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Zandi, R. ,
Entezari, A. ,
Baaghide, M. ,
Khosravian, M. Publication Date: 2021/07/23
Researches in Earth Sciences (20088299) 12(2)pp. 36-49
Introduction Materials and methods Discussion and conclusion Results
Zandi, R. ,
Sadeghi, H. ,
Ghaedi, S. ,
Far, G.S.P. Publication Date: 2026
Urban Climate (22120955) 65
Effective management of air pollution in Isfahan metropolis requires robust, operational forecasting tools. Aiming to develop an efficient and cost-effective model for the simultaneous prediction of gaseous (CO, NO₂, SO₂) and non-gaseous (UVAI) pollutants, this study leveraged the capabilities of Artificial Neural Networks (ANN). The primary innovation of this research lies in its strategic and hybrid approach to input variable selection. In addition to traditional environmental variables (such as NDVI and distance from point sources), the model incorporates critical anthropogenic and urban variables, specifically population density and building density. This integration of data aims to account for the dynamic effects of local wind patterns induced by the city's physical fabric within the prediction model. The dataset comprised 2400 data points covering spatial and temporal variables. After normalization, the data were split into training (70 %) and test (30 %) sets. Model optimization parameters included 20,000 epochs, 10 hidden-layer neurons, and a weight-decay penalty (0.01) to mitigate overfitting. The model's quantitative performance was excellent. The coefficient of determination (R2) for UVAI prediction was 0.999, indicating exceptional accuracy. Additionally, R2 values were calculated at 0.98 for NO₂, 0.94 for SO₂, and approximately 0.90 for CO. These results, coupled with near-zero values for Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), confirm the model's high capability for the early and reliable prediction of pollutants. Variable Importance in Projection (VIP) analysis revealed that population density and building density were the most significant predictors of urban pollution, highlighting the critical importance of controlling urban development to improve air quality. However, limitations regarding temporal data and the inherent non-dynamic nature of ANNs in fully modeling atmospheric phenomena remain fundamental constraints. Consequently, future research proposals focus on expanding temporal datasets (specifically including multi-year data for long-term validation) and transitioning towards hybrid models, such as CNN-LSTM. These approaches aim to process time-series dynamics better and maximize model accuracy across various atmospheric scenarios. Ultimately, the proposed ANN model provides a robust decision-support tool for short-term air quality management in Isfahan. © 2025 Elsevier B.V.
Publication Date: 2025
The Egyptian Journal Of Remote Sensing And Space Sciences (11109823) 28(3)pp. 512-522
Landslides are among the phenomena associated with environmental impacts and human and financial losses worldwide. Investigating environmental issues such as landslides and preparing hazard maps are essential for managers and planners. This study examines and models landslides in the catchment area of Karun-3 Dam located in Khuzestan province, Iran, using six machine learning algorithms, including Random Forest (RF), Boosted Regression Tree (BRT), Generalized Aggregate Model (GAM), Support Vector Model (SVM), Classification and Regression Tree (CART), and Generalized Linear Model (GLM). Thirteen independent parameters were identified as the main parameters. Then, their correlation and effects were examined using 284 old landslides, and machine learning models were validated using efficiency, sensitivity, and accuracy indicators. The validation results showed that although all the models used have sufficient accuracy, the RF model (AUC = 0.982, Efficiency = 0.943) has more accuracy than the other five models. Also, the impact of different factors on landslide generation in various models is not the same. In general, the significance of the mentioned parameters is in the range of 0.043 and 0.160. Comparing the results of different models using a non-parametric test shows more similarities between the models used. In general, the results of various models show that the risk of landslides is generally higher on the steep banks of rivers, in the vicinity of lakes, dams, and roads, and especially in lands with soft lithology such as marl. This fact shows us the influence of anthropogenic factors and natural factors simultaneously. © 2025 The Author(s)
Mokhtari, L.G. ,
Amirahmadi, A. ,
Zandi, R. ,
Nasiri, A. ,
Ganji, M. ,
Shafiei, N. Publication Date: 2025
Geoheritage (18672477) 17(3)
The identification, interpretation, and evaluation of geographical diversity elements are essential for organizing specific regions and the entire landscape, as well as coordinating effective conservation or management. This study focused on the geodiversity analysis of the Binalood mountain range, renowned as the roof of Khorasan with an area of 3798 km2. Quantitative assessment methods were employed to investigate the geoenvironmental diversity of this region. The study utilized the Geomorphodiversity Index (GMI) as a tool for geodiversity assessment. The results reveal a decrease in geodiversity values from valleys to peaks within the study area. The increase in geodiversity values is primarily influenced by geological factors (rock types and formations) and fluvial processes. By comparing the percentage of area obtained for each class (V1 to V5) using the GMI method in two specified domains, it is observed that geodiversity values are higher in the northern domain of the Binalood Mountains compared to the southern domain. The highest geodiversity values were associated with class V5, covering a larger area in the northern domain than the southern domain (12.5% of the recreational area of Akhlamed and Chalidare, located northeast of the northern slopes of Binaloud). Class V4 (49.35%) in the northern domain also allocated a higher percentage of area compared to the southern domain. © The Author(s), under exclusive licence to International Association for the Conservation of Geological Heritage 2025.
