En
  • دکتری (1393)

    سنجش از دور و سیستمهای اطلاعات مکانی

    دانشگاه هایدلبرگ، هایدلبرگ، آلمان

  • کارشناسی‌ارشد (1387)

    سنجش از دور و سیستمهای اطلاعات مکانی

    تربیت مدرس، تهران، ایران

  • کارشناسی (1385)

    جغرافیا و برنامه ریزی شهری

    شهید چمران اهواز، اهواز، ایران

  • داده کاوی و یادگیری ماشینی در مدل سازی های محیطی
  • مدل سازی و پیش بینی زمانی - مکانی
  • پردازش رقومی تصاویر
  • کاربری اراضی، امنیت غذایی و تغییر اقلیم
  • نقشه برداری رقومی خاک

    حسین شفیع زاده مقدم با تخصص سنجش از دور و سیستم های اطلاعات جغرافیایی از سال 1394 همکاری خود را به عنوان عضو گروه مهندسی منابع آب در دانشگاه ترببیت مدرس آغاز کرد. وی دکترای تخصصی خود را از دانشگاه هایدلبرگ آلمان در سال 1393 دریافت کرد.

    ارتباط

    رزومه

    Integrating a Forward Feature Selection algorithm, Random Forest, and Cellular Automata to extrapolate urban growth in the Tehran-Karaj Region of Iran

    Hossein Shafizadeh-Moghadam, Masoud Minaei, Robert Gilmore Pontius Jr, Ali Asghari, Hashem Dadashpoor
    Journal PapersComputers, Environment and Urban Systems , Volume 87 , 2021 May 1, {Pages 101595 }

    Abstract

    This paper couples a Forward Feature Selection algorithm with Random Forest (FFS-RF) to create a transition index map, which then guides the spatial allocation for the extrapolation of urban growth using a Cellular Automata model. We used Landsat imagery to generate land cover maps at the years 1998, 2008, and 2018 for the Tehran-Karaj Region (TKR) in Iran. The FFS-RF considered the independent variables of slope, altitude, and distances from urban, crop, greenery, barren, and roads. The FFS-RF revealed temporal non-stationary of drivers from 1998–2008 to 2008–2018. The FFS-RF detected that altitude and distance from greenery were the most important drivers of urban growth during 1998–2008, then distances from crop and barren were the

    Flash-flood susceptibility mapping based on XGBoost, Random Forest and Boosted Regression Trees

    Rahebeh Abedi, Romulus Costache, Hossein Shafizadeh-Moghadam, Quoc Bao Pham
    Journal PapersGeocarto International , Issue just-accepted, 2021 April 22, {Pages 14-Jan }

    Abstract

    Historical exploration of flash flood events and producing flash flood susceptibility maps are crucial steps for decision makers in disaster management. In this paper, classification and regression tree (CART) methodology and its ensemble models of random forest (RF), boosted regression trees (BRT), and extreme gradient boosting (XGBoost) were implemented to create a flash flood susceptibility map of the B?sca Chiojdului River Basin, one of the areas in Romania that is constantly exposed to flash floods. The torrential areas including 962 flash flood events were delineated from orthophotomaps and field observations. Furthermore, a set of conditioning forces to explain the flash floods was constructed which included aspect, land use and land

    Fully component selection: An efficient combination of feature selection and principal component analysis to increase model performance

    H Shafizadeh-Moghadam
    Journal Papers , , {Pages }

    Abstract

    Investigation of Land Use Changes in Karkheh Watershed during 1990 and 2020 Using Google Earth Engine Platform and Landsat Satellite Images

    A Sadian, H Shafizadeh-Moghadam
    Journal Papers , , {Pages }

    Abstract

    Rural electrification in protected areas: A spatial assessment of solar photovoltaic suitability using the fuzzy best worst method

    F Minaei, M Minaei, I Kougias, H Shafizadeh-Moghadam, SA Hosseini
    Journal Papers , , {Pages }

    Abstract

    Validation of the CHIRPS and CPC-Unified products for estimating extreme daily precipitation over southwestern Iran

    HA Ghaedamini, S Morid, MJ Nazemosadat, A Shamsoddini, ...
    Journal Papers , , {Pages }

    Abstract

    Google Earth Engine for large-scale land use and land cover mapping: An object-based classification approach using spectral, textural and topographical factors

