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The purpose of this research was to model areas prone to erosion in the Gol-Mehran catchment in southern Iran. For this purpose, the soil erosion map was determined using membership functions and analytic hierarchy process (AHP) determined the soil erosion map. Additionally, using the self-organizing map (SOM) and principal component analysis (PCA) methods, the most crucial parameters affecting gully erosion were extracted. Finally, soil erosion was predicted using a multilayer perceptron (MLP) and radial basis function. The results of the fuzzy AHP method with all data and the selected data with SOM and PCA demonstrated that areas located in the center of the region were prone to gully erosion. The results of this research also demonstrated that urban lands have expanded significantly, while vegetation has decreased from 1990 to 2019, which has had a significant impact on soil erosion. The results also showed that the MLP model, with R2 = 0.97, could accurately predict soil erosion. [ABSTRACT FROM AUTHOR]

This study focuses on identifying optimal areas for wheat cultivation in the northwestern part of the Khuzestan province- southwest Iran. This research adopts a multi-method approach that integrates Fuzzy logic, the Analytical Hierarchy Process (AHP), and satellite images to facilitate result-driven analysis. The study employs 86 bands and band ratios from Landsat-9, Sentinel-3, Sentinel-2, and fused Landsat-9_Sentinel-2 satellite images to establish correlations (CR) with key agro-ecological parameters relevant to wheat cultivation. The analysis encompasses climatic data (average, minimum, and maximum temperature, and annual rainfall), topographic data (slope percentage), and soil physical–chemical properties (organic carbon percentage, electrical conductivity, exchangeable sodium percentage, pH, gypsum, and soil texture). The choice of Sentinel-2 bands and band ratios is based on their superior CR values, which enable the development of robust regression models. This approach facilitates the generation of precise maps for all pertinent agro-ecological parameters. By overlaying weighted fuzzy maps, the research unveils distinct suitability degrees for wheat cultivation across various regions. The center and northern parts of the study area exhibit highly and moderately suitable classes, while the southeastern and southwestern areas display marginally and permanently not suitable classes. Out of the total land area, 2.29% is highly suitable, 6.51% is moderately suitable, 48.45% is marginally suitable, 36.75 is marginally not suitable, and 6% is permanently not suitable. To validate the findings, the researchers collect field data, which confirms the accuracy of the generated output map. Furthermore, a comparison between the present research method and traditional approaches highlights the superiority of the proposed methodology. [ABSTRACT FROM AUTHOR]

The study aims to monitor air pollution in Iranian metropolises using remote sensing, specifically focusing on pollutants such as O3, CH4, NO2, CO2, SO2, CO, and suspended particles (aerosols) in 2001 and 2019. Sentinel 5 satellite images are utilized to prepare maps of each pollutant. The relationship between these pollutants and land surface temperature (LST) is determined using linear regression analysis. Additionally, the study estimates air pollution levels in 2040 using Markov and Cellular Automata (CA)-Markov chains. Furthermore, three neural network models, namely multilayer perceptron (MLP), radial basis function (RBF), and long short-term memory (LSTM), are employed for predicting contamination levels. The results of the research indicate an increase in pollution levels from 2010 to 2019. It is observed that temperature has a strong correlation with contamination levels (R2 = 0.87). The neural network models, particularly RBF and LSTM, demonstrate higher accuracy in predicting pollution with an R2 value of 0.90. The findings highlight the significance of managing industrial towns to minimize pollution, as these areas exhibit both high pollution levels and temperatures. So, the study emphasizes the importance of monitoring air pollution and its correlation with temperature. Remote sensing techniques and advanced prediction models can provide valuable insights for effective pollution management and decision-making processes. [ABSTRACT FROM AUTHOR]

Rosmarinus officinalis is a widely cultivated plant with various medicinal and culinary uses. However, irrigation with contaminated water can lead to the accumulation of heavy elements in the tissues of this plant. Therefore, the purpose of this study was to investigate the contamination of Rosmarinus officinalis with heavy elements during irrigation with polluted water (wastewater). To achieve this, 39 seedlings were uniformly planted in pots and irrigated with water contaminated with zinc, lead, nickel, and cadmium. The level of contamination in the plant was measured at three vegetative stages using target hazard quotient (THQ), hazard index (HI), and bioconcentration factor (BCF) indicators. In addition, a spectrometer in the range of 400–1030 nm was used to measure the amount of reflection of plants to electromagnetic waves. The K-means method was then applied to investigate the relationship between the morphological characteristics of the plants and heavy metal pollution. The results showed that the highest THQ values were observed in the third vegetative stage (THQPb = 113, THQNi = 0.08, THQZn = 0.25, THQCd = 0.1). Furthermore, the BCFCd and BCFPb indices indicated that the highest contamination levels occurred in the third vegetative stage. The regression analysis identified the spectral bands of 880, 580, 1030, 400, 760, 570, 650, 1050, 560, and 910 nm as the most important for identifying heavy element-contaminated plants. Finally, the K-means method showed high accuracy (R2 = 0.89) for identifying and classifying plant organs affected by pollution from healthy parts. It is worth noting that the investigation of the contamination of Rosmarinus officinalis with heavy elements using electromagnetic waves represents a novel contribution to the field, particularly given the importance of this plant in the pharmaceutical and food industries. [ABSTRACT FROM AUTHOR]

The study aims to predict solar energy generation to ensure the successful operation of solar power plants. This objective is crucial in light of the increasing energy demand, global warming concerns, and greenhouse gas emissions. To achieve this, the study employs multiple linear regression and feature selection techniques to calculate energy generation. Additionally, long short‐term memory (LSTM) is used to predict energy generation levels based on climate conditions. Furthermore, the spatial distribution of energy generation is analyzed using inverse distance weighting. The results of the study reveal that temperature, solar radiation, relative humidity, wind speed, wind direction, and vapor pressure deficit are the most significant parameters for predicting energy generation. The LSTM method proves to be highly accurate in predicting fluctuating energy generation patterns. Notably, the southern regions of the study area exhibit a greater potential for energy generation compared to the northern regions. Approximately 30% of the region generates over 1400 kWh, with the southern areas, characterized by hot and dry climates, producing around 1500 kWh, while the northern regions, with cold and humid climates, generate approximately 1100 kWh. [ABSTRACT FROM AUTHOR]

The research aims to classify alluvial fans' morphometric properties using the SOM algorithm. It also determines the relationship between morphometric characteristics and erosion rate and lithology using the GMDH algorithm. For this purpose, alluvial fans of 4 watersheds in Iran are extracted semi-automatically using GIS and digital elevation model (DEM) analysis. The relationships between 25 morphometric features of these watersheds, the amount of erosion, and formation material are investigated using the self-organizing map (SOM) method. Principal component analysis (PCA), Greedy, Best first, Genetic search, Random search as feature selection algorithms are used to select the most important parameters affecting erosion and formation material. The group method of data handling (GMDH) algorithm is employed to predict erosion and formation material based on morphometries. The results indicated that the semi-automatic method in GIS could detect alluvial fans. The SOM algorithm determined that the morphometric factors affecting the formation material were fan length, minimum height of fan, and minimum fan slope. The main factors affecting erosion were fan area (Af) and minimum fan height (Hmin-f). The feature selection algorithm identified (Hmin-f), maximum fan height (Hmax-f), minimum fan slope, and fan length (Lf) to be the morphometries most important for determining formation material, and basin area, fan area, (Hmax-f) and compactness coefficient (Cirb) were the most important characteristics for determining erosion rates. The GMDH algorithm predicted the fan formation materials and rates of erosion with high accuracy (R2 = 0.94, R2 = 0.87). [ABSTRACT FROM AUTHOR]

