Alireza Sepahvand; Nasrin Beiranvand; Negar Arjmand
Abstract
Water quality (WQ) is influenced by various variables, including natural ones like rainfall and erosion and human ones like urban, agricultural, and industry operations, that plays a very important role in assessment and determining factors such as environmental conditions, public health, economic and ...
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Water quality (WQ) is influenced by various variables, including natural ones like rainfall and erosion and human ones like urban, agricultural, and industry operations, that plays a very important role in assessment and determining factors such as environmental conditions, public health, economic and social progress and development. Therefore, temporal and spatial trending of water quality is necessary for planning water resource management. In this research, the performance of the six soft computing techniques, including, Random Forest, Reduced Error Pruning Tree (REPt), M5P model, bagging RF, bagging REPt and bagging M5P were compared to estimate the water quality index (WQI) in Khorramabad, Biranshahr and Alashtar sub-watersheds, Lorestan province, Iran. At first, based on water quality data, water quality index (WQI) was calculated and ten distinct water quality parameters (2014 to 2023) were used as input variables and WQI as output. Total data set consists of water quality parameters of three sub-watersheds out of which 70% data used to training and 30% data were used to testing phase. Finally, the models were compared with Correlation Coefficient (C.C.), Root Mean Square Error (RMSE), Maximum Absolute Error (MAE), Taylor diagram and Violin plot box. The obtained results suggest that the BM5P is more accurate to estimate the water quality index (WQI) compared to the M5P, ReepTree and Random Forest (RF) models for the given study area. According to the results of the test part of the BM5P model, it has given us the best result, which are the correlation coefficient, the Root Mean Square Error and the Mean Absolute Error 0.99, 0.2, and 0.15, respectively. Also, the Taylor diagram and violin box plot were concluded that BM5P was the most reliable soft computing technique for the prediction of WQI. Finally, the structure of Artificial Intelligence Techniques (AIT) for modeling is very simple and very less time consumable. Thus, the BM5P model can be useful in the water quality index (WQI) modeling not only for accuracy but also for its time-saving and simple structure compared with other models.
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.
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.
Eisa Gholami; Mehdi Vatakhah; Seyed Jalil Alavi
Abstract
Due to the lack of information in most of the watersheds, many researchers attempt to use spatial analysis within Geographic Information System (GIS) in hydrological and Flood Prone (FP) area studies. The present study was designed to compare the efficiency of three models i.e. Support Vector Machine ...
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Due to the lack of information in most of the watersheds, many researchers attempt to use spatial analysis within Geographic Information System (GIS) in hydrological and Flood Prone (FP) area studies. The present study was designed to compare the efficiency of three models i.e. Support Vector Machine (SVM), Generalized Linear Model (GLM) and Generalized Additive Model (GAM) for preparing the flood susceptibility mapping in Guilan province, Iran. For this purpose, slope, aspect, plan curvature, elevation, distance from the river, drainage density, geology, land use, Topographic Wetness Index (TWI) and Stream Power Index (SPI) layers were derived in GIS (ArcGIS and SAGA-GIS). Using 220 flood locations, 70% and 30% out of total flood locations were then used to calibrate and to validate the performance of the models, respectively. The evaluation results of the models accuracy using the area under the curve (AUC) and Kappa indices showed that in terms of AUC, the SVM with 0.835 and the GAM with 0.827, and the GLM with of 0.79 performed very good and good classes, respectively. In terms of Kappa index, the SVM with 0.58, GAM with 0.53 and GLM with 0.48 are performed good and acceptable classes, respectively. Therefore, based on the mentioned indices, the SVM superior to other two models for identifying the flood susceptibility areas.
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.
shahin mohammadi; Hamidreza karimzadeh; saeid pourmanafi; Saeed Soltani
Abstract
Soil is one of the most important production factors that has a great impact on human socio-economic life and the process of soil erosion is one of the environmental issues that threatens the environment, natural resources and agriculture. Spatial and temporal information of the soil loss and soil erosion ...