    H Shafizadeh-Moghadam, M Khazaei, SK Alavipanah, Q Weng
    Journal Papers , , {Pages }

    Abstract

    Evaluation of ECMWF mid-range ensemble forecasts of precipitation for the Karun River basin

    Mozhgan Abedi, Hossein Shafizadeh-Moghadam, Saeed Morid, Martijn J Booij, Majid Delavar
    Journal PapersTheoretical and Applied Climatology , 2020 March 26, {Pages 10-Jan }

    Abstract

    Ensemble Prediction Systems (EPSs) are increasingly applied for rainfall forecasts and flooding warning systems. In this paper, these forecasts and their skills are evaluated through relevant criteria, particularly by considering forecast performances for different lead times. Furthermore, to enhance their performance, we propose to preprocess the EPS forecasts’ output using bias correction methods. For this aim, forecasts for different ranges of precipitation as well as various climatic conditions are evaluated, which is particularly important for extreme events that can lead to flooding. The Karun River basin in Iran is used as case study, a large area including various climate conditions. The results showed that the performance of Euro

    Multiple-depth Modeling of Soil Organic Carbon using Visible–Near Infrared Spectroscopy

    Elham Shahrayini, Hossein Shafizadeh-Moghadam, Ali Akbar Noroozi, Mostafa Karimian Eghbal
    Journal PapersGeocarto International , Issue just-accepted, 2020 May 6, {Pages 18-Jan }

    Abstract

    This paper evaluates the capability of visible-near-infrared (VIS-NIR) spectroscopy to estimate soil organic carbon (SOC) at multiple depths including 0-15, 15-40, 40-60, and 60-80 cm. Four modeling algorithms, namely partial least squares regression(PLSR), principal component regression(PCR), support vector regression(SVR), and Random forest(RF) were calibrated to process the spectroscopy data. Overall, 120 soil samples were taken from 30 profiles at the depth of 0–80 cm. The selected models were calibrated considering different pre-processing techniques including Savitzky-Golay first deviation(SGD), normalization(N), and standard normal variate transformation(SNV). Results revealed that RF outperformed other models at multiple depth

    Modeling the spatial variation of urban land surface temperature in relation to environmental and anthropogenic factors: a case study of Tehran, Iran

    Hossein Shafizadeh-Moghadam, Qihao Weng, Hua Liu, Roozbeh Valavi
    Journal PapersGIScience & Remote Sensing , Volume 57 , Issue 4, 2020 May 18, {Pages 483-496 }

    Abstract

    Spatial variation of Urban Land Surface Temperature (ULST) is a complex function of environmental, climatic, and anthropogenic factors. It thus requires specific techniques to quantify this phenomenon and its influencing factors. In this study, four models, Random Forest (RF), Generalized Additive Model (GAM), Boosted Regression Tree (BRT), and Support Vector Machine (SVM), are calibrated to simulate the ULST based on independent factors, i.e., land use/land cover (LULC), solar radiation, altitude, aspect, distance to major roads, and Normalized Difference Vegetation Index (NDVI). Additionally, the spatial influence and the main interactions among the influential factors of the ULST are explored. Landsat-8 is the main source for data extrac

    Modeling the probability of gully occurrence and investigating the spatial effects of its drivers using the boosted tree regression

    Hossein Talebi Khiavi, Hossein Shafizadeh Moghadam, Mostafa Karimian Eghbal
    Journal PapersEnvironmental Sciences , Volume 18 , Issue 3, 2020 September 22, {Pages 167-183 }

    Abstract

    Introduction: Gully erosion is a subtype of water erosion that makes agricultural lands impracticable during its development. Given the geographical and environmental conditions, various factors contribute to the development and expansion of gully erosion. In this study, due to the extensive expansion of gully erosion in Jafarabad Moghan, and damaging the agricultural lands and rangelands, the probability of gully occurrence and the spatial effects of its drivers has been investigated. Material and methods: In this study, using a boosted regression tree model, the effect of the following factors on the gully occurrence were investigated: slope, aspect, plan curvature, altitude, clay content of horizon A, clay content of horizon B, sand co

    Evaluation of ECMWF mid-range ensemble forecasts of precipitation for the Karun River basin.