The research aims to classify alluvial fans' morphometric properties using the SOM algorithm. It also determines the relationship between morphometric characteristics and erosion rate and lithology using the GMDH algorithm. For this purpose, alluvial fans of 4 watersheds in Iran are extracted semi-automatically using GIS and digital elevation model (DEM) analysis. The relationships between 25 morphometric features of these watersheds, the amount of erosion, and formation material are investigated using the self-organizing map (SOM) method. Principal component analysis (PCA), Greedy, Best first, Genetic search, Random search as feature selection algorithms are used to select the most important parameters affecting erosion and formation material. The group method of data handling (GMDH) algorithm is employed to predict erosion and formation material based on morphometries. The results indicated that the semi-automatic method in GIS could detect alluvial fans. The SOM algorithm determined that the morphometric factors affecting the formation material were fan length, minimum height of fan, and minimum fan slope. The main factors affecting erosion were fan area (Af) and minimum fan height (Hmin-f). The feature selection algorithm identified (Hmin-f), maximum fan height (Hmax-f), minimum fan slope, and fan length (Lf) to be the morphometries most important for determining formation material, and basin area, fan area, (Hmax-f) and compactness coefficient (Cirb) were the most important characteristics for determining erosion rates. The GMDH algorithm predicted the fan formation materials and rates of erosion with high accuracy (R2 = 0.94, R2 = 0.87). [ABSTRACT FROM AUTHOR]

Every year, droughts cause a lot of damage in arid and semi-arid regions. Based on drought indicators, the study aimed to determine and predict drought in southern Iran. In this study, we used Reconnaissance Drought Index (RDI), Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Temperature Vegetation Dryness Index (TVDI) drought indices prepared from MODIS image and Standardized Precipitation Index (SPI), and RDI prepared from 18 meteorological stations from 2000 to 2020 (March to September) in time scale 1, 3, 6, and 9, 12-months. In addition to Principal component analysis (PCA), the most relevant remote sensing drought indices are also selected using meteorological drought indices. The drought situation for the region is also forecast using the Markov and CA-Markov methods. Further, the multilayer perceptron (MLP) method is used to predict drought and replace meteorological drought indices with remote sensing drought indices. It also examined how drought conditions relate to landforms in the study area. the results of indices from 2000 to 2020 showed that currently, the drought situation is more severe in the eastern half of the study area. According to PCA results in the study area, SPI and TVDI are the most relevant drought and meteorological indices to investigate the drought. SPI drought index can also be predicted using the MLP method using TVDI with an accuracy of about 0.8. Based on the CA-Markov the study area will suffer from drought in 2040. So that 34% of the area is classified as arid in 2040. According to the results of the relationship between landforms and drought indices, ridges and peaks have the least drought compared to other landforms. So, it is clear that the region is at risk of drought in the future, but if management measures are taken, damage should be prevented. [ABSTRACT FROM AUTHOR]

BACKGROUND: In this study, the quality of land suitability for vine cultivation in south‐western Iran was evaluated and a land suitability map for vine cultivation was developed using the fuzzy‐analytic hierarchy process (AHP). The best harvest time of vines was determined based on the relationship between total soluble solids (TSS) and titratable acidity (TA) with fruit colour (red, green, and blue or RGB) in ten points and also the growing degree‐days (GDD) maps from April to December. The relationship between GDD and effective parameters in vine cultivation was determined using principal component analysis (PCA) and Pearson correlation methods. RESULTS: The results illustrated that the maximum temperature and relative humidity (RH) have the greatest effect on vine cultivation and its yield (weight 0.24). The results of the land suitability map showed that central regions have better conditions for growing vines (32%). The measurements of TA and TSS depicted that vines of the northern parts have higher TA and lower TSS and there is a significant relationship between them and fruit colour. The results of GDD maps showed that the harvest time of ruby vine and Gezel‐azm is July and August, respectively. Also, the temperature (Tmean, Tmax and Tmin), wind speed, and GDD were the most important parameters to determine the best location for vine cultivation. CONCLUSION: Determining land suitability for cultivation, the best harvest time, the time of grape ripening, and following the exact time of export and import of vine has a vital role to increase its productivity and services. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Pistachio is an export product with very high economic value in different regions of the world, including Iran. Since each agricultural product requires specific climatic and environmental conditions, land management based on appropriate models, identification, and evaluation is important to determine suitable places for its cultivation to be used with maximum yield. This study aims to identify the ecological potential of pistachio cultivation areas in the south of Fars province, Iran, with an emphasis on climatic parameters. In this regard, the ecological needs of pistachio such as topographic indicators (altitude, slope), geological features, and climatic characteristics (precipitation, relative humidity, and temperature) were prepared. Then, using near and small fuzzy membership functions, a fuzzy map of each of the effective parameters in cultivating this crop was prepared. Also, using the opinions of experts and a pairwise comparison of each parameter, the importance of each parameter for the cultivation of this crop was quantified. Using the weights prepared by the AHP method, land suitability maps were prepared for the cultivation of this crop. Finally, by preparing a map of cold needs from April to October, suitable areas for growing 8 different cultivars of pistachio (Kerman, Peters, Kaleghoochi, Ouhadi, Ahmad Aghaei, Akbari, Fandoghi Ghafouri, Choruk) were identified. The results of AHP and fuzzy methods showed that the central parts of the study area (about 50%) have more potential for the cultivation of this plant. [ABSTRACT FROM AUTHOR]

Development of rural tourism activities improves the quality of life, with many associated benefits for local people. Accordingly, this study aims to prioritize tourism areas in Isfahan province using the multiple‐criteria decision‐making (MCDM) model to provide the best ecosystem services to tourists. Nine rural tourism activities (camel‐riding, hiking, mountaineering, desert tourism, wildlife observation, cycling, canyoning, camping, and horse riding) are assessed based on integrating the priorities of each suitability criteria (fuzzy membership and Analytic Hierarchical Process method) with the different risk levels. The results show that mountaineering (86.48% moderate risk; 91% high risk) and canyoning (70% moderate risk; 97% high risk) represent the best alternatives for development of tourism. According to the results of this research, tourists can make the best decisions about individual activities by determining their priorities based on associated levels of risk. Key Points: Rural tourism activities can be analyzed with different risk levels using the multiple‐criteria decision‐making (MCDM) methodMCDM method is a strong and accurate method to check land suitabilityBetter management can be done in the region by preparing land suitability maps with different risk levels [ABSTRACT FROM AUTHOR]

