Elham Mehrabi Gohari; Roghaye Shahriyaripour; Ahmad Tajabadipoor; Seyed Roohollah Mousavi
Abstract
This study aims to evaluate and compare the efficiency of Artificial Neural Network (ANN), Regression Tree (RT) and Neuro-Fuzzy (ANFIS) models using a digital soil mapping framework to predict soil texture in a part of Sirjan province. Sampling was carried out at 84 observation points with a regular ...
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This study aims to evaluate and compare the efficiency of Artificial Neural Network (ANN), Regression Tree (RT) and Neuro-Fuzzy (ANFIS) models using a digital soil mapping framework to predict soil texture in a part of Sirjan province. Sampling was carried out at 84 observation points with a regular grid of 2x2 km, and soil texture components were determined from the soil surface depth of 0 to 30 cm. Auxiliary variables included primary and secondary derivatives of the digital elevation model (DEM), a geomorphological map and remote sensing (RS) spectral indices. The appropriate variables selected using the Principal Component Analysis (PCA) feature selection method. Based on PCA, eight topographic variables and six vegetation indices and spectra from RS selected to predict soil texture components (sand, silt and clay). The efficiency of the models was evaluated using coefficient of determination (R2), mean error (ME), root mean square error (RMSE) and normalised root mean square error (nRMSE). The RMSE values in the neuro-fuzzy model compared with the regression tree model. The results of the neuro-fuzzy model were 1.43% for clay, 1.98% for sand and 2.1% for silt, which were 4.32%, 5% and 4.54% lower respectively compared to the regression tree model. The results of this study showed that the ANFIS model was more accurate in predicting clay, silt and sand compared to ANN and RT. Also, the geomorphology map, topographic wetness index, multi-resolution valley bottumn flatness index and Landsat 8 bands 5 and 6 had the highest relative importance in predicting soil texture components.
Mohammad Reza Sayadi; Mehdi Ghorbani; Mohammad Jafari; Hamidreza Keshtkar; Leila Avazpour
Abstract
The objective of this paper is to identify the factors affecting the medicinal plant supply chain in the Nadushan region using a Glaser approach. The research method is applied in terms of purpose and qualitative in terms of method based on grounded theory and Glaser approach (emerging approach); and ...
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The objective of this paper is to identify the factors affecting the medicinal plant supply chain in the Nadushan region using a Glaser approach. The research method is applied in terms of purpose and qualitative in terms of method based on grounded theory and Glaser approach (emerging approach); and it is exploratory based on the nature of the data and the use of inductive philosophy. The study population consisted of experienced local people and managers and experts in the field of the medicinal plant supply chain with more than five years of experience. Participants were selected using purposeful sampling and theoretical judgment. The data collection method was fieldwork, and the data collection tool was in-depth and structured interviews with 30 participants, including native farmers (15), researchers and experts (10), and intermediaries (5) in the field. The grounded theory approach was used to analyze the data and identify the key factors affecting the supply chain. The results identified 9 selective codes and 41 core codes. The factors affecting the supply chain include climate and weather, the region's high potential for medicinal plant cultivation, initial budget and capital, storage conditions, institutional support, policy, medicinal plant production and harvesting management, medicinal plant processing management, and the use of healthy practices in productivity. Therefore, ensuring a sustainable and efficient supply chain is crucial for maintaining the quality, availability, and affordability of medicinal plants.
Zeinab Sheikhi; Aliakbar Nazari Samani; Haji Karimi; Reza Bayat
Abstract
Gully erosion is a typically threshold process which is important in land degradation and sediment contribution. Having knowledge on driving conditions and affected lands by gullies are crucial for land degradation management. In this research to prepare the gully erosion map over Iran land mass about ...