    M Abedi, H Shafizadeh-Moghadam, S Morid, MJ Booij, M Delavar
    Journal Papers , , {Pages }

    Abstract

    Big data in Geohazard; pattern mining and large scale analysis of landslides in Iran

    Hossein Shafizadeh-Moghadam, Masoud Minaei, Himan Shahabi, Julian Hagenauer
    Journal PapersEarth Science Informatics , 2019 July 12, {Pages 17-Jan }

    Abstract

    In this paper, we clustered and analyzed landslides and investigated their underlying driving forces at two levels, country and cluster, all over Iran. Considering 12 conditioning factors, the landslides were clustered into nine relatively homogeneous regions using the Contextual Neural Gas (CNG) algorithm. Next, their underlying driving forces were ranked using the Random Forest (RF) algorithm at country and cluster levels. Our results indicate that the mechanisms for landslide occurrence varied for each cluster and that driving forces of the landslides operated differently at a country level compared to the cluster level. Moreover, slope, altitude, average annual rainfall, and distance to the main roads were identified as the

    GlobeLand30 maps show four times larger gross than net land change from 2000 to 2010 in Asia

    Hossein Shafizadeh-Moghadam, Masoud Minaei, Yongjiu Feng, Robert Gilmore Pontius Jr
    Journal PapersInternational Journal of Applied Earth Observation and Geoinformation , Volume 78 , 2019 June 1, {Pages 240-248 }

    Abstract

    This article uses the GlobeLand30 maps of land cover to characterize the difference between years 2000 and 2010 in Asia. Methods of Intensity Analysis and Difference Components dissect the transition matrix for nine categories: Barren, Grass, Cultivated, Forest, Shrub, Water, Artificial, Wetland and Ice. Results show that Barren, Grass, Cultivated, and Forest each account for more than 21% of Asia at both 2000 and 2010, while transitions among those four categories account for more than half of the temporal difference. Nearly ten percent of Asia shows overall temporal difference, which is the sum of three components: quantity, exchange and shift. Quantity accounts for less than a quarter of the temporal difference, while exchange accounts f

    Urban expansion simulation and scenario prediction using cellular automata: comparison between individual and multiple influencing factors

    Yongjiu Feng, Jiafeng Wang, Xiaohua Tong, Hossein Shafizadeh-Moghadam, Zongbo Cai, Shurui Chen, Zhenkun Lei, Chen Gao
    Journal PapersEnvironmental monitoring and assessment , Volume 191 , Issue 5, 2019 May 1, {Pages 291 }

    Abstract

    Quantifying the contribution of driving factors is crucial to urban expansion modeling based on cellular automata (CA). The objective of this study is to compare individual-factor-based (IFB) models and multi-factor-based (MFB) models as well as examine the impacts of each factor on future urban scenarios. We quantified the contribution of driving factors using a generalized additive model (GAM), and calibrated six IFB-DE-CA models and fifteen MFB-DE-CA models using a differential evolution (DE) algorithm. The six IFB-DE-CA models and five MFB-DE-CA models were selected to simulate the 2005–2015 urban expansion of Hangzhou, China, and all IFB-DE-CA models were applied to project future urban scenarios out to the year 2030.

    Exploring the driving forces and digital mapping of soil organic carbon using remote sensing and soil texture

    Nikou Hamzehpour, Hossein Shafizadeh-Moghadam, Roozbeh Valavi
    Journal PapersCATENA , Volume 182 , 2019 November 1, {Pages 104141 }

    Abstract

    The main objectives of this paper were 1) to estimate soil organic carbon (SOC) using remote sensing covariates, soil properties, and topographic factors , and 2) to evaluate the interaction and the relative influence of the selected factors on the spatial variation of SOC. Thirteen factors were considered for digital mapping of SOC in the west Urmia Lake in Iran. To quantify multicollinearity among the predictor variables, Variance Inflation Factor (VIF) was calculated. Among them, nine independent factors were remained including silt, sand, slope, enhanced vegetation index (EVI), brightness, wetness, land cover, and latitude and longitude. A machine learning algorithm called Gradient Boosting Machine (GBM) was calibrated for understanding

    Modelling climate change effects on Zagros forests in Iran using individual and ensemble forecasting approaches

    Roozbeh Valavi, Hossein Shafizadeh-Moghadam, AliAkbar Matkan, Alireza Shakiba, Babak Mirbagheri, Seyed Hossein Kia
    Journal PapersTheoretical and Applied Climatology , Volume 137 , Issue 02-Jan, 2019 July 1, {Pages 1015-1025 }

