stations of Saghez and Sanandaj in yearly time scales. Although the stations of Saghez and Zanjan have no trend in the period of 1977-1995, they have decreasing and increasing trends in some time scales in the period of 1977-2014.
Amin Salehpour Jam; Amir Sarreshtehdari; Mahmoudreza Tabatabaei
Abstract
Consciousness of preventing factors affecting on stakeholder participation in watershed areas' plans is a main principle in realization of effective participation of stakeholder and obtaining integrated watershed management goals. There are twelve watershed areas surrounding the city of Tehran that their ...
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Consciousness of preventing factors affecting on stakeholder participation in watershed areas' plans is a main principle in realization of effective participation of stakeholder and obtaining integrated watershed management goals. There are twelve watershed areas surrounding the city of Tehran that their runoff influence on northern and northeast parts of Tehran. Different factors can prevent stakeholder' participation that they have been classified into economic, social, human and planning indices. Obtained results from prioritizing effective indices on preventing stakeholder' participation in watershed plans based on obtained weights from Fuzzy-AHP method and questionary data obtained from Administration of Natural Resources and Watershed Management and its branches indicate that economic and human indices have respectively maximum and minimum priorities, in the manner that prioritization of indices based on normalized weights from maximum to minimum importance are economic, planning, social and finally human, respectively. In this research, preventing sub-indices affecting on stakeholder' participation in watershed area´s plans were created based on library studies, expert ideas and also interviewing with 240 residences and finally the validity of questionary was verified based on expert ideas. Obtained results with ranking sub-indices based on the Friedman test show that sub-indices have different roles in preventing stakeholder' participation in watershed plans, in the manner that “ignoring people´s income as a direct economic motivation” with mean rank of 10.77 and “being low literacy and knowledge” with mean rank of 1.80 have a maximum and minimum of mean rank, respectively. In this research, sub-indices of “ignoring people´s income as a direct economic motivation” from economic index, “concentration of decision making power in center” from planning index, “shortage of trust to project results” from social index and finally “shortage of education of watershed residents about plants and their purposes” from human index were ranked as the most important sub-indices.