The southern region of Iran is a dry land zone where the drought is a driving force of land-use changes controlling water resources and aquatic ecosystems, such as lakes and wetlands. This study aimed to predict the 2040 drought condition in the Bakhtegan Lake watershed (Iran) and its correlation with land use changes. Extracting from Landsat and MODIS imagery, we classify land-use classes in 2000–2020 and investigate its relationship with multiple drought indicators through principal components analysis (PCA) analysis. In addition, using the Markov and cellular automata (CA)-Markov chain, the 2040 prediction maps of land-use and drought indices were made. Then, the regression analysis was used to reveal the influence trend of desertification on land-use activities in the future. Finally, the ecosystem services value and changes in agricultural lands between 2000 and 2020 were studied in the study area. The observed results of drought indices and land-use maps in 2000–2020 indicated that the expansion of the drought susceptibility zone causes the increasing areas of salt and bare land in parts of the northwestern and southern region. Based on PCA analysis, Normalized Difference Vegetation Index, Enhanced Vegetation Index, and Vegetation Condition Index were chosen to be input data in the CA–Markov model for drought-indices prediction in 2040. The value of these indices would decrease in 2040, indicating more drought in the region. The land-use prediction results in 2040 found that it will increase the percentage of bare land to 0.91, salt land to 0.89, and decrease 0.93 and 0.9 percentages of agricultural land and water lakes. Regression results showed that land use changes were related to drought indices with high accuracy (R2 = 0.93). In addition, the unprincipled use of agricultural lands and their conversion into saline and barren lands has led to a decrease of more than one million and nine hundred thousand dollars annually in the ecosystem services value. [ABSTRACT FROM AUTHOR]

The spatial distribution of fig trees infected by Zaprionus indianus (ZI) disease, an invasive pest, was analyzed as a control solution to determine the prone area of their growth and cultivation prevention in Southwest Iran. With this aim, the study presented the use of 9 suitability variables for fig tree cultivation mapping in 3 main steps: (i) pre-processing data of each input variable with fuzzy membership function, (ii) land suitability mapping (LSM) by using the pair-wise comparison matrix of analytical hierarchy process (AHP) method and Geographical Information System (GIS) technique, (iii) exclusion layers of Zaprionus indianus from the temperature data and growing degree days (GDD) (from April to October) with the support of inverse distance weighting (IDW) method. The results show that the central regions and parts of the east and northwest of the region (16%) are more suitable for fig cultivation. Compared to 7 growth periods, the insect is more active in the southern parts of the region than in the northern parts. Therefore, it is possible to cultivate figs with high yield in parts of the region where the land is suitable for growing this crop with the lowest activity of ZI. The overlay results show that the suitability distribution of fig cultivation in high and very high levels is mainly in the central regions (13,300 km2, 10%), parts of the east (5320 km2, 4%), and northwest (2660 km2, 2%) of the region. The proposed approach can be useful for management, planners, and local people in the development of agricultural production areas. [ABSTRACT FROM AUTHOR]

According to the importance of selecting the appropriate index for assessing drought, in this study, the performance of ten drought indices including six meteorological and four remote sensing drought indices for assessing drought conditions in 1, 3, 6, and 12-month time scales at the Fars province was compared. In this research, the climatic data series of ten synoptic stations, actual data series of annual rain-fed winter wheat yield (AWWY) in 25 counties, and data of Tropical Rainfall Measurement Mission (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 to 2020 were used. First, the zoning map of drought indices and AWWY (for each year) was prepared. Then, the 1000 random points over the study area were selected, and based on the data series of these points, the correlation coefficient (CC) between drought indices and AWWY, based on the XI correlation method (XICOR), was estimated for each year. Eventually, the clustering analysis method was used to cluster drought indices (based on estimated correlations). The results indicated that the Vegetation Condition Index (VCI) had the lowest CC with the AWWY in all years. Among remote sensing drought indices, the overall level of the estimated CC between the Temperature Condition Index (TCI), and Precipitation Condition Index (PCI) with the AWWY was more than other indices. Also, among meteorological drought indices, the overall level of the estimated CC between the Modified Standardized Precipitation Evapotranspiration Index (MSPEI), Modified Reconnaissance Drought Index (MRDI), and Modified Standardized Precipitation Index (MSPI) with the AWWY was more than other indices. According to the results, on average, the MSPEI, MRDI, and MSPI indices with the highest CC with the AWWY were the best drought indices, respectively. [ABSTRACT FROM AUTHOR]

With the growth of the population, the development of industrial, agricultural and so on, the amount of gas demand has been increasing steadily. Supplying the required gas energy often involves the construction of new power plants. The construction of power plants has social, economic, political and environmental consequences. Given the impact of power plant location in the cost of production and transmission of energy, environment, and so on, the importance of selecting the right place for the construction of the power plant is obvious. Therefore, in this study, geographical information system (GIS) based fuzzy-analytic hierarchy process (AHP) as one of the multicriteria decision methods and Dempster–Shafer theory (DST) methods are used to determine suitable locations for construction of gas power plant in Esfahan province, southwest of Iran. Different layers including distance to pipeline, distance to road, lithology, distance to stream, elevation, slope, and land use are prepared using ArcGIS. Subsequently, the importance of each factor in determining gas power plant location is defined using membership function. In the fuzzy-AHP method, membership function fuzzy maps for each layer are prepared. The DST model in Matlab is also used. The method was applied with three levels of confidence 99.5%, 99%, and 95% with the results compare. The results with the fuzzy method showed that 9.57% of the study area is unsuitable for the gas power location. On the hand, the results of the DST model showed that unsuitable areas for the DST method occupy 10.36%, 13.01%, and 19.55% of study area for levels of confidence of 95%, 99%, and 99.5%, respectively. [ABSTRACT FROM AUTHOR]

The purpose of the study is to predict drought changes in Dariun, Fars Province, and their impact on water and soil quality. To prepare drought, water, and soil quality zoning maps, Landsat satellite images and the kriging method were used. The fuzzy maps and weights for each parameter were then determined using fuzzy and analytic hierarchy process (AHP) methods. Additionally, cellular automata (CA)-Markov chains were used in order to predict the impact of drought changes on water and soil quality. Using the fuzzy-AHP method, water quality and soil fertility in 2020 were lower compared to previous years, mainly because of land use changes that increased pollution. Based on results of the Markov and CA-Markov chains, approximately 31% of the region will have very poor levels of soil fertility and water quality in 2050. Further, based on remote sensing indicators, it is determined that about 25% of the region will be at high risk of drought in 2050. Thus, if adequate management of the region is not done, the possibility of living in these areas may diminish in the coming years due to drought and deteriorated water and soil quality. [ABSTRACT FROM AUTHOR]

This study aims to propose a hybrid method for suitability assessment with different risk levels to construct solar power plants (CSPPs) in southern Iran. The fuzzy-analytic hierarchy process (AHP) and fuzzy were applied to forecast and determine the suitable location for CSPPs. To extract suitable location maps with different risk levels for CSPPs, ordered weighted averaging (OWA) was implemented. In addition, the best subset regression method was used to determine the most effective factors in CSPPs. Based on the results of the fuzzy-AHP method, 42% of the southern regions of the area was suitable for CSPPs. Based on the results of the OWA method, the most suitable areas were located in the north and south in all of the risk areas with increasing values. The results demonstrated that the FCM and sub-clustering approaches can accurately predict land suitability classes (LSCs) for CSPPs. Moreover, the best subsets regression (BSR) results showed that distance to power transmission line (PTL) and temperature exhibited the strongest correlation. Finally, receiver operating characteristics (ROC) were used to determine the accuracy of these methods. The results showed that the area under the curve (AUC) values were highly accurate (AUCFuzzy-AHP = 85.0%, AUCOWA = 83.0%). [ABSTRACT FROM AUTHOR]

