Seyed Masoud Soleimanpour; Omid Rahmati; Samad Shadfar; Maryam Enayati
Abstract
Field measurements of soil loss due to gully erosion are very time-consuming and costly, so direct measurement of gully erosion at large scales is a time-consuming, costly, and labor-intensive process. For this purpose, the present study attempted to accomplish this by modeling soil loss due to gully ...
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Field measurements of soil loss due to gully erosion are very time-consuming and costly, so direct measurement of gully erosion at large scales is a time-consuming, costly, and labor-intensive process. For this purpose, the present study attempted to accomplish this by modeling soil loss due to gully erosion using random forest and support vector machine learning models and evaluating their efficiency in the Mahurmilati watershed located in the southwest of Fars province. Field measurements of dimensional parameters of 70 gullies were conducted over four years (2021 to 2024). In the modeling process, 15 environmental factors were considered as independent variables and the rate of soil loss in ditches as the dependent variable, and modeling was performed with a cross-validation approach. The accuracy of the models was evaluated using quantitative criteria such as root mean square error (RMSE), coefficient of determination (R2), root mean square error (RSR), and correlation coefficient (d). The rate of soil loss in gullies during the study period was 15300.94 tons. The results of the model prediction accuracy evaluation showed that the random forest model has better performance than the support vector machine model in terms of evaluation criteria and was introduced as the superior model for predicting the rate of soil loss due to gully erosion. The findings showed that "modeling" can provide valuable services to water and soil conservation management in saving time and money. For this purpose, it is suggested that the use of artificial intelligence-based models and machine learning structures be given more attention in future research.
Omid Kavoosi; Khaled Ahmadaali; Aliakbar Nazari Samani
Abstract
Soil erosion and its consequences, such as soil destruction at the source, silting of rivers and filling of reservoirs of dams, are one of the most important natural hazards in watersheds, which reduce ecosystem durability. To be one of the most important practical solutions to control sedimentation ...
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Soil erosion and its consequences, such as soil destruction at the source, silting of rivers and filling of reservoirs of dams, are one of the most important natural hazards in watersheds, which reduce ecosystem durability. To be one of the most important practical solutions to control sedimentation and reduce peak flow is to build a check dam. Therefore, determining the quantitative variables affecting the volume of the structure is an important factor in determining the construction costs and their effectiveness. The present study was conducted to model checkdam volumes at the level of 100 sub-basins in different provinces of Iran (Alborz, East Azerbaijan, Ilam, Isfahan, Bushehr, Tehran, Qazvin, Fars, Mazandaran, and Hamadan). The database used for modeling includes 27 environmental features extracted in each of 100 sub-basins and the modeling was done using Genetic Expression Algorithm (GEP). The results of modeling showed that the most important characteristics in estimating the volume of checkdam among the 27 characteristics are: precipitation, temperature, TWI index, shape factor, height difference, concentration time, slope, drainage density and NDVI index. The results of estimating the volume of the structures using the nine selected variables showed that the R2, RRMSE and NSE values for the training phase are .088, .035 and 0.92, respectively, and for the test phase, they are 0.91, 0.29 and 0.91, respectively. Also, based on the results, the characteristics of environmental precipitation can be used with great accuracy to estimate the volume of sediment control structures in a short time, and therefore, before their implementation, the related costs were known in order to prioritize the areas.
Milad Momtazi Burojeni; Fereydoon Sarmadian
Abstract
Soil resource management is essential to maintain community production and the environment. Soil is usually used to produce agricultural products and livestock fodder. As a result, the mapping of high-resolution digital maps is crucial for the distribution of soil and soil properties and land management. ...