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Gully erosion is a typically threshold process which is important in land degradation and sediment contribution. Having knowledge on driving conditions and affected lands by gullies are crucial for land degradation management. In this research to prepare the gully erosion map over Iran land mass about scientific references (article, thesis and report) were investigated in order to scrutinizing of spatial data base and gullying map. Location of 2719 gully headcuts under different climate and land use were identified. Environmental attributes including: soil, climate, rainfall and temperature were collected through using of DEM (12.5 and 30 m), and landuse by using of maps, scientific reports, research studies were extracted. The frequency of gullies was investigated in relation to literature investigation. Topographic threshold conditions were determined and the relative frequency of gully area under mentioned environmental factors were analyzed. The results show that the total gully area in Iran is about 1,328,852 ha. The most occurrence of this erosion is in semi-arid climates, with annual rainfall is 250-350 mm, high silt content, low slope (<5%) and under dry farming landuse. The lowest threshold coefficient () in both arid and semi-arid climates is related to ranglands. The coefficient varies from 0.06 to 0.37 in arid climate and from 0.002 to 0.46 in semi-arid climate, which is indicating of mixing process (surface and subsurface) on gully developing. The resistance of sandy soils (due to permeability) and clay soils (due to cohesion) is higher than silty ones The degradation of canopy cover and runoff generating are two main driving forces, which will be more important under climate change in the near future.
Nazanin Akbari mohammadabad; Mojgan sadat Azimi; Hassan Yegane; Mostafa Khoshal Sarmast; Elham Malekzadeh
Abstract
The symbiosis of plants with soil microorganisms leads to the preservation and stability of soil and plant species. Arbuscular mycorrhizal (AM) fungi can enhance the reproduction, growth, health, and resistance of plants to various environmental factors. Overturned tulip is a pasture plant and one of ...
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The symbiosis of plants with soil microorganisms leads to the preservation and stability of soil and plant species. Arbuscular mycorrhizal (AM) fungi can enhance the reproduction, growth, health, and resistance of plants to various environmental factors. Overturned tulip is a pasture plant and one of the valuable genetic resources of the country. The aim of this research was to investigate the symbiotic relationship of yellow overturned tulip by AM fungi and the correlation of spore abundance and root mycorrhizal colonization with some physical and chemical properties of soil. For this purpose, 30 soil samples were collected along with plant roots from the habitat of F. raddeana, then some soil properties were measured. After staining the roots and spores extraction from the soil samples, root colonization rate and spore numbers of AM fungi were also measured. Also, the correlation of some soil properties with these fungal factors was investigated. The soil of the plant habitat was silty loam, non-salin, slightly alkalin with moderate content of organic matter and adequate level of N, P and K nutrients. By observing different fungal structures in the plant roots, there was a symbiotic relationship (average colonization of 50%) of AM fungi with the plant and it indicated the mycorrhizal dependency of the F. raddeana in the habitat conditions. The correlation between the frequency of fungal spores and the root colonization was statistically significant at the level of 0.01. Also, no significant relashioship was observed between root colonization and spore numbers with soil physical and chemical properties.
Bahram Bakhtiyari; Arash Malekian; Ali Akbar Nazari Samani; Reza Shahbazi
Abstract
Land subsidence is a complex geomorphological phenomenon characterized by the vertical downward movement of the Earth's surface, with far-reaching implications for ecosystems, human infrastructure, and the environment. This study aims to analyze the relationship between groundwater level decline and ...
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Land subsidence is a complex geomorphological phenomenon characterized by the vertical downward movement of the Earth's surface, with far-reaching implications for ecosystems, human infrastructure, and the environment. This study aims to analyze the relationship between groundwater level decline and land subsidence in the Tehran-Shahryar plain, using measured data. The research methodology is based on advanced remote sensing techniques, including Interferometric Synthetic Aperture Radar (InSAR) using Sentinel-1 satellite images, and analysis of groundwater level data using statistical methods. A key innovation of this research is the simultaneous use of all available piezometers in the study area and the calibration of radar data with accurate ground leveling measurements. The findings of the study indicate that groundwater level decline is the main trigger of subsidence in the region. Cross-correlation analysis of the data reveals a time lag of 0 to 3 years, with an average of 1 year, between groundwater level decline and the occurrence of subsidence across the entire area. This time lag is due to complex geotechnical processes in the water-bearing sedimentary layers, especially in areas with complex geological structures. Spatial-temporal analyses show that the Tehran-Shahryar plain is facing a high potential for subsidence, with average subsidence rates exceeding 23 mm per year in some areas. This trend can pose serious threats to critical infrastructure, engineering structures, and the region's ecosystem. The implications of this study highlight the importance of integrated groundwater management, controlled extraction, and continuous monitoring of geodynamic changes. Moreover, the results can provide a suitable scientific basis for making major decisions in water resources management and mitigating environmental hazards.
Alireza Sepahvand; Nasrin Beiranvand; Negar Arjmand
Abstract
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 ...
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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.
Hassan Yeganeh; Fatemeh Mobarhen; Fatemeh Mehdi Gholian; Sadegh Atashi
Abstract
Prickly pear plays a very important role in arid and semi-arid ecosystems due to its high ability to withstand adverse weather conditions and soil protection. This study aims to investigate some morphological and phytochemical characteristics of different parts of the Opuntia stricta plant in the Neka ...
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Prickly pear plays a very important role in arid and semi-arid ecosystems due to its high ability to withstand adverse weather conditions and soil protection. This study aims to investigate some morphological and phytochemical characteristics of different parts of the Opuntia stricta plant in the Neka and Anbaralum regions. The sampling of different organs was done randomly in 2019. Some characteristics of the plant include fruit and cladode size, vitamin C and acidity, measurement of pectin from skin and fruit flesh, measurement of chlorophyll and carotenoid, anthocyanin of skin and flesh, measurement of soluble solids (TSS) from plant organs using calipers. , spectrophotometer, and refractometer were measured. The results showed that the amount of dry weight of the seed, the amount of electrical conductivity (EC), the number of bases, the number of fruit, and the number of cladodes are significant at the level of 5 and 1%, and the amount of dry weight of the seed in Neka region (1.71 grams) is more than Anbar Alum. 1.3) is warm. Also, the amount of electrical conductivity (EC) in the Neka area (3.78 decisiemens/cm) was measured higher than in Anbaralum (6.34 decisiemens/cm). On the other hand, the number of stems, number of fruits, and number of cladodes per unit area were measured in the Neka region (1.7, 51, 111.16) more than Anbaralum (1.33, 11.03, 36.4). It can be said that climatic conditions have an effect on its growth parameters and fruit production, and the most important factor is access to water.
Masoud Salari; Fereydoon Sarmadian; Ali Salajegheh
Abstract
Wild sheep (Ovis orientalis) represent a critical component of wildlife biodiversity in Iran, categorized as Vulnerable (VU) on the IUCN Red List. This species plays a crucial role in the integrity of rangeland ecosystems and contributes to the maintenance of ecological balance within their habitats. ...
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Wild sheep (Ovis orientalis) represent a critical component of wildlife biodiversity in Iran, categorized as Vulnerable (VU) on the IUCN Red List. This species plays a crucial role in the integrity of rangeland ecosystems and contributes to the maintenance of ecological balance within their habitats. Variations in land characteristics including climate, topography, soil, vegetation, hydrological factors and land use, result in the delineation of distinct land suitability classifications for this species. The present study was undertaken through a longitudinal observation of wild sheep behavior over a decade, with the objective of identifying the paramount factors impacting habitat suitability for wild sheep and generating a habitat suitability map employing machine learning algorithms alongside the Analytical Hierarchy Process (AHP) within Khabr National Park. The findings indicated that the region possesses relatively high suitability for this species; Elevation, slope, vegetation cover, and proximity to water resources were as the most important factors affecting the land suitability for wild sheep. The validation of the results through the kappa coefficient and the overall accuracy index demonstrates a high level of precision in the findings, thereby emphasizing the significance of employing machine learning models integrated AHP in land suitability evaluation studies and aiding management in understanding the species’ ecological requirements while also identifying areas of conservation priority
Sajjad Khalafi Asl; Behzad Moteshaffeh
Abstract
The main problem in sustainable utilization of natural resources is understanding the complex situation and relationships of the various stakeholders involved in the implementation of projects. In this regard, social network analysis is one of the sociological approaches to investigate organizational ...
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The main problem in sustainable utilization of natural resources is understanding the complex situation and relationships of the various stakeholders involved in the implementation of projects. In this regard, social network analysis is one of the sociological approaches to investigate organizational relationship. In this study, the social network of information exchange and participation of eleven organizations involved in the management of natural resources of Behbahan county was examined. first questionnaires were distributed among the organizations and rated on a Likert scale based on the presence or absence of connection. Then, using the Ucinet 6.528 software, the indicators at the macro, intermediate and micro levels of the networks were calculated. The results showed that the density of information exchange and participation networks was medium and low, respectively, while in both links, the reciprocity and transitivity indices were evaluated as very high and high. The average geodesic distance index for both links was in the optimal range, indicating the desired speed of information circulation and participation in the network. Also, in both networks, the Agriculture Jihad, Natural Resources, Water, Rural Cooperatives, Governorate and Tribal Affairs organization were identified as the main and central actors, and the Environment, Meteorology, Cooperatives, Labor and Social Welfare organizations and Khatam Alanbia University of Technology, Behbahan, and Behbahan Agricultural research Station were identified as and peripheral actors. At the level of stakeholders ‘network, the governorate generally has high authority and influence with the highest amount of centrality and also has the highest role of control and mediation in the network.
Farzaneh Parsaie; Ahmad Farrokhian Firouzi; Masoud Davari; Ruhollah Taghizadeh-Mehrjardi
Abstract
Surface soil saturated hydraulic conductivity (Ks), as one of the most important physical properties of soil, plays a key role in the distribution of water and nutrients within the soil environment and holds particular significance in water and soil resource management. This study aimed to digitally ...
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Surface soil saturated hydraulic conductivity (Ks), as one of the most important physical properties of soil, plays a key role in the distribution of water and nutrients within the soil environment and holds particular significance in water and soil resource management. This study aimed to digitally model Ks using machine learning approaches in the Kilanah watershed, located in Kurdistan Province, covering an area of 12,000 hectares. Three machine learning algorithms, including Gradient Boosted Decision Tree (XGBoost), Random Forest (RF), and k-Nearest Neighbors (k-NN), were utilized, incorporating various environmental variables derived from the digital elevation model and Sentinel-2 satellite imagery. These variables included distance from the drainage channel, valley depth, relative slope position, channel base level, brightness index, wind effect index, Normalized Difference Vegetation Index (NDVI), Band 12, greenness index, and surface curvature. Additionally, soil parameters such as organic matter, lime content, bulk density, geometric mean particle diameter, soil texture, and near-soil spectroscopic data (Latent Variable) within the wavelength range of 400–2450 nm were used as proxies for pedogenic factors to model saturated hydraulic conductivity. The results indicated that the XGBoost model exhibited the highest accuracy for predicting Ks, with an R² value of 0.65 and an nRMSE of 0.25, outperforming the other models. Spectral data, topographic variables, and soil parameters, as model inputs, played a significant role in predicting the spatial variability of Ks. The XGBoost model was able to provide highly accurate predictions. The results demonstrated that topographic, physical, and spectral variables influence Ks; organic matter, soil texture, and topographic indices such as slope and relative position had the most substantial impact. The generated maps can be utilized for water and soil resource management and hydrological models.
Ziba Maghsodi; Hamid Reza Matinfar; Seyed Roohollah Mousavi
Abstract
The scale of environmental variables is one of the most important features to consider when selecting data. The aim of this study is to improve the accuracy of digital mapping by selecting the optimal scale for predicting six soil properties, For this purpose, 100 surface soil samples (0-30 cm depth) ...
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The scale of environmental variables is one of the most important features to consider when selecting data. The aim of this study is to improve the accuracy of digital mapping by selecting the optimal scale for predicting six soil properties, For this purpose, 100 surface soil samples (0-30 cm depth) were collected based on a random sampling pattern. Environmental variables related to topography and remote sensing were extracted from the digital elevation model (DEM) and Landsat-8 satellite. The optimal environmental variables were selected using the recursive feature elimination method in the Silakhor Plain region. Soil property modeling was conducted using machine learning models such as random forest (RF), Support Vector Regression (SVR), Cubist (CB), and hybrid modeling. The modeling results showed that the RF model performed best for predicting CCE, pH, sand, and silt with R² values of 0.64, 0.65, 0.59, and 0.70, respectively. Additionally, the SVR model showed the highest accuracy for predicting SOC with an R² of 0.62, while the CB model had the highest accuracy for predicting clay with an R² of 0.66. The most suitable cell sizes for CCE, pH, sand, and silt were identified as 30*30 m, for SOC as 60*60m, and for clay as 90*90m. The most important environmental variables for SOC, pH, silt, sand, and clay were valley depth, differential vegetation index, and modified vegetation index, respectively. Overall, the results indicated that in the study areas, the use of intermediate scales (cell sizes of 30 to 90 m) led to higher accuracy in predicting soil properties. This is because using larger cell sizes introduces noise that hinders accuracy.
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