Wind energy is considered one of the most efficient and cost-effective ways to generate electricity, since it has a low environmental impact. So, it is essential to identify the best places to build wind farms that have the lowest impact on human health and the highest performance. In order to determine the appropriate locations for the construction of wind power plants, in the study first, the interpolation maps of the most important parameters for the construction of wind power plants were created. Then, using the analytic network process (ANP) method due to higher accuracy than other weighting methods (the two-by-two comparison of external and internal data), the weight of each criterion was determined by establishing the external and internal relationships between the criteria and sub-criteria. In this study, since the objective was to prepare land suitability maps with different levels of risk in order to further manage the area, the OWA method was used to prepare land suitability maps. Based on the results of the ANP method for weighing each parameter, wind speed and protected areas were the most and least important parameters to build the power plant. According to the results of the OWA method, 0.78 and 0.1% of the area were suitable for building power plants at high and low risk levels, respectively. The study also found that the number of wind turbines that can be built in the region at both high and low risk levels was 422 and 75, respectively. Using the buffer function, the number of turbines for the construction of high-risk power plants was reduced to 284 by using the appropriate distance from residential areas. The ANP and OWA methods were used to prepare several maps for the evaluation of land suitability with different levels of risk, one of which could be used for the construction of a power plant. [ABSTRACT FROM AUTHOR]

This study employs geographic information system (GIS) and remote-sensing techniques to predict drainage morphometry parameters and hydrological processes. A new approach, named genetic landscape evolution (GLE), is used to model drainage based on optimal waterway network states to predict future drainage patterns and morphometry. Following the theoretical balance between the forces of uplift and sedimentation, GLE strives to achieve the minimum total energy expenditure of the system. The predicted stream-network landscapes created using the GLE algorithm have the self-similar tree structure of natural stream networks. Morphometric analysis shows that the basin has 6th-level stream orders in the 30 m DEM and 5th-level stream orders in the GLE algorithm-produced DEM. The drainage density value of the basin for the 30-m DEM was 0.933 km/km2 and for the GLE DEM was 0.877 km/km2. These drainage densities create coarse drainage textures and therefore, this can be used to predict future soil texture. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the morphometry of alluvial fans and predict the soil erosion located in the vicinity of the Sabzevar, Daruneh, Khaf, and Jangal faults in northeastern Iran. First, the morphometric properties of each alluvial fan were determined. In addition to determining their influence on erosion, principal component analysis (PCA) was used to select the most important morphometric factors affecting erosion. Then, the data regarding the important parameters were input into adaptive neural-fuzzy networks (ANFIS) to predict erosion rates and determine the effect of faults on alluvial morphometry. The asymmetric factor (Af), hypsometric integral (Hi), and basin shape (BS) indicate that most of the sub-basins are tectonically active. The results of the PCA revealed that the most important parameters affecting erosion were Af, Pf, Lf, Rf, Vf, Pb, Ab, LC, Lb, Dd, and the geological unit. The ANFIS method showed that among the soil erosion prediction models, the FCM hybrid model had the highest accuracy. It is concluded that morphometric features can be used to predict the erosion processes in the basin. Thus, the Sabzevar, Daruneh, Khaf, and Jangal faults have been more influential on morphometry than other tectonic factors. The results showed that faults were active and affected the morphometry of the watershed. [ABSTRACT FROM AUTHOR]

This study tried to conduct an investigation into the rate of contamination by heavy metals (HMs) in both the soil used in the plantation of the basil (Ocimum basilicum L.) as well as the plant itself. The proposed methodology works by assessing the concentrations of 4 heavy metals, inclusive of Pb, Zn, Ni, and Cd. The target hazard quotient (THQ) and the bioconcentration factor (BCF) were deployed for assessing the rate of contamination by HMs within the plant. The plant samples were also analyzed at different stages of growth (DSG) through inspection of their reaction to electromagnetic waves (EW). The results indicated that the THQ was substantially high for Pb and Zn, indicative of the high contamination of the study samples by the metals thereof. The hazard index (HI) for non-carcinogenic hazards was also measured for the entire HMs at 46.64, denoting a high level of contamination in the basil. BCF results also indicated Cd as the most absorbed contaminant (BCF = 1.88) by the target plant. The optimal vegetation index for assessment of HM contamination in the target plant, on the report of the findings, was identified as PD312. Therefore, utilizing EW, the reaction of contaminated plants in DSG is forecastable. [ABSTRACT FROM AUTHOR]

The present study attempts to assess the threat of water contamination in Persian Gulf by heavy metals (Fe, Cr, Pb, Cu, Zn, Cd, Se, and Ni) and its subsequent effects on five fish species including Scomberomorus guttatus (S. guttatus), Lethrinus nebulosus (L. nebulosus), Brachirus orientalis (B. orientalis), Pomadasys kaakan (P. kaakan), and Scomberomorus commerson (S. commerson). Water and fish samples from fourteen monitoring stations were obtained to determine the concentrations of contaminants in water and fish. Heavy metal pollution index (HMPI) and non-carcinogenic hazard quotient (NHQI) were employed to evaluate contamination level in water and fish muscle. The Kriging geostatistical method was employed to determine the spatial distribution of different heavy metals around petrochemical plants. The highest NHQI values for P. kaakan and B. orientalis species were 1.036 and 1.046, respectively. In both cases, the NHQI values were higher than the maximum allowable value of 1, indicating that both fish species were on the verge of contamination by heavy metals, which in turn makes the consumption of these fish highly hazardous to human health. The lowest NHQI value was observed in S. commerson species at a value of 0.394, indicating its harmlessness to human health. Overall, fish species living within the top 5 m of the water column (S. commerson and S. guttatus) were found to be less contaminated by heavy metals compared to species dwelling near the seafloor (P. kaakan and B. orientalis). Results also indicated the pollution absorption rate in S. commerson and S. guttatus were 0.45 and 3.4 mg/kg-year, while the corresponding values for the B. orientalis and L. nebulosus species were 6 and 2 mg/kg-year, respectively. P. kaakan species showed a pollution absorption rate of 3.2 mg/kg-year. High heavy metal concentrations of 4.8, 10, 9.8, 5.2, 9.4, and 6.7 mg/L were obtained for Cr, Zn, Pb, Ni, Fe, Cu, and Cd, respectively, in water samples obtained from stations nearby petrochemical plants. The HMPI index for the most contaminated stations was ten times that of the maximum allowable limit. Given the intense activity of oil, gas, and petrochemical plants in the Persian Gulf, defining safe fishing areas by management practices similar to contamination zoning maps presented in this study can substantially protect the public health from heavy metal contamination. [ABSTRACT FROM AUTHOR]

Earthquakes have caused considerable economic damage and loss of lives. The ability to determine earthquake-prone areas has utmost importance to crisis management. Given the gravity of the issue, this research proposes to incorporate geographic information system-based fuzzy-analytic network process (FAHP) and fuzzy-analytic hierarchy process (FANP) methods to determine earthquake-prone areas in the southwest of Iran. In addition, this paper is to utilize the feature selection algorithm (FSA) to select the most relevant parameters influencing earthquakes and prepare seismic activity maps. The final maps obtained using the relevant data were compared with maps obtained using the entire data. For this aim, a fuzzy membership function was initially employed to generate a fuzzy map for different layers, after which the analytic network process and analytic hierarchy process methods were utilized for assigning proper weights to each layer and eventually procuring earthquake maps. The input information for the mentioned methods consisted of landscape, slope, elevation, lithology, land use, soil, road, and fault maps. According to the FSA approach, the most relevant parameters include distance to fault (DTF), lithology, slope, and landscape. The results of FANP and FAHP methods utilizing the receiver-operating characteristic curve showed that the FANP method has a higher accuracy for determining earthquake-prone areas. As the areas located in the northern parts of the region are more at risk. In addition, Finally, the results of earthquake maps obtained using solely the relevant data from FSA were compared with the maps obtained using the entire data. The results of FSA showed that the Best first and Greedy-Stepwise models have high accuracy (R = 0.99) and the most important data for determining earthquake-prone areas using these models are DTF, lithology, slope, aspect. Therefore, by saving time and money, it is possible to prepare maps of earthquake-prone areas with high accuracy. [ABSTRACT FROM AUTHOR]