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Soil resource management is essential to maintain community production and the environment. Soil is usually used to produce agricultural products and livestock fodder. As a result, the mapping of high-resolution digital maps is crucial for the distribution of soil and soil properties and land management. The decision tree model is a widely used method for predicting soil class in digital soil mapping studies. This study aimed to provide a digital soil mapping in four levels of taxonomy using a decision tree with Boost-reinforced C5.0 algorithm using satellite data and digital Elevation Model and geological maps as environmental variables in 41,000 hectares of Abyek Area. This area was identified using randomized gridding of the geographic location of 128 soil profiles and then described, sampled, and classified. In this research, using the principal component analysis method on environmental variables, 20 environmental variables were selected as the representative of stacking factors for modeling. Multiresolution Valley Flatness Index is the most important environmental variable that was selected as input for the model. The results of the overall accuracy of the integrated model for predicting taxonomic levels of the Order, Suborder, great group, and subgroup were shown to be 89%, 85%, 58%, and 58%, respectively. The study also examined the effect of the boosting technique on the tree model, which showed that all taxonomic levels were better predicted by using the boost model than when no boosting was used and boosting resulted in an increase in overall accuracy and kappa coefficient It turned out.
mahshid souri; alireza eftekhari; Zhila Ghorbani; nadia kamali
Abstract
Soil is the most important component of rangeland ecosystems and by preserving it and its characteristics, In the present study, the amount of potassium and phosphorus in the soil of Ghoshchi rangelands of Urmia located in West Azerbaijan province from 2019 to 2021 under the influence of grazing and ...
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Soil is the most important component of rangeland ecosystems and by preserving it and its characteristics, In the present study, the amount of potassium and phosphorus in the soil of Ghoshchi rangelands of Urmia located in West Azerbaijan province from 2019 to 2021 under the influence of grazing and grazing conditions was investigated. In addition, the development and evaluation of an adaptive fuzzy-neural inference model (ANFIS) was presented in order to predict the amount of potassium and phosphorus in the soil and compare its results with the regression model. The mean squared error (RMSE) and the coefficient of explanation (R2) were used to evaluate the regression and inference models. The results of analysis of variance showed that different years and conditions under confinement and grazing had a significant effect on the amount of potassium and phosphorus in the soil, but their interaction was meaningless. The highest amount of soil potassium is related to the year 2021 and the conditions under grazing. While the highest amount of soil phosphorus was related to 2020. In the phosphorus factor modeling section, the ANFIS model with higher accuracy (R2 = 59.5) and less error (RMSE = 0.087) than the regression model (R2=0.38) with more error (RMSE = 0.089) was able to determine the amount of P to predict. Regarding potassium factor, ANFIS model with higher accuracy (R2 = 0.62 and less error (RMSE = 0.017) than regression model (R2 = 0.42) with more error (RMSE = 0.097) was able to measure soil potassium.
Mohsen Bagheri Bodaghabadi; Mohammad Jamshidi; Zohreh Mosleh
Abstract
Soil organic carbon (OC) is one of the most important soil properties, especially from an environmental point of view. For this reason, OC modeling and estimating has been highly considered. In modeling, application of pedotransfer functions to estimate soil properties from the other ones have an important ...
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Soil organic carbon (OC) is one of the most important soil properties, especially from an environmental point of view. For this reason, OC modeling and estimating has been highly considered. In modeling, application of pedotransfer functions to estimate soil properties from the other ones have an important place in soil science. Unfortunately, not much attention has been paid to the valuable data that are obtained with the least cost and time in the soil profile description. The aim of this study was to determine the importance of data that obtained from soil profile description to estimate the soil organic carbon in Dehgolan region in Kordestan Province. For this purpose, 30 pedons were excavated and described. Soil samples were collected from different horizons and soil properties such as texture, pH, EC, CCE and gypsum were determined. Modeling was performed in three scenario including laboratory data, data of soil profile description and application of laboratory and soil profile description data simultaneously. The results showed that based on laboratory data, soil organic carbon has a significant relationship with silt and CCE properties with a coefficient of determination about 25% (R2 = 0.25); While, the two soil profile description data of soil color (chroma) and genetic horizon with coefficients of determination about 65% (R2 = 0.65). With compilation of laboratory and soil profile description data the coefficient of determination was also obtained 65%. This level of accuracy clearly shows the value and importance of data related to the soil profile description data.