Soltanifard, H. ,
Kashki, A. ,
Zandi, R. ,
Amani-beni, M. Publication Date: 2025
PLoS ONE (19326203) 20(4 April)
Boroughani, M. ,
Zandi, R. ,
Pourhashemi, S. ,
Gholami, H. ,
Kaskaoutis, D.G. Publication Date: 2025
Atmospheric Pollution Research (13091042) 16(2)
Sand and dust storms (SDS), as a direct consequence of land degradation and wind erosion, is an important environmental challenge in the last two decades, especially in arid and semi-arid areas. Land use changes due to human intervention and soil's susceptibility to erosion are among the most important factors influencing the SDS hotspots. This study aims to explore possible linkage between land use changes and SDS hotspots in Iran during a 20-years period (2001–2022). In this scope, four dust characterization indices based on MODIS observations (BTD3132, BTD2931, NDDI, and D) were employed to identify the SDS hotspots. Then, the land use – land cover (LULC) changes over Iran were mapped using MODIS images, aiming to identify the areas exhibiting large LULC changes and tendency to become SDS hotspots. Finally, the LULC changes were analyzed with respect to SDS hotspots. The results revealed 618 SDS hotspots in the whole Iranian territory, with the largest number of them located in non-vegetated lands, scattered shrubs and rangelands. In addition, Zabol in east Iran presented the highest frequency of SDS, while southwest Iran faced also a large number of SDS. The highest number of SDS was recorded in 2008 in most of the country's stations, following the dust-regime shift in the Middle East due to prolonged drought. Current methodology links SDS hotspots and LULC changes very well and can be helpful for developing mitigation strategies for the consequences of human and climate-induced LULC changes, wind erosion and SDS in arid environments. © 2024 Turkish National Committee for Air Pollution Research and Control
Asadi, M.A.Z. ,
Mokhtari, L.G. ,
Zandi, R. ,
Naemitabar m., M. Publication Date: 2025
Applied Water Science (21905495) 15(3)
Studying sediment transport to rivers is crucial for effective river management, engineering, and environmental preservation. Neglecting this aspect can lead to significant harm to natural ecosystems. This research aims to estimate suspended sediment levels in the Kal-e Shur Sabzevar River using various machine learning algorithms, which have gained popularity in recent years due to their high accuracy and reliability. The study employs ensemble Bagging algorithms, the gradient boosting machine (GBM), genetic algorithm, Naïve Bayes algorithm, gradient boosting decision trees, and extremely randomized trees. These algorithms provide a coherent framework that can serve as a standard for evaluating and comparing models in future research. Initially, data from 354 sediment measurement stations, including flow discharge, sediment discharge, and precipitation, were collected. After validating data homogeneity using the double mass method, 70% of the data were allocated for training, and 30% for testing. The algorithms were trained with this data, and their performance was evaluated using the coefficient of determination (R2), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) statistics. Additionally, a partial least squares (PLS) regression model was employed to identify the most influential factors affecting suspended sediment load in the basin. The results demonstrate that the gradient boosting machine (GBM) model outperforms other algorithms, exhibiting R2 values of 0.95, RMSE values of 0.019, and NSE values of 0.78. The PLS model identified geological factors and slope as primary determinants of suspended sediment load in the region. Lastly, the algorithms predicted sediment levels, with the GBM algorithm estimating a sediment concentration of 8955 mg/liter with a relative error of 8.54%, indicating strong alignment with the total sediment load in the region. © The Author(s) 2025.