This study aims to determine the effect of a flood-spreading system on the morphometric characteristics of alluvial fans (AFs) in Gribayegan Fasa, Iran, and its relationship with erosion, age, texture, and type of formations. After determining the AFs using the semiautomatic method and determining their recharged watersheds, 25 morphometric characteristics were investigated. The most important morphometric characteristics were identified using principal component analysis (PCA). The group method of data handling (GMDH) neural network is used to predict erosion, soil texture, age, and formation material based on the morphometric characteristics of the fan. The results demonstrate that the semiautomatic method can effectively extract AF from the landscape. The results of AF morphometry before and after flood spreading show that the fan area, drainage basin circularity (Cirb), fan perimeter, relief ratio of the fan, fan length, minimum fan height, and maximum fan height were higher before flood spreading, and basin shape, St (soil texture), upper fan slope, fan volume, and sweep angle had higher values after the flood. In addition, the results of PCA show that fan area, fan perimeter, fan length, fan radius, fan volume, basin area, basin perimeter, main channel length, basin length, and drainage density are important factors. The results of the GMDH algorithm reveal that this method can accurately predict the formation, age, soil texture, and erosion rate using morphometric characteristics. Therefore, the R2 is 0.92 for the formation age and R2 is 0.86 for the erosion rates, formation types, and soil texture, respectively. Therefore, the most important morphometric parameters can be determined using PCA, and the conditions and processes in the basin, such as formation material, age, soil texture, and erosion rate, can be predicted with high accuracy using the GMDH algorithm. [ABSTRACT FROM AUTHOR]

This study aims to investigate the toxicity of the plant to heavy elements (HMs). For this purpose, the estimated daily intake (EDI) parameters of potentially toxic elements (PTE) per kilogram of body weight, target hazard quotient (THQ) for non-carcinogenic disorders, total hazard index (HI), and bioconcentration factor (BCF) are determined in the plant at different stages of growth. In this study, the reaction of the plant to different electromagnetic waves at different stages of growth (DSG) is also investigated, and the relationship between the THQ values and electromagnetic waves is prepared. The results show that Pb has the highest EDI value (5.97), Pb (74.67) and Cd (9.75) have the highest THQ values, and Cd has the highest BCF value (30.44). Also, the results show that HI values are higher than the threshold in the growth (69.98), flowering (71.38), and fruiting (68.06) stages. Results of BCF indicate Pb, and Cd has absorption rate in Capsicum towards. Contaminated Capsicum plants submitted to electromagnetic waves showed a significant relationship between Pb and the b490, and b560 spectra, Cd and Ni the b450 spectrum, and Zn the b460 spectrum. This finding highlights the salience of employing electromagnetic waves in assessing contamination in plants. Put differently, THQ can be estimated using electromagnetic waves without any need for laboratory studies. [ABSTRACT FROM AUTHOR]

This study aims to determine suitable locations for artificial recharge of groundwater (ARG) using the GIS-based analytic hierarchy process (AHP) and order weight average (OWA). To determine the weights of the different parameters, the AHP method of pair-comparison was used after preparing a fuzzy map for each attribute. After that, using the OWA–AHP method for different levels of confidence (different values), the weighting process was used for each parameter to produce land suitability maps of varied risks. In addition, the adaptive network-based fuzzy inference system (ANFIS) method was used to predict land suitability classes using input parameters. Then, using the best subset regression method, the most important effective ARG parameters were identified. Fuzzy-AHP results show that 27% of the area has "good" and "very good" conditions for ARG. Under low-level risk and no trade-off, the combined OWA–AHP method shows that the more area is in the "very low" class (80%) while in case of higher level of risk and average trade-off, the highest values are in the "very low" class (27%). The results of the ANFIS method indicate that both fuzzy c-means (FCM) and sub-clustering methods can be used to predict appropriate places for ARG. The results of the best subset regression method show that slope, lithology, land use, and altitude with the lowest Cp values (5.2) are effective parameters to determine the suitability of ARG locations. [ABSTRACT FROM AUTHOR]

The present study seeks to both estimate and analyze the rate of heavy metal changes in soils of road margins affected by the traffic on Fasa‐Darab Road, Iran. Muller index, contamination factor (CF), Geoaccumulation index (Igeo), potential ecological risk index (PERI), and pollution load index (PLI) were calculated to determine the contamination rate. Principal component analysis was also employed to determine the most relevant parameters affecting the PERI. The maximum amount of heavy metal contamination (2.5 mg/kg) occurred at the nearest road distance, that is, 2 m. The results of the Igeo show that these elements are naturally present in the soil, albeit anthropogenic activity caused an increase in the concentration of these metals in soil. Results of the CF index showed that Ni, Cu, and Zn values were less than 1, indicative of low contamination, whereas Pb contamination, with values ranging from 3 to 6 was indicative of high pollution. The PLI results for the most polluted stations showed that stations 14 and 23 were classified as very high contamination and other stations as uncontaminated. The results of clustering data showed that the data were in two pollution classes. As maintained by the findings, the first component contributed to 44.98% of the changes and included the Cd, CFCd, RI, and Igeo(Cd) parameters, while the second component represented 27.83% of the changes, containing parameters Pb, Igeo(Pb), and CFPb. Therefore, Pb and Cd are the most relevant parameters in the analysis of PERI within the study area. Key Points: Investigation of metal changes in sediments of road margins affected by the traffic on Fasa‐Darab RoadUsing CF, Geoaccumulation index (Igeo), potential ecological risk index (PERI), and PLI to determine amount of pollutionPrincipal component analysis (PCA) was also employed to determine the most relevant parameters affecting the PERI [ABSTRACT FROM AUTHOR]

This paper aims to extract optimal location for cultivating orange trees. In order to reach this goal, a combination of Dempster-Shafer theory (DST) and cloud computing is proposed. The DST method is applied to make weights for input parameters, and cloud computing is used for creating a cost-effective integrated solution on collected information of different geographic regions. To do this, eight parameters including minimum and maximum temperatures, aspect, elevation, growing degree days, rainfall, relative humidity, solar radiation and slope are incorporated to determine the most optimal region for orange cultivation. Moreover, interpolation maps for each parameter are determined with using the inverse distance weighting model in a geographic information system software. The DST model as a novel method for the determination of land suitability is eventually applied in the MATLAB software environment to complete the performance evaluation. Three confidence levels are set as 99.5%, 99% and 95% such that the final results for each confidence level are compared accordingly. It will be shown that the proposed method is successful in predicting suitable locations for the cultivation of oranges by generating different maps at various degrees of confidence. [ABSTRACT FROM AUTHOR]