Mohammad Tahmoures; davud nikkami
Abstract
Erosion and sedimentation phenomena are two inevitable phenomena of watersheds that are subject to complex factors. Identifying these factors and recognizing their effect on erosion and sediment will help in better planning to reduce the damage caused by erosion and sediment in a basin. In this study, ...
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Erosion and sedimentation phenomena are two inevitable phenomena of watersheds that are subject to complex factors. Identifying these factors and recognizing their effect on erosion and sediment will help in better planning to reduce the damage caused by erosion and sediment in a basin. In this study, to determine the factors affecting sedimentation, the Urmia Lake watershed was selected as the study basin. After identifying 30 characteristics affecting the sedimentation of sub-basins of the study area, including hydrological, physiographic, geomorphological, geological and soil characteristics, climate, land use and vegetation as independent variables, the amount of sediment produced in each sub-basin. Was identified as a dependent variable. Using factor analysis, principal component analysis (PCA), cluster analysis and stepwise multivariate regression between selected independent variables and dependent variable using SPSS software Statistical relationship was obtained between sedimentation of sub-basins and watershed characteristics. According to the selected regression model, it is determined that the amount of sediment in the watershed of Lake Urmia to five factors of agricultural land area (rainfed, irrigated and orchards), the area of sub-basins, the total area of erosion and Quaternary structures, average discharge The annual and basin form factor depends on the fact that these five factors control 89% of the sediment production changes in the selected sub-basins, which is significant at the 5% confidence level. In general, the factors affecting erosion and sedimentation of the Urmia Lake watershed can be divided into three groups: human factors and land use change, geology and physiography.
Ali Azareh; Elham Rafiei Sardooi
Abstract
The purpose of this study is to investigate land use changes in the past and predict future land use using land change modeler in Halil River watershed. The detection of land use changes was performed using Landsat satellite images (L5-TM-1991, L7- ETM+-2003 and L8-OLI-2020). Transition potential modeling ...
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The purpose of this study is to investigate land use changes in the past and predict future land use using land change modeler in Halil River watershed. The detection of land use changes was performed using Landsat satellite images (L5-TM-1991, L7- ETM+-2003 and L8-OLI-2020). Transition potential modeling was done using MLP neural network method and eight variables including altitude, slope, aspect, distance to road, distance to river, distance to agricultural lands, distance to urban and Normalized Difference Vegetation Index (NDVI). Finally, the Markov chain was used to predict future land use changes. Investigating the calibration periods using kappa statistics showed that the period of 1991-2020 had the highest accuracy to predict land use for 2041. The results of land use changes indicated that during the calibration period, among the six categories namely rangeland, agricultural land, residential land, barren land, rock and orchard, the highest increase and the highest decrease in area was related to agricultural lands and rangelands by 293.7 and 382.6 km2, respectively. Also, the area of barren lands, orchard and residential lands has increased and rocky lands have remained unchanged. The degradation of rangelands has been more in line with the conversion of these lands into agricultural, orchard and residential lands. Also, the prediction of future land use map (2041) using land change modeler showed that , the area of rangelands will decrease by 201.1 km2 and the area of agricultural lands, residential lands, orchards and barren lands will increase by 158.01, 22.38, 20.2 and 0.53 km2, respectively.
Mohammad Ansari Ghojghar; Masoud Pourgholam-Amiji; Shahab Araghinejad; Banafsheh Zahraie; Saman Razavi; Ali Salajegheh
Abstract
Due to the growing development of meta-models and their combination with optimization algorithms for modeling and predicting meteorological variables, in this research four metaheuristic optimization algorithms of Particle Swarm Optimization (PSO), Genetics Algorithms (GA), Ant Colony Optimization for ...