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Soil is one of the most important production factors that has a great impact on human socio-economic life and the process of soil erosion is one of the environmental issues that threatens the environment, natural resources and agriculture. Spatial and temporal information of the soil loss and soil erosion on the land has a significant role in influencing management practices, soil erosion control and watershed management. Therefore, this study was conducted with the aim of studying the spatial and temporal estimation of soil erosion during 1994, 1999, 2008 and 2015 in the sub-basin of Menderjan with an area of 21100 hectares located in the west of Isfahan province using RS and GIS. In the present study, while conducting field studies, various data and information including the digital elevation model, satellite images, soil, and statistics on rain gauge stations were used as a research tool. Estimation of soil erosion in the study area was carried out using RUSLE Model. The results of this study showed that the amount of soil erosion in 1994, 1999, 2008 and 2015 was 0.001 to 233, 0.001 to 297, 0.001 to 231 and 0.001 to 215 "ton/”ha.year”. The topography factor in the study area with the correlation coefficient of 80% had the greatest effect on the estimation of annual soil erosion by the RUSLE model. This research corroborate the effectiveness of modern GIS technologies and remote sensing in temporal simulation for quantitative, exact, and point-to-point estimates in the whole area to obtain soil erosion content.
Farshad Soleimani; Naser Brumand; ali azareh
Abstract
In recent decades, increasing population growth and development of agriculture have increased groundwater consumption and decreased the quality of groundwater resources of most parts of the country. Given the importance of this issue, present study investigates the spatial and temporal changes in parameters ...
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In recent decades, increasing population growth and development of agriculture have increased groundwater consumption and decreased the quality of groundwater resources of most parts of the country. Given the importance of this issue, present study investigates the spatial and temporal changes in parameters of calcium, magnesium, pH, chloride, sodium sulfate and water in Jiroft plain. The data was obtained from 40 wells in the region of Kerman province over which in 2002-2012 water harvesting and qualitative analysis had been done. In this regard, after normalizing the data, the accuracy of different geo-statistical methods including the Kriging and inverse distance weighted were evaluated and then the map of the spatial zoning was prepared in the software quality parameters ArcGIS9.3 using the best method of interpolation. The results showed that the amount of pH, Sodium, Chlorine, and Sulfate increased but the amount of calcium and magnesium declined. But in general, in 2012the quality of groundwater resources of Jiroft plain decreased compared to 2002 and the trend of changes showed water quality reduces toward the South and West.
Abazar Esmali; Hasan Ahmadi; Mohammad Tahmoures
Abstract
The aim of this research is to describe the development of a methodology based on present knowledge and available data for evaluation of water erosion behavior and risk as well as modeling and estimation of soil erosion, which is compatible for other similar areas of Iran. Accordingly, the conducted ...
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The aim of this research is to describe the development of a methodology based on present knowledge and available data for evaluation of water erosion behavior and risk as well as modeling and estimation of soil erosion, which is compatible for other similar areas of Iran. Accordingly, the conducted research was based on four major types of water erosions including: sheet, rill, channel and riverbank which have considerable role on sediment yields of Baleghli Chay Watershed, Ardebil Province, were separately and spatially studied. In order to determine the inter-effects of effective factors, the study was conducted using stepwise multivariate statistical tests. For each erosion type, an individual model was then presented. In the next step, after determining of relations between sediment yield and environmental factors (fixed & variable) through statistical analyses and selecting of effective factors on erosion and sediment yields, was created an empirical structure for modeling erosion and sediment yields based on MPSIAC erosion model. In formulation of the new model, were used of eight effective factors on erosion in the area. These factors are susceptibility of geological formation, soil erodibility, rainfall erosivity, runoff erosivity, topography, hydrographic drainage, Normalized Difference of Vegetation Index (NDVI) and field conditions of erosion features. In the presented model, with summation of the scores of mentioned eight factors, obtains the M value, which can estimate the amount of erosion and sediment yields of the area, using exponential formula between sediment yield and M values. In addition, in order to obtain the confidence of presented model, it was used in "Nir" catchment for evaluation the precision. The results showed 11 percent difference. With accepting of this error value, the water erosion hazard map of the area was provided and presented using new model.