Iran is located in an arid and semi-arid region of the world; however, more than 90% of Iran's total water is allocated to agricultural sector. Owing to the climatic limitations of the country, agricultural water management has become one of the country's greatest concerns. Therefore, the present study has attempted to identify optimal locations in terms of high yield and low total water footprint (WF) (m3/$) in the northwest and southeast provinces of the country to produce main cereals such as wheat, barley, and maize. In this regard, inverse distance weighting (IDW) method was first employed 150 sample coordinates to prepare interpolation maps of evapotranspiration (ETc) (mm), irrigation requirements (IR) (mm), nitrogen application (kg/ha), and effective precipitation (Peff) (mm). Subsequently, green WF, blue WF, gray WF, total WF (TWF) (m3/ton), and economical total WF (ETWF) (m3/$) maps were prepared for each crop. Multiple linear regression (MLR) was then applied to determine the correlation between ETWF (m3/$) and other parameters. ETWF (m3/$) was later predicted using multiple linear regression (MLR), in conjunction with artificial neural networks (ANNs) including multilayer perceptron (MLP) and radial basis function (RBF). Findings showed that IDW can be applied with a relatively low error so that an interpolation map for each parameter can be obtained and the changes can be examined. The results of MLR indicated that for assessing the yield of barley and wheat the TWF possesses and for assessing the yield of maize, the IR model had the highest rate of regression. The results of ANN indicated that the RBF (R2 = 0.901), the RBF (R2 = 0.882), and the MLP (R2 = 0.947) were the most appropriate models for the prediction and evaluation of ETWF and yield with respect to measured estimates of ETWF (m3/$) and yield of wheat, barley, and maize (in order of appropriability). [ABSTRACT FROM AUTHOR]

Given the tendency of heavy metals to accumulate in soil and plants, the purpose of this study was to determine the contamination levels of Cd, Ni, Pb, and Zn on peppers (leaves and fruit) grown in contaminated soils in industrial centers. For this purpose, we measured the uptake of the four heavy metals by peppers grown in the heavy metal contaminated soils throughout the four growth stages: two-leaf, growth, flowering, and fruiting, and calculated various vegetation indices to evaluate the heavy metal contamination potentials. Electromagnetic waves were also applied for analyzing the responses of the target plants to various heavy metals. Based on the relevant spectral bands identified by principal component analysis (PCA) and random search methods, a regression method was then employed to determine the most optimal spectral bands for estimating the target hazard quotient (THQ). The THQ was found to be the highest in the plants contaminated by Pb (THQ = 62) and Zn (THQ = 5.07). The results of PCA and random search indicated that the spectra at the bands of b570, b650, and b760 for Pb, b400 and b1030 for Ni, b400 and b880 for Cd, and b560, b910, and b1050 for Zn were the most optimal spectra for assessing THQ. Therefore, in future studies, instead of examining the amount of heavy metals in plants by chemical analysis in the laboratory, the responses of the plants to the electromagnetic waves in the identified bands can be readily investigated in the field based on the established correlations. [ABSTRACT FROM AUTHOR]

This study seeks to assess the reaction of the eggplant (Solanum melongena L.) to soil samples contaminated. Following, cultivation, growth, and harvest, the plant samples were prepared and maximum absorption rates of heavy metals were measured in both leaf and fruit. The estimated daily intake (EDI), the target hazard quotient (THQ), and the bio-concentration factor (BCF) were measured at various intervals during the growth period of the plant. Spectral analysis was also performed to assess the reaction of target crops to heavy metals. The results showed that in the second and third stages of plant growth, the THQ values were more than 1 for infected plants with Cd, Pb, and Zn. According to results from the BCF analysis, the absorption rate in Pb, during the growth stages was relatively high, in crops contaminated by Ni was around 1 in the second and third stages, and in plants contaminated by Cd was extremely high. All crops contaminated by heavy metals showed higher reflection rates in the 400–500 and 600–700 nm range. So, using electromagnetic waves during different stages of growth, the reaction of eggplant cultivated in soil samples contaminated by heavy metals is predictable. [ABSTRACT FROM AUTHOR]

In this study, Dempster–Shafer theory (DST) is integrated into a geographic information system to model vulnerability of the land surface to earthquake events in northwestern Kermanshah Province, Iran, to predict where damage is most likely to occur. DST has never been used to spatially model earthquake vulnerability. To achieve this, data layers for several environmental attributes—aspect, elevation, lithology, slope angle, land use, distance from river courses, distance from roads, and distance from faults—were compiled in ArcGIS 10.2.2 software. Using membership functions, fuzzy maps were generated for each parameter. These fuzzy maps provided input data for the DST model. The predicted values were analyzed and compared at three confidence levels to determine the effectiveness of the model. The results are that 11.14%, 14.14%, and 17.18% (95%, 99%, and 99.5% confidence levels, respectively) of the study area are predicted to be susceptible to earthquakes based on receiver operating characteristic curves. The results also show that, according to the area under the curve (AUC) values (0.967, 0.828, and 0.849 for 95%, 99%, and 99.5% confidence levels, respectively), DST model generates earthquake zoning maps with high accuracy. Therefore, this model can be used for generating earthquake zoning maps with confidence levels that best suit the economic conditions and significance of the region. [ABSTRACT FROM AUTHOR]

Considering environmental concerns regarding air pollution which is induced by burning fossil fuels to generate electrical power, utilizing solar energy as a green and sustainable energy source is of great interest. This study proposes a novel framework to determine the optimal location for constructing solar photovoltaic (PV) farms. To locate the suitable areas for PV farms, firstly, a fuzzy-based method is utilized to homogenize the input parameters, thereafter, the analytical hierarchy process (AHP) and Dempster-Shafer (DS) methods are independently used. In the AHP method, the proper weight for each input parameter is generated utilizing a pairwise comparison matrix. However, the DS method identifies output in different confident levels. Finally, southeast of Fars province in Iran as a region with high sunny hours in the year is selected, and the applicability of proposed methods is examined. The results show that 32% of the case study is located at high and good suitability classes in the fuzzy_AHP method. However, it is 18.56%, 16.70%, 16.32% according to 95%, 99% and 99.5% confident levels in the fuzzy_DS method, respectively. Comparisons of the fuzzy_AHP and fuzzy_DS methods at 20 points with various solar radiation intensities and the number of dusty days parameters indicate that the fuzzy_DS method can more reliably determine the optimal PV farm locations. Additionally, as the fuzzy_DS method determines the optimal locations with different confident levels, this method can benefit decision-makers to determine the risks associated with selecting a specific site for constructing solar PV farms. [ABSTRACT FROM AUTHOR]

Groundwater is an essential and major source of water in semi-arid and arid regions, and thus, determining potential locations for groundwater recharge, via prompt and accurate techniques, is necessary. The aim of the research is finding the best artificial recharge location for injecting the excess surface water into aquifer in Shiraz watershed comparing fuzzy-AHP method and Dempster–Shafer theory (DST). The former employs membership functions to determine the importance of each factor in artificial recharge and generates fuzzy maps through analytic hierarchy process (AHP), whereas the later takes the level of confidence, management strategy and geographic conditions into account. The outcomes of this study demonstrate the applicability of the proposed methods for the artificial recharge location prediction. The DST is among the foremost tools, which produces an artificial recharge map at a given level of confidence. For Shiraz watershed, three levels of confident of 99.5%, 99%, and 95% were applied and the results were compared with fuzzy-AHP method. The fuzzy-AHP method identified ~ 19.76% and 6.02% of the study area as highly suitable and unsuitable classes, respectively. While in the DST, the method identified ~ 32% of the area as moderately suitable. The comparison of the two methods to determine the best location for artificial recharge showed that the fuzzy-AHP method had a lower accuracy than DST in determining suitable locations for artificial recharge. Additionally, the DST method can generate several maps with different confidence levels that suit the economic conditions and importance of the study area. [ABSTRACT FROM AUTHOR]