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Due to the growing development of meta-models and their combination with optimization algorithms for modeling and predicting meteorological variables, in this research four metaheuristic optimization algorithms of Particle Swarm Optimization (PSO), Genetics Algorithms (GA), Ant Colony Optimization for Continuous Domains (ACOR) and Differential Evolutionary (DE) were combined with the adaptive neural-fuzzy inference system (ANFIS) model. The performance of four combined models developed with ANFIS model to predict the Frequency variables of Dust Stormy Days (FDSD) on a seasonal scale in Khuzestan province in the southwest of Iran was evaluated. For this purpose, hourly dust data and codes of the Word Meteorological Organization were used on a seasonal scale with a statistical period of 40 years (1980-2019) in seven synoptic stations of Khuzestan province. The results of good fit indices in the training and testing phase showed that there is no significant difference between the ANFIS method and other combined models used. R and RMSE values of the best combined model (ANFIS-PSO) from 0.88 to 0.97 and 0.10 to 0.19, respectively, and in the ANFIS model from 0.83 to 0.94 and 0.11 to 21, respectively, were variable. The results also showed that the combination of optimization algorithms used with the ANFIS model does not significantly improve the results of the model compared to the individual ANFIS model.
ali dastranj; ahmad nohegar; Arash Malekian
Abstract
Abstract Feeding areas, and the spatial distribution of recharge in karstic aquifers, depends to factors such as geomorphology karst and its development, climate, slope, vegetation, soil and geological factors. Karstic aquifers in the study area, has the main role in the development of human civilization ...
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Abstract Feeding areas, and the spatial distribution of recharge in karstic aquifers, depends to factors such as geomorphology karst and its development, climate, slope, vegetation, soil and geological factors. Karstic aquifers in the study area, has the main role in the development of human civilization and supply water to communities. The purpose of this research is modeling the aquifer feeding areas Azhvan-Bisotun in Kermanshah province using KARSTLOP model. The results feed zoning show that the annual charge Bisotun karst aquifer obtained for between 36 to 83 percent. The greatest amount of recharge in areas with more than 78% charged, is accordance sinkholes and the recharge rate of 36 to 40% and 40% to 45% occur in alluvial plains. Bisotun geomorphology karst mountains represent a major role in the spatial distribution of values in the aquifer is recharged.
Sina NabiZadeh; Ataollah Ebrahimi; Masoumeh Aghababaei; Iraj Rahimi
Abstract
The land use of the watersheds is one of the most affected and highly vulnerable due to developmental process which effect on the other variables such as the hydrological function. The purpose of this research is to monitor land use changes in the past and to investigate predictability of its future ...
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The land use of the watersheds is one of the most affected and highly vulnerable due to developmental process which effect on the other variables such as the hydrological function. The purpose of this research is to monitor land use changes in the past and to investigate predictability of its future using Land Change Modeler (LCM) in the watershed of Farsan County of Chaharmahal-va-Bakhtiari province. For this purpose, the Landsat-5 TM images of 1986 and 2009 as well as the Landsat-8 OLI images of 2017 were analyzed. Land covers including residential areas, agricultural land, dryland farming, rangelands, rocks, water bodies, bare-land and snow were classified for the three periods. The prediction of land cover of 2017 was done using the LCM model based on Artificial Neural Network and Markov chain analysis after assessing model’s accuracy based on Kappa index. The land cover of 2027 was also predicted using a change probability table extracted from occurred changes from 1986-2017. The results show that the rangeland decreased by 4379-ha in the years 1986 to 2017, but the agricultural land increased by 1922-ha. This study proved that the LCM could accurately forecast future changes (85% overall accuracy). An increase of 149-ha of residential area and 100-ha decrease of rangelands in the study area was predicted for 2027. Therefore, while emphasizing the conservation of rangelands, it is necessary to study and use this technique to predict changes, its causes, as well as the consequences of land use changes at the broader scales.