    Abstract

    It is believed that climate change will cause the extinction of many species in the near future. In this study, we assessed the impact of climate change on the climatic suitability of the Persian oak in Zagros forests in southwest Iran, by simulating their conditions under four climate change scenarios in the 2030s, 2050s, 2070s, and 2080s. Additionally, we evaluated the predictive performance of different modelling algorithms by projecting the geographic distribution of Persian oak, using a block cross-validation technique. According to the results, the Persian oak shows a stronger response to temperature, particularly the maximum temperature of the warmest month, rather than precipitation variables. This indicates that temper

    How much can temporally stationary factors explain cellular automata-based simulations of past and future urban growth?

    Yongjiu Feng, Rong Wang, Xiaohua Tong, Hossein Shafizadeh-Moghadam
    Journal PapersComputers, Environment and Urban Systems , Volume 76 , 2019 July 1, {Pages 150-162 }

    Abstract

    Driving factors are usually assumed temporally stationary in cellular automata (CA) based land use modeling, hence the persistence of their relationships. Therefore, major questions as to how much do the temporally stationary factors explain the past and future urban growth, and how long can these factors justify the projection of urban scenarios in the future, are worth further study. We selected seven explanatory driving factors to calibrate a DE-CA (differential evolution-based CA) model to simulate urban growth in Ningbo of China during 2000–2015 and project nine scenarios of urban growth from 2015 to 2060. We evaluated the effects of factors on urban growth using generalized additive models (GAM) based on fitting statistics such as a

    Improving spatial accuracy of urban growth simulation models using ensemble forecasting approaches

    Hossein Shafizadeh-Moghadam
    Journal PapersComputers, Environment and Urban Systems , Volume 76 , 2019 July 1, {Pages 91-100 }

    Abstract

    This paper aims to improve the spatial accuracy of urban growth simulation models and clarify any associated uncertainties. Artificial Neural Networks (ANNs), Random Forest (RF), and Logistic Regression (LR) were implemented to simulate urban growth in the megacity of Tehran, Iran, as a case study. Model calibration was performed using data between 1985 and 1999 whereas the data between 1999 and 2014 was used for model validation. First of all, Transition Index Maps (TIMs) were computed by means of each model to assess the potential of urban growth for each cell. Using the standard deviation, consensus between the TIMs was evaluated. Because the TIMs of the individual models manifested discrepancies, they were combined using a number of ens

    Future urban rainfall projections considering the impacts of climate change and urbanization with statistical–dynamical integrated approach

    Hiteshri Shastri, Subimal Ghosh, Supantha Paul, Hossein Shafizadeh-Moghadam, Marco Helbich, Subhankar Karmakar
    Journal PapersClimate Dynamics , 2019 January , {Pages 19-Jan }

    Abstract

    Impacts of global warming and local scale urbanization on precipitation are evident from observations; hence both must be considered in future projections of urban precipitation. Dynamic regional models at a fine spatial resolution can capture the signature of urbanization on precipitation, however simulations for multiple decades are computationally expensive. In contrast, statistical regional models are computationally inexpensive but incapable of assessing the impacts of urbanization due to the stationary relationship between predictors and predictand. This paper aims to develop a unique modelling framework with a demonstration for Mumbai, India, where future urbanization is projected using a Markov Chain Cellular Automata a

    دروس نیمسال جاری

    • كارشناسي ارشد
      كارگاه GIS ( واحد)
      دانشکده کشاورزی، گروه ترويج و آموزش كشاورزي
    • كارشناسي ارشد
      كارگاه GIS ( واحد)

    دروس نیمسال قبل

    • دكتري
      سنجش ازدور ( واحد)
      دانشکده کشاورزی، گروه خاكشناسي
    • دكتري
      سنجش ازدور ( واحد)
    • كارشناسي ارشد
      سنجش از دور تكميلي ( واحد)
    • كارشناسي ارشد
      سنجش از دور تكميلي ( واحد)
    • 1397
      سعديان, علي
      بررسي و مدل‌سازي مكاني اثر تغيير اقليم و كاربري اراضي بر روي كاربري‌هاي زراعي در استان هاي كردستان، كرمانشاه و ايلام.
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