This study seeks to investigate the effect of topographic (slope, elevation, and aspect) and climatic (precipitation and temperature) factors on vegetation in Kharestan region using remote sensing (RS) and geographic information system (GIS) techniques. To this aim, therefore, the changes occurred in such vegetation indices as normalized difference index (NDVI), enhanced vegetation index (EVI), soil-adjusted vegetation index (SAVI), and difference vegetation index (DVI) between 2008 and 2015 were evaluated, using Landsat ETM7. The findings of the study showed that the highest density for the NDVI, EVI, SAVI, and DVI could be found at an elevation range of 2000 to 2800 and the lowest density could be found at elevations lower than 2000 and higher than 2800 m. In addition, the maximum values for the indices at elevation range of 2050 to 2250 were 0.56, 0.54, 0.4, and 0.25 respectively. Moreover, the highest values for the indices were found at such aspects as the northeast (64° and 42°) and the north (348°), while the lowest ones were observed at the eastern (74°) and southwestern (243° and 206°) aspects. As for the density of the vegetation, the maximum mean values for the indices were found to be located at slope range of 4 and 12°. These findings indicate that lower elevations, shaded sides of the domains, and gentle slope enjoy appropriate conditions for the growth of vegetation. Furthermore, there was a significant correlation between the indices, the temperature, and the precipitation (P < 0.01). Considering the aforementioned results, it could be argued that environmental factors such as elevation, aspect, slope, precipitation, and temperature are among the main factors, which control the vegetation in the region studied in this research. [ABSTRACT FROM AUTHOR]

In this research, some characteristic qualities of orange fruits such as vitamin C and acid content; weight; fruit and skin diameter; and red (R), green (G), and blue (B) values of the RGB color model for 70 samples were used to predict the taste of orange grown in Darab, southeast of Fars Province, Iran, by multiple linear regression (MLR) and adaptive neuro‐fuzzy inference system (ANFIS). To use MLR, firstly the most important input data were selected, and then, the best model to predict the taste of orange was applied. In this research, methodology of ANFIS consisted of selection of dependent orange taste, fuzzification, fuzzy inference rule, membership function, and defuzzification process. The predictive capability of these models was evaluated by various descriptive statistical indicators such as mean square error (MSE) and determination coefficient (R2). The results showed that the prediction performance of the MLR model has a strong significant relationship between orange taste and vitamin C (0.897**), red color (0.901**), and blue color (0.713*). Also, the results of ANFIS model showed that with low error for train and check data increased the most accuracy for prediction of orange taste. Moreover, the results indicated that the success rate of taste determination for orange is higher by using ANFIS compared to the MLR. This research was to provide valuable information for orange taste. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of different geological units on groundwater quality of an aquifer in southern Iran. The Kriging interpolation technique with a Gaussian semivariogram model was employed to prepare groundwater maps for different water quality constituents. In the next stage, two different models based on fuzzy analytic hierarchy process (AHP) and Dempster–Shafer theory (DST) were used to evaluate the overall water quality index based on the World Health Organization's drinking water standard in different parts of the aquifer. The DST model was able to generate water quality maps with 99.5%, 99%, and 95% confidence levels. The water quality maps were subsequently compared with the geology map of the area to determine the effects of different soil types on the water quality of the aquifer. Both methods showed poor water quality indices in the areas with an Asmari formation containing elevated levels of chloride and sodium ions. Comparison of water quality maps generated by the fuzzy-AHP and DST model revealed that the DST could more reliably handle the uncertainty in the water quality data, and thus was able to generate more accurate water quality maps. Increasing the confidence level in the DST model yielded water quality maps with a decreased overall water quality index. Results of this study could assist water management practices to generate water quality maps for their groundwater resources with confidence levels commensurate socio-economic importance of the region. [ABSTRACT FROM AUTHOR]

Landslides are as the movement of soil on slopes that they are one of the most common natural hazards in many mountainous areas. Landslides are recognized as an important natural hazard in many countries. So in the study, the geographic information system-based the fuzzy quantifier is used to determinate the landslide susceptibility modeling in the north of Khorramabad, west of Kermanshah Province, Iran. To determine the landslide susceptibility modeling generated aspect, some input data were prepared such as the digital elevation model, lithology, slope, land use, river, road, fault, and precipitation maps. Fuzzy map showed that almost all of the area was medium landslide susceptibility that had the value close to 1. Fuzzy-AHP model showed that 77.62% of the study area had medium landslide susceptibility and this method was a useful tool for forecasting of landslide susceptibility status in each case study. [ABSTRACT FROM AUTHOR]

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Soil fertility refers to the ability of a soil to supply plant nutrients. Naturally, micro and macro elements are made available to plants by breakdown of the mineral and organic materials in the soil. Artificial neural network (ANN) provides deeper understanding of human cognitive capabilities. Among various methods of ANN and learning an algorithm, self-organizing maps (SOM) are one of the most popular neural network models. The aim of this study was to classify the factors influencing soil fertility in Shiraz plain, southern Iran. The relationships among soil features were studied using the SOM in which, according to qualitative data, the clustering tendency of soil fertility was investigated using seven parameters (N, P, K, Fe, Zn, Mn, and Cu). The results showed that for soil fertility there is a close relationship between P and N, and also between P and Zn. The other parameters, such as K, Fe, Mn, and Cu, are not mutually related. The results showed that there are six clusters for soil fertility and also that group 1 soils are more fertile than the other. [ABSTRACT FROM AUTHOR]

Correction to: I Scientific Reports i https://doi.org/10.1038/s41598-023-35634-2, published online 25 May 2023 The original version of this Article contained an error in Affiliation 1, which was incorrectly given as 'Department of Geography, Faculty of Economics, Management and Social Sciences, Shiraz University, Shiraz, Darab, 71379-58756, Iran'. The correct affiliation is listed below: Department of Geography, Faculty of Economics, Management and Social Science, Shiraz University, Shiraz, Iran. [Extracted from the article]

The aim of this study is to perform landform classification in the south of Isfahan, Iran. In order to make landform classification, Digital Elevation Models (DEMs) with 30 m resolution and Topographic Position Index (TPI) were used. Also, the percentage of each landforms classes in geomorphological units was determined. The results show that there were ten classes for the study area that consist of classes of canyons, shallow valleys, upland drainages, U-shaped valleys, plains small, open slopes, upper slopes, hills in valleys, midslope ridges and mountain tops. Also, the results show that the highest area of landform was for the classes of plain small for Playa unit. While for pediment unit, the area of landform was canyons, streams, U-shaped valleys, mountain tops and plains small. It was also determined that the highest area of landform for mountain units was mountain tops, canyons and stream landforms. [ABSTRACT FROM AUTHOR]

In this study, the fuzzy analytic hierarchy process (AHP) is used to study the relationship between drinking water quality based on content of inorganic components and landform classes in the south of Firozabad, west of Fars province, Iran. For determination of drinking water quality based on content of inorganic components, parameters of calcium (Ca), chlorine (Cl), magnesium (Mg), thorium (TH), sodium (Na), electrical conductivity (EC), sulfate (SO4), and total dissolved solids (TDS) were used. It was found that 8.29% of the study area has low water quality; 64.01 %, moderate; 23.33 %, high; and 4.38 %, very high. Areas with suitable drinking water quality based on content of inorganic components are located in parts of the south-eastern and south-western parts of the study area. The relationship between landform class and drinking water quality based on content of inorganic components shows that drinking water quality based on content of inorganic components is high in the stream, valleys, upland drainages, and local ridge classes, and low in the plain small and midslope classes. In fact we can predict water quality using extraction of landform classes from a digital elevation model (DEM) by the Topographic Position Index (TPI) method, so that streams, valleys, upland drainages, and local ridge classes have more water quality than the other classes. In the study we determined that without measurement of water sample characteristics, we can determine water quality by landform classes. [ABSTRACT FROM AUTHOR]

The Multi-criteria Decision Analysis (MCDA) and the Geographical Information Systems (GIS) are used to provide more accurate decisions for decision makers in order to evaluate the effective factors of the natural science. One of the popular algorithms of the multi-criteria analysis is the Ordered Weighted Averaging (OWA). The OWA procedure depends on some parameters which can be specified by means of the fuzzy logic. The aim of this study is to take the advantage of incorporating the fuzzy logic into GIS-based soil fertility analysis by OWA in the west of Shiraz, Fars province, Iran. In fact, different soil fertility maps with different risk level are prepared in the present study. This study introduces a method for farmers in case of make balance between their budget and their farm soil parameters. A farmer can accept more risk it can use more areas for farming and also the amount of needed budget increases too. For determining the soil fertility maps, the OWA parameters such as potassium (K), phosphor (P), copper (Cu), iron (Fe), manganese (Mn), organic carbon (OC) and zinc (Zn) were used. After generating the interpolation maps with the Inverse Distance Weighted (IDW), the fuzzy maps were generated by the membership functions for each parameter. Finally, by utilizing OWA, six fertility maps with different risk levels (degrees of uncertainty) were made. The results show that by decreasing the risk (no trade-off), increasing the risk, more area within the study area was suitable in terms of the soil fertility. Therefore, using OWA can generate many maps with different risk levels. This leads to different managements based on different financial conditions of farmers. [ABSTRACT FROM AUTHOR]

In this study, the Mazandran River watershed is selected for detailed morphometric analysis. Morphometric analysis for parameters of stream number (Nu), stream orde (U ), cumulative length of streams (L), bifurcation rati (Rb), watershed relief (Bb), drainage density (Dd), stream frequency (Rf), form factor (Rf), circularity rati (Rb) and elongation rati (Rb) is carried out to describe the drainage properties of the three subwatersheds in the study area (W1, W2 and W3). The results show that among the subwatersheds, the highest Nu is 333 (stream order 1 for W1), while the lowest Nu is 1 (stream order 4 for W1). For U, among the subwatersheds, W1 and W2 are of order 3, and W3 is of order 4, showing that they are in the early stages of erodibility. W1 has the highest L of streams (5.72 km), whereas W3 has the lowest L of streams (3.11 km). The Rb values for the subwatersheds are between 0.73 to 1.84, whie Dd varies between 0.0055 and 0.0060 km/km2. The results also show that the Fs values for the subwatersheds exhibit positive correlations with Dd (from 0.48 to 0.58). The minimum and maximum form factors (Rf) are observed for W3 (0.58) and W2 (0.93) respectively, indicating W2 is more round, resulting in lower lag time. Rc is in the range of 0.43 to 0.75. The overall results indicate that in the subwatersheds are have high erodibility and need more management. [ABSTRACT FROM AUTHOR]

In this study, fuzzy analytic hierarchy process (AHP) is used to study the relationship between drinking water quality and landform classes in south of Firozabad, east of Fars province, Iran. For determination of drinking water quality, parameters of calcium (Ca), chlorine (Cl), magnesium (Mg), thorium (TH), sodium (Na), electrical conductivity (EC), sulfate (So4) and total dissolved solids (TDS) were used. It was found that 8.29 % of the study area have low water quality; 64.01 %, moderate; 23.33 %, high; and very high, 4.38 %. Areas with suitable drinking water quality are located in parts of the southeast and southwest parts of the study area. The relationship between landform class and drinking water quality show that drinking water quality is high in the stream, valleys, upland drainages and local ridge classes, and low in the plain small and midslope classes. [ABSTRACT FROM AUTHOR]

In this research, the relationship between classes of landform, and electrical conductivity (EC) of soil and water in the Shiraz Plain, Fars province Iran was investigated using a combination of geographical information system (GIS) and fuzzy model. The results of the fuzzy method for water EC showed that 36.6% of the land to be moderately land suitable for agriculture; high, 31.69%; and very high, 31.65%. In comparison, the results of the fuzzy method for soil EC showed that 24.31% of the land to be as not suitable for agriculture (low class); moderate, 11.78%; high, 25.74%; and very high, 38.16%. In the total, the land suitable for agriculture with low EC is located in the north and northeast of the study area. The relationship between landform and EC shows that EC of water is high for the valley classes, while EC of soil is high in the upland drainage class. In addition, the lowest EC for soil and water are in the plain small class. [ABSTRACT FROM AUTHOR]

The Multicriteria Decision Analysis (MCDA) and Geographical Information Systems (GIS) are used to provide accurate information on Pedogenic processes and facilitate the work of decision makers. So, MCDA and GIS, can provide a wide range of decision strategies or scenarios in some procedures. One of the popular algorithm of multicriteria analysis is Ordered Weighted Averaging (OWA). The OWA procedure depends on some parameters, which can be specified by means of fuzzy. The aim of this study is to take the advantage of the incorporation of fuzzy into GIS-based soil fertility analysis by OWA in west Shiraz, Fars province, Iran. For the determination of soil fertility maps, OWA parameters such as potassium (K), phosphor (P), copper (Cu), iron (Fe), manganese (Mn), organic carbon (OC) and zinc (Zn) were used. After generated interpolation maps with Inverse Distance Weighted (IDW), fuzzy maps for each parameter were generated by the membership functions. Finally, with OWA six maps for fertility with different risk level were made. The results show that with decreasing risk (no trade-off), almost all of the parts of the study area were not suitable for soil fertility. While increasing risk, more area was suitable in terms of soil fertility in the study area. So using OWA can generate many maps with different risk levels that lead to different management due to the different financial conditions of farmers. [ABSTRACT FROM AUTHOR]

The aim of this study is to cluster landforms in the west of the Fars province, Iran using selforganizing maps (SOM). According to qualitative data, the clustering tendencies of landforms are investigated using six morphometric parameters, which are slope, profile, plan, elevation, curvature and aspect. First, topographic position index (TPI) is used to prepare the landform classification map. The results of SOM show that there are five classes for landform classification in the study area. Cluster 5 corresponds to high slope, high elevation but with different of concavity and convexity that consist of ridge landforms, while Cluster 3 corresponds to flat areas, possibly plantation areas, in medium elevation and almost flat terrain. Clusters 1, 2 and 4 correspond to channels with different slope conditions. [ABSTRACT FROM AUTHOR]