En
  • دکتری (1387)

    علوم و مهندسی آبخیزداری

    دانشگاه تهران، ایران

  • کارشناسی‌ارشد (1377)

    آبخیزداری

    دانشگاه تربیت مدرس،

  • کارشناسی (1374)

    مهندسی منابع طبیعی ، مرتع وآبخیزداری

    دانشگاه علوم کشاورزی و منابع طبیعی گرگان،

  • هیدرولوژی آبهای سطحی
  • هیدرولوژی برف
  • مهندسی آب و خاک (پیوست)

    زمینه های پژوهشی:

    کارشناس مسئول: صادق بور

    تلفن:

    مکان: آزمایشگاه مرکزی

اینجانب مهدی وفاخواه از سال 1377 به عنوان عضو هیئت علمی دانشکده منابع طبیعی و علوم دریایی دانشگاه تربیت مدرس مشغول به فعالیت می باشم. دروس هیدرولوژی کاربردی، مدیریت و کنترل سیلاب، مهندسی رودخانه، آبخیزداری شهری، برف و بهمن، سامانه اطلاعات جغرافیایی پیشرفته، هیدرولوژی برف، هیدرولوژی پیشرفته و مدلهای بارش-رواناب را در مقاطع تحصیلی کارشناسی ارشد و دکتری تدریس نموده‌ام. زمینه تحقیقاتی اینجانب اثرتغییر اقلیم و کاربری اراضی بر ویژگی‌های سیلاب، پیش بینی و مدلسازی سیلاب، هیدرولوژی سیلاب شهری و هیدرولوژی آب‌های سطحی می‌باشد.

ارتباط

رزومه

Flood hydrograph modeling using artificial neural network and adaptive neuro-fuzzy inference system based on rainfall components

Saeid Janizadeh, Mehdi Vafakhah
Journal PapersArabian Journal of Geosciences , Volume 14 , Issue 5, 2021 March , {Pages 14-Jan }

Abstract

Different limitations such as the lack of enough hydrometric stations, difficulty in collecting hydrometric data with costly data collection are caused to create hydrologic models for estimating the flood hydrograph. Based on the easy and more access to rainfall statistics, preparing the hydrologic model based on rainfall characteristics and data seems to be the very applicable and logical method. Data-driven models have increasingly been used to describe the behavior of hydrological systems, which can be used to complement or even replace physical-based models. In this study, the efficiency of two data mining models including artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) was evaluated in o

Hybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling

S Janizadeh, M Vafakhah, Z Kapelan, N Mobarghaee Dinan
Journal Papers , , {Pages }

Abstract

Novel Bayesian Additive Regression Tree Methodology for Flood Susceptibility Modeling

S Janizadeh, M Vafakhah, Z Kapelan, NM Dinan
Journal Papers , , {Pages }

Abstract

Optimal prioritization of best management practices through simulation-optimization model

M Vafakhah, H Noor
Journal Papers , , {Pages }

Abstract

Flood hazard zoning using HEC-RAS Hydraulic Model and ArcGIS (Case Study: CheshmehKileh River in Tonekabon County)

S Pornaby Darzi, M Vafakhah, MR Rajabi
Journal Papers , , {Pages }

Abstract

Modeling Snowmelt Runoff Under CMIP5 Scenarios in the Beheshtabad Watershed

MB Raisi, M Vafakhah, H Moradi
Journal Papers , , {Pages }

Abstract

Integrated and Problem-Based Management of the Watershed using Strategic Planning Framework

SH Sadeghi, A Khaledi Darvishan, M Vafakhah, ...
Journal Papers , , {Pages }

Abstract

Application of artificial neural network and adaptive neuro-fuzzy inference system in streamflow forecasting

M Vafakhah, S Janizadeh
Journal Papers , , {Pages }

Abstract

Regional Analysis of Flow Duration Curves through Support Vector Regression

Mehdi Vafakhah, Saeid Khosrobeigi Bozchaloei
Journal PapersWater Resources Management , Volume 34 , Issue 1, 2020 January 1, {Pages 283-294 }

Abstract

A flow-duration curve (FDC) shows the relationship between magnitude and frequency of daily streamflows over a specific time period. Artificial intelligence methods eg Support Vector Machines for Regression (SVR) and Artificial Neural Network (ANN) are useful techniques in the prediction of FDCs in ungagged basins. Regional analysis of FDCs were performed through SVR, ANN and Nonlinear Regression (NLR) using streamflow with durations of 0.02, 0.10, 0.20, 0.50 and 0.90% as dependent variables and six watershed characteristics chosen as effective independent variables on 33 selected watersheds in the Namak-Lake basin located in central zone of Iran. The results shows that the most important watershed characteristics are weighted average heigh

Spatial Resolution Effect of Remotely Sensed Data on Flood Hydrograph Simulation

Javad Chezgi, Mehdi Vafakhah, Samereh Falahatkar
Journal PapersJournal of the Indian Society of Remote Sensing , Volume 48 , Issue 1, 2020 January 1, {Pages 97-112 }

Abstract

The objective of this study is to compare the effect of different spatial resolution of satellite images (Landsat-8, Sentinel-2 and Gaofen-1) for deriving LULC map and its effects on the performance of HEC-HMS lumped and Flood Hydro distributed models in flood hydrograph simulation in the Ammameh Watershed, Iran. The Soil Conservation Service Curve Number method was used for considering the rainfall loss rate in both models. The value of curve number was determined based on hydrologic soil groups and produced LULC maps from the satellite images with respect to soil moisture conditions. The performance of HEC-HMS lumped and Flood Hydro distributed models in flood hydrograph simulation was evaluated for 15 and five rainfall-runoff events, res

An improved land use classification scheme using multi-seasonal satellite images and secondary data

Sajjad Mirzaei, Mehdi Vafakhah, Biswajeet Pradhan, Seyed Jalil Alavi
Journal PapersECOPERSIA , Volume 8 , Issue 2, 2020 June 10, {Pages 97-107 }

Abstract

Aims: Generally, optical satellite images are used to produce a land use map. Due to spectral mixing, these data can affect the accuracy of land use classifications, especially in areas with diverse vegetation.Materials & Methods: In the present study, in order to achieve the correct land use classification in a mountainous-forested basin, four Landsat 8 thermal images were used with a few additional information (Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), slope angle and slope aspect) along with optical data and data of multi-temporal images.Findings: Results showed that thermal data, slope angle and DEM have a significant role in increasing the accuracy of land use classification, so that they increase th

Simulation of rainfall-runoff process using geomorphology-based adaptive neuro-fuzzy inference system (ANFIS)

SH Gholami, M Vafakhah, K Ghaderi, M Javadi
Journal PapersCaspian Journal of Environmental Sciences , 2020 April 25, {Pages 14-Jan }

Abstract

This research was conducted to present an integrated rainfall-runoff model based on the physical characteristics of the watershed, and to predict discharge not only in the outlet, but also at any desired point within the basin. To achieve this goal, a matrix of hydro-climatic variables (i.e. daily rainfall and daily discharge) and geomorphologic characteristics such as upstream drainage area (A), mean slope of watershed (S) and curve number (CN) was designed and simulated using artificial intelligence techniques. Integrated Geomorphology-based Artificial Neural Network (IGANN) model with Root Mean Squared Error (RMSE) of 0.02786 m3 s-1 and Nash-Sutcliffe Efficiency (NSE) of 0.9403 and Integrated Geomorphology-based Adaptive Neuro-Fuzzy Infe

Comparing performance of random forest and adaptive neuro-fuzzy inference system data mining models for flood susceptibility mapping

Mehdi Vafakhah, Sajad Mohammad Hasani Loor, Hamidreza Pourghasemi, Azadeh Katebikord
Journal PapersArabian Journal of Geosciences , Volume 13 , 2020 January , {Pages 417 }

Abstract

Flood is one of the important destructive natural disasters in the world. Therefore, preparing flood susceptibility map is necessary for flood management and mitigation in a region. This research was planned to compare the performance of frequency ratio (FR), adaptive neuro-fuzzy inference system (ANFIS), and random forest (RF) models for flood susceptibility mapping (FSM) in the Gilan Province, Iran. First, a geospatial database included 220 flood locations and eleven effective flood factors (slope angle, aspect, altitude, distance from rivers, drainage density, lithology, land use, topographic wetness index (TWI), and stream power index (SPI)) were produced. According to flood locations, 30–70% of them were used for training and validat

Prediction of snow water equivalent using artificial neural network and adaptive neuro-fuzzy inference system with two sampling schemes in semi-arid region of Iran

Hojat Ghanjkhanlo, Mehdi Vafakhah, Hossein Zeinivand, Ali Fathzadeh
Journal PapersJournal of Mountain Science , 2020 June 16, {Pages 12-Jan }

Abstract

Direct measurement of snow water equivalent (SWE) in snow-dominated mountainous areas is difficult, thus its prediction is essential for water resources management in such areas. In addition, because of nonlinear trend of snow spatial distribution