Asadi, M.A.Z. ,
Mokhtari, L.G. ,
Zandi, R. ,
Naemitabar m., M. Publication Date: 2024
Sustainable Water Resources Management (23635045) 10(6)
This research examines the impact of land use changes from 2000 to 2023 on flood regimes and discharge in the Kal-e Shur Sabzevar basin. The study simulated rainfall-runoff processes using PCSWMM and HEC-HMS hydrological models, while the CA–Markov model projected future land use changes. The SCS-CN method was applied to simulate flood hydrographs, and the Muskingum method analyzed river trends. Results revealed notable peak discharge and flood volume increases: 24.6% in 2000, 49.8% in 2010, and 68% in 2020. The study computed maximum flood discharges for various return periods ranging from 2 to 1000 years. The model evaluation showed good accuracy with R2 = 0.97, NSE = 0.85, and PBIAS = 4.22. The HEC-HMS model was remarkably accurate for estimating peak flood discharge for the 100-year return period. Analysis indicates that land use changes, such as the conversion of pasture to agriculture, significantly increase maximum flood discharge and runoff volume. For instance, a 7.6% increase in discharge and runoff was observed for floods with a 500-year return period. Model calibration showed errors between 6.3% for the 100-year return period and 28% for the 2-year return period. Future projections with the CA–Markov model suggest a rise in non-vegetated and agricultural areas by 2050, driven by human activities, which will substantially alter land cover. These insights are valuable for local, national, and international policy development, sustainable land use planning, environmental protection, and flood risk management. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
Assadi, M.A.Z. ,
Nasiri, A. ,
Zandi, R. ,
Shafiei, N. Publication Date: 2024
Environmental Monitoring And Assessment (01676369) 196(12)
During the recent decades, subsidence has increasingly occurred in many countries around the world, with the case being even worse for developing countries like Iran. In Iran, the main factor contributing to the occurrence of land subsidence is excessive groundwater extraction of groundwater for agricultural activities. One of the best techniques for subsidence detection is differential interferometry. In terms of agricultural development, Nourabad is an important plain in Fars province, Iran, where land subsidence due to overconsumption of groundwater has been experienced during the recent past. Cropping pattern plays a significant role in the subsidence and acknowledging the critical state of water resources in Iran alongside the fact that the farming method, in terms of its water demand can greatly contribute to reduced level of local aquifer reserve and hence the occurrence of land subsidence, the present research seeks to investigate the effect of farming method (i.e., dry farming, irrigated farming, and summer crops farming) on the subsidence in Nourabad Watershed. Satellite images acquired by Sentinel-2 and Sentinel-1 were used. Data analysis was performed through radar data interferometry, support vector machine (SVM), and a hybrid of the two via spatial regression. Results of SVM showed that The maximum land subsidence recorded was approximately 10 cm per year was experienced on rice-farmed lands. Fields cultivated with rice exhibited a subsidence rate 50% higher than those with wheat") would emphasize the relationship between agricultural practices and environmental effects. Investigation of cropping pattern was showed The correlation between groundwater extraction and land subsidence was significant at p < 0.05". © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
Nasiri, A. ,
Khosravian, M. ,
Zandi, R. ,
Entezari, A. ,
Baaghide, M. Publication Date: 2023
The Egyptian Journal Of Remote Sensing And Space Sciences (11109823) 26(3)pp. 851-861
In recent decades, severe climate change, decreased precipitation, temperature rise, and increased evapotranspiration (ET) have significantly reduced waterbodies. Furthermore, governments have prioritized the study of water level fluctuations of lakes to protect them from degradation nationally and regionally. The present study investigated the physical changes in lakes Bakhtegan and Tashk due to climatic parameters. To this end, Landsat satellite imagery and the NDWI were employed to calculate the area of the waterbodies from 1986 to 2018. The results showed that the area had decreased during the study period– since 2009, Lake Bakhtegan had dried up completely. In 2008 and 2010, the lowest precipitation was 127.82 and 107.7 mm, respectively. During the study period (1986 to 2018), the average temperature was 19.44 °C, with an increase of 0.6 °C. Among the climatic parameters, precipitation, with a correlation coefficient of 0.55, and potential evapotranspiration (PET), with a correlation coefficient of about −0.68, were more strongly correlated with changes in the area of the waterbodies. To predict temperature and precipitation in the study area in the coming decades (2020–2050), the HadCM2 model of the CORDEX Project -WAS (South Asia) was used under two scenarios: RCP4.5 and RCP8.5. These scenarios revealed the decrease in precipitation and increase in temperature trends. As a result, the waterbodies’ areas were estimated using the projected precipitation and PET for the period 2050–2020, indicating a decrease in the areas of the waterbodies. © 2023 National Authority of Remote Sensing & Space Science
Nasiri, A. ,
Khosravian, M. ,
Zandi, R. ,
Entezari, A. ,
Baaghide, M. Publication Date: 2023
Environmental Earth Sciences (18666299) 82(19)
Conventional methods are inefficient and costly when it comes to collecting information on changes in water level and land surface temperatures (LST). Whereas, satellite data can be used to comprehensively investigate these parameters. By duplicating telemetry data at different times, it is possible to identify and study variable and dynamic phenomena in the environment. Overall, any change in the land surface has severe effects on the weather in these areas. Drying inland lakes is one of the emerging meteorological problems, such as the drying of Bakhtegan and Tashk lakes. The purpose of this research is the changes in Tashk and Bakhtegan lakes. In this study, Landsat 5–8 satellite data and the NDWI index were used to calculate the area of Tashk and Bakhtegan lakes from 1986 to 2018. During the study period, the area of the aforementioned lakes decreased significantly, so Lake Bakhtegan has dried up completely since 2009. As a result of coding LST in the Google Earth Engine system, the average land surface temperature has increased from 22.1 °C in 1986 to 30.34 °C in 2018, an increase of more than 8 °C. Furthermore, using the Grace satellite data and studying the change in groundwater level, it has been found that the highest rate of groundwater drop occurred in 2017 by 20 cm, and there is a general decreasing trend of more than 8 cm/year. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Zandi, R. ,
Zanganeh, Y. ,
Karami, M. ,
Khosravian, M. Publication Date: 2022
The Egyptian Journal Of Remote Sensing And Space Sciences (11109823) 25(4)pp. 1069-1088
In recent years, climate change has caused an increase in the air temperature and consequently the temperature of the earth's surface. The temperature of the earth's surface has had various effects on urban life and has caused the formation of thermal islands in cities. Thermal islands are one of the important criteria in regional planning. The city of Shiraz is considered to be a large population center in the southwest of Iran on the one hand and one of the important tourism centers of Iran on the other hand, so the purpose of this research is to monitor the spatial form of thermal islands in this city. The satellite images of Shiraz city were first collected and extracted in the hot periods of the year from 1985-to 2020 using the data of Landsat's 4 and 5 (TM), 7 (ETM+), and 8 (OLI/TIRS). After required preprocessing, the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Normalized Difference Built-up Index (NDBI), and the urban heat dispersion index were determined by the Urban Thermal Field Variance Index (UTFVI). The regions with UHI, the variation of the temperature in the city, and the relationship between the LST and surface vegetation were evaluated based on the results of image processing to determine the UHIs of Shiraz. The temporal variations of the temperature pattern of Shiraz city indicated that about 12.76 km2 was added to the area of the fourth temperature class from 1985 to 2020. Results of NDVI evaluation in the studied period showed a decline in the vegetation from 22.28 km2 in 1985 to 17.54 km2 in 2020 due to alteration of the land use which can explain the formation and increase of the UHIs in the mentioned regions. At the beginning of the investigated period (1985), the temperature of the earth's surface was 17°Celsius and reached 21°Celsius at the end of the period (2020). The monitoring of the spatial form of the surface temperature of the earth shows that heat islands are moving from the outskirts of the city towards the city center. UTFVI also revealed that the awful regions (very hot temperatures) are mainly concentrated in the west and north-west to the south west of Shiraz (including some parts of districts 9 and 10), south east of district 7, and north of district 1. © 2022 National Authority of Remote Sensing & Space Science
Khodaie, A. ,
Pahlavani, A. ,
Ghelichipour, Z. ,
Zandi, R. Publication Date: 2022
Watershed Engineering and Management (22519300) 14(1)pp. 40-54
The protection and management of each user in different areas should be based on ecological conditions, which can be achieved by assessing the ecological potential in each area. Assessing ecological potential means examining the potential power of the land and determining its natural use by humans. The main purpose of this study is to identify the ecological potential in Khodaafarin City with an area of 161,607 ha, using the multi-criteria assessment method, the common land management model of Dr. Makhdoom and using GIS. In this study, after identifying ecological resources (sustainable and unstable), the resources were analyzed and summarized. Then, in the software environment (ArcGIS 10.6), the information layers were combined and then the maps were evaluated. Finally, according to the existing values, the potential strengths and bottlenecks of the region were estimated and the permitted uses were prioritized in the region. As a result, after combining the necessary maps and correcting them, the environmental capacities and ecological potential of the region were estimated as area (percentage) in Khodaafarin City. According to the objectives of the research, areas prone to segregation of aquaculture-agriculture (2.65), aquaculture-rangeland management (0.14), aquaculture-urban and rural development (0.2), aquaculture-extensive tourism (0.049), conservation-extensive tourism (0.45), conservation-forestry (0.12), centralized tourism-forestry (0.021), aquaculture (6.34), extensive tourism (12.61), centralized tourism (2.64), rangeland management (33.1), agriculture (7.51), conservation (13.57), urban, rural and industrial development (1.8), forestry (18.8) were zoned and identified. The results also showed that the highest potential is related to the rangeland management with an area of 61567.55 ha of which less than 50% (30457 ha) is consistent with the current conditions. © 2022, Soil Conservation and Watershed Management Research Institute, All rights reserved.
Publication Date: 2022
پژوهشنامه مدیریت حوزه آبخیز (22516174) 13(25)pp. 133-144
Introduction and Objective: Flood, like other hydrological phenomena, is an uncertain phenomenon that can occur at any time and place and is influenced by various climatic factors, physical characteristics of the basin, vegetation status and land use, and human intervention. Determining the contribution of each parameter to the flood incidence is important. At present, with the development of GIS, remote sensing (RS), and machine learning (ML) methods, very accurate modeling of flood probability can be performed. However, the construction of these models requires accurate and principled knowledge of the flood occurrence process, the study of effective parameters in flood formation, understanding of how each parameter affects flood generation, and the selection and development of appropriate models and their evaluation. Due to the importance of determining flood-prone areas in different areas, especially basins located in arid and semi-arid areas such as the study area, the present study was conducted to assess flood risk using vector mining data mining models. Random support, grass, and forest are targeted in this area. Material and Methods: In the present study, to support the risk of flooding, data support models of support vector machine, field, and random forest have been used. In general, the purpose of presenting data mining models is to achieve a reasonable and accurate estimate of spatial prediction of flood occurrence, compare the efficiency of the models and select the most appropriate method for preparing a flood sensitivity assessment map. In this study, from various information such as the topographic map of scale 1: 50000 to extract level lines, a geological map of scale 1: 100000, a soil map prepared by the General Department of Natural Resources and Watershed Management of Khorasan Razavi province, digital elevation model (DEM) image with Spatial resolution of 12.5 m, Google Earth satellite imagery, meteorological data, rain gauge, statistical period of 20 years (98-78), Andarkh stations, Olang Asadi, Kardeh Dam, Marshak, Bulgur, Bala Gosh, Al, Chenaran, Moghan, Chekneh Olya, Abqad Frizi, Talgur, Qadirabad, and Kabkan have been used. Elevation, slope, slope direction, drainage networks, main waterways, and convexity of the ground surface were extracted from the DEM image and level lines. Land use of the region was prepared from Google Earth satellite images related to 2020 and in a supervised classification method. The vegetation map of the region was also prepared based on the NDVI index and from satellite images of Landsat 8 in 2018. Results: The elevation factor plays a key role in controlling the direction of flood movement and water surface depth. At an altitude of 2000 m and more, with increasing altitude, the flood potential in the study area increases. According to the results, among the uses of the studied basin, irrigated and garden lands produce less runoff due to more infiltration and are less prone to flooding. In the study area, at a slope of 60 degrees, due to the increase in slope, the latency of the basin is low, the amount of water infiltration into the soil is low, and as a result, the volume of floods and surface runoff will increase. Class 0/0074-0/0120 has the greatest impact on the occurrence of basin floods. The northern, northwestern, and western slopes have the potential for flooding due to heavy rainfall, long-term snow retention, and moisture. In the study area, more than 250 mm of rainfall has the greatest impact on the occurrence of floods. In the study area, due to the relatively low permeability of the soil, the soil produces more runoff and floods. Based on the results of topographic moisture index classes in the study area, classes 268/38-359/99 had a great impact on the occurrence of floods. In the study area, concave areas have a great impact on floods because the most important and effective factors in the occurrence of floods are the slope and curvature of the earth. At present, the predictability of flood sensitivity in the study area was investigated using the area under the curve. The results of this study show that in the linear support vector machine model with the best scenario M3 with the highest correlation coefficient of 0.972 and the lowest value of MAE = 0.538, and in the random forest model the best scenario M10 with the highest correlation coefficient 961 0.0 and the lowest error value MAE = 0.685, in the Chaid decision tree model, the best scenario was M8 with the highest correlation coefficient of 0.954 and the lowest error value was MAE = 0.723. Conclusion: In general, according to the results of the present study, the floors with low and medium flooding potential are more located in the eastern and southern parts of the basin, so in the eastern part of the basin due to low slope and good permeability flood risk, It is average. According to the results, due to the existence of poor rangeland land uses in the western and northwestern half of the basin, the highest flood potential has been observed. The results also showed that the northern and western parts of the basin, which in terms of geology and lithology have surface formations such as marl, clay, and silt, and their permeability coefficient is very low and vegetation is low, have a high potential for occurrence. They have flooded. In this study, the models were evaluated using correlation coefficient (R) and mean absolute error values (MAE). Examination of the results of the models showed that the support vector machine, chad, and random forest models with scenarios M3, M8, and M10 with the highest correlation and the lowest mean error, respectively, have high accuracy in estimating the risk of floods in the study area. In addition, the area under the curve (ROC) was used to evaluate the proposed models. Accordingly, these values have more accurate results in both educational data and training data in the algorithm (SVM) and the new random forest algorithm model. This result indicates that both models have been validated in terms of modeling accuracy and validity. © 2022, Sari Agricultural Sciences and Natural Resources University. All rights reserved.
Publication Date: 2022
Journal of the Indian Society of Remote Sensing (09743006) 50(5)pp. 833-847
As one of the most significant aspects of rapid growth without urban planning, reduction in vegetation is often replaced by unauthorized surfaces such as buildings and other impervious surfaces. Karaj metropolis is one of the significant urban areas located 20 km west of Tehran with such rapid growth. The present study evaluates the temporal-spatial variations of Land Surface Temperature (LST) and the Urban Liveability Index (ULI). It also seeks to examine the Urban Heat Islands (UHIs) using the data of the Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) over 32 years (1987–2019). The Fractal Net Evolution Approach (FNEA) was employed to calculate UHIs, and the deductive Environmental Critical Condition (ECI) method based on LST and NDVI was used for probing the urban environmental situation. The results indicated that the average LST in Karaj metropolis is between 22 and 35 °C. Furthermore, in 1990, the standard deviation of the LST was increased so that more than seven °C was observed for LST. Analysis of temperature zones and their effective parameters such as construction, transportation, and road construction in Karaj metropolis shows a significant negative correlation between LST and NDVI and a positive one between LST and constructed urban areas. © 2022, Indian Society of Remote Sensing.
Publication Date: 2022
Geoheritage (18672477) 14(4)
Geodiversity is a landscape property linked to the heterogeneity of physical features across the Earth’s surface. It refers to the wide span of diversity in geological features and phenomena, resembling the concept of biodiversity in natural living organisms (including plants and animals). Hosting highly complex geological phenomena and features, Iran exhibits high degrees of geodiversity. The present study aims to evaluate the geodiversity in pretty virgin natural areas for developing geotourism spaces, attracting tourists, and expanding ecosystems in the Noorabad basin. The case study is part of sub-basins (SWs) in Hendijan and Jarahi, located northwest of the Fars Province, southwestern Iran. The methodology of this research is based on analytic techniques and field surveys. For this purpose, we began by preparing topographic position indicator (TPI), climatology, geology, and morphology layers, then calculating diversity and richness indexes. Finally, the layers were compiled to come up with a final map of five geodiversity indexes, including patch richness density (PRD), Simpson’s roughness index (SIEI), Shannon’s roughness index (SHEI), Shannon’s diversity index (SHDI), and Simpson’s diversity index (SIDI). The results showed that the geodiversity was higher in the SW1 due to the higher diversity of this zone in terms of lithology, geomorphology, and climatology. In the SW1, the values of PRD, SIEI, SHEI, SHDI, and SIDI were estimated at 0.31, 0.81, 0.68, 1.56, and 0.87, respectively. This geodiversity in the southern part of the Noorabad basin has leveraged tourism development in this area. © 2022, The Author(s), under exclusive licence to International Association for the Conservation of Geological Heritage.
Publication Date: 2022
Watershed Engineering and Management (22519300) 14(4)pp. 549-562
Occurrence of numerous floods in different regions of the country has always caused a lot of damages in various fields. Therefore, it seems necessary to prepare and compile a comprehensive plan in the field of flood control. The study area is influenced by the Mediterranean climate and within the radius of the Caspian and Caucasian climates. Due to the high altitude differences, it has a variety of climates and high variability in rainfall, and known as one of the areas exposed to destructive floods. The purpose of this study is to identify flood prone areas based on multi-criteria decision making and neural network model in Khodaafarin Watershed. For this purpose, according to the factors affecting the occurrence of floods, the information layers of the region including Curve Number (CN), Gravilius coefficient, runoff height, precipitation, distance from waterway, soil retention, waterways, slope, drainage density, geology and vegetation, according to the study of maps, reports, satellite images and field surveys. In order to weight the criteria in the present study, network analysis method (ANP) and Super Decisions software were used. The factor of runoff height with the amount of 0.241, slope with the amount of 0.207 and precipitation with the weight of 0.169 were the most important in relation to flood occurrence. Finally, by combining these layers according to their weight in the GIS environment, a flood risk zoning map was extracted in five classes. The results also showed that, 43 square kilometers (3% of the area) of the watershed is in the very high flood risk class and 288 square kilometers (18% of the area) in the high flood risk calss. More than 21% of the area is among the areas with high and very high flood potential. Therefore, it seems that the need for surface water management in the region in order to prevent floods and the proper use of water in the region is necessary. © 2022, Soil Conservation and Watershed Management Research Institute. All rights reserved.
Publication Date: 2021
Urban Climate (22120955) 37
Examining the land surface temperature (LST) and its mechanism is very significant for urban planning.The purpose of this study is to determine the factors affecting the surface temperature of urban areas of Shiraz. The OLS and GWR were used o determine the effective factors. Also, satellite data of Landsat 8 for summer 2019 were used to obtain the surface temperature of Shiraz. To this end, the Landsat 8 satellite images of Shiraz urban districts during Summer 2019, were prepared. First, the LST and vegetation was extracted from the images. Before performing the regression model between the LST as the dependent variable and geographical variables such as slope, slope gradient, elevation, distance to rivers, direct and indirect solar radiation, sunshine duration, and Total Solar Irradiance (TSI) as independent variables, the component analysis method was employed to eliminate collinearity and dependence relations among independent variables and reducing variables. Four significant components that had eigenvalues above one and about 86.75% of the variance of the initial variables were selected, which included the significance of (direct, indirect, general) solar radiation, the direction of slope, distance to rivers, vegetation, and slope gradient, respectively. In the next step, the regression analysis was performed between the components and the LST via the Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR). The results showed that GWR has better performance in showing the spatial distribution of LSTs in Shiraz. Shiraz UHIs correspond to the airport and the dirt and barren districts around the city, which are often non-residential. © 2021 Elsevier B.V.
Publication Date: 2021
Earth Science Informatics (18650473) 14(4)pp. 2133-2144
During the past years, land subsidence due to different reasons such as uncontrolled population growth, overuse of groundwater resources, tectonic and other factors has led to numerous challenges and problems for agricultural lands, residential buildings, roads, power transmission lines, etc. Hence, it is important to monitor the rate of subsidence and address its influencing cause or causes to control and monitor risk. Sentinel-1A data were used in Sentinels Application Platform (SNAP) in this study to examine the subsidence status in the Fahlian aquifer and determine the changes in the land surface in the specified time period. Grace satellite was also used to monitor the changes in the aquifer groundwater fluctuations. OLS model was used to perform spatial analysis of parameters influencing subsidence, the results of which indicated several influencing factors. Accordingly, the greatest impact on land subsidence was related to groundwater drawdown, altitude, and vegetation with probabilities of 0.002, 0.001, and 0.001%, respectively, indicating the significance of the mentioned parameters in the subsidence of the Fahlian aquifer. The output of the CSR, JPL, and CFZ algorithms showed a drawdown trend for the groundwater level since 2009, reaching about 20 cm in 2017. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Publication Date: 2019
Journal of Urban Management (25890360) 8(3)pp. 342-354
The present descriptive-analytical study employs a survey research method, documentary technique, and applied-developmental research design to zone the 18 neighborhoods of Sabzevar City in terms of urban poverty indicators. Data collection was done through a questionnaire distributed among a sample with the size of 384 participants selected from for citizens of 18 neighborhoods of Sabzevar City. A total of 17 urban poverty indicators were surveyed in the form of three sociocultural, economic, and access to urban services indicators. For data analysis, the analytic network process (ANP), Grey Relational Analysis (GRA), and spatial statistics tests were used. The results of the integration of the three economic, sociocultural, and access to urban services indicators depict that the highest urban poverty is in neighborhoods 17 and 18, 6, 14, 15, 13, 12, 11, and 9 respectively. All these neighborhoods are among the marginal neighborhoods of the city. The lowest urban poverty levels are in neighborhoods 1, 2, 3, 4, 5 and partly in neighborhoods 13, 14 and 16, which are part of the city's central neighborhoods, mostly including government employees, the salaried, and those with high-paying jobs. Comparing different types of urban textures via the Integrated Zoning Map of Poverty in Sabzevar City shows that urban poverty zones correspond to the areas of unofficial settlements and extension villages, and the economic poverty in the southern regions of the city is higher than other urban areas. According to the principles of Grey Relational Analysis (GRA), neighborhoods 1 and 2, which includes Southern Kashifi St., Northern Asrar St., and Imam Khomeini Blvd., is considered to be at a higher level than other areas in terms of poor urban poverty. The results of spatial statistics tests (spatial autocorrelation test and G-test) indicate the correlation and clustering of the data model or urban poverty indicators of the study area. © 2019 Zhejiang University and Chinese Association of Urban Management
Perevedentsev, P.Y. ,
Shantalinskii, K.M. ,
Zandi, R. ,
Aukhadeev, T.R. Publication Date: 2015
Uchenye Zapiski Kazanskogo Universiteta. Seriya Estestvennye Nauki (2542064X) 157(1)pp. 7-19
Spatial and temporal changes in the temperature conditions of the Persian Gulf region (27.5-40° N, 37.5-55.0° E) have been considered. The study is based on the reanalysis of near-surface air temperature, mass fraction of water vapour, atmospheric pressure reduced to sea level, and wind speed. Statistical processing of the material, as well as construction of linear trends and low-frequency components of the filtered meteoparameters with oscillations for periods of less than 10 years, has revealed certain trends of long-period changes in the basic climatic indicators. It has been demonstrated that the temperature tends to increase in July, whereas the characteristics of air humidity and wind speed, on the contrary, decrease. In addition, the bioclimatic indices ("heat index", effective temperature, etc.) point to a worsening of the climatic conditions, which affects human health. © 2015 Kazan Federal University. All rights reserved.
Perevedentsev, P.Y. ,
Shantalinskii, K.M. ,
Aukhadeev, T.R. ,
Ismagilov n.v., ,
Zandi, R. Publication Date: 2014
Uchenye Zapiski Kazanskogo Universiteta. Seriya Estestvennye Nauki (2542064X) 156(2)pp. 156-169
The paper describes spatiotemporal variability of atmospheric pressure fields, air temperature and wind speed in the troposphere of the Northern Hemisphere over the period from 1948 to 2013. The southern oscillation in the subequatorial Pacific Ocean, and also the delay of the low-frequency temperature component with respect to the changes in zonal atmospheric circulation in a latitudinal zone between 30° N and 70° N are revealed as a response in the baric fields of extra-tropical latitudes on the El Ninõ phenomenon during the winter period. The contribution of wind speed to the temperature variations reaches 60%. The reaction of air temperature in the Volga Region to the influence of a number of circulating systems is considered as an example. © 2014 Kazan Federal University. All rights reserved.
Perevedentsev, P.Y. ,
Zandi, R. ,
Aukhadeev, T.R. ,
Shantalinskii, K.M. Publication Date: 2014
Uchenye Zapiski Kazanskogo Universiteta. Seriya Estestvennye Nauki (2542064X) 156(2)pp. 147-155
The article considers the origin and spatio-temporal distribution of dust storms within the territory of Khuzestan Province located in the southwestern part of Iran. It was found that approximately 75% of the dust storms occur outside the region, the rest are of local origin. The main attention is focused on the long-term features of dust storms, their duration, annual and daily cycle, and horizontal visibility deterioration caused by dust storms. The analysis of state and variability of temperature conditions in the region over the past few decades showed that in winter temperature experiences temporary fluctuations, and there is a clear upward trend in summer. © 2014 Kazan Federal University. All rights reserved.
Perevedentsev, P.Y. ,
Zandi, R. ,
Aukhadeev, T.R. Publication Date: 2013
Uchenye Zapiski Kazanskogo Universiteta. Seriya Estestvennye Nauki (2542064X) 155(4)pp. 144-156
This study considers spatiotemporal changes in the main climatic characteristics (air temperature and humidity, atmospheric precipitation, and wind velocity) in the southwest of Iran within 1951–2010, using the data of meteorological observations from 13 stations. The frequency of anomalies (of varying intensity) in meteorological values and the degree of dryness of the region’s climate are estimated. A general tendency of temperature rise throughout the year, especially in August, and the unstable nature of wind regime are revealed. © 2013 Kazan Federal University. All rights reserved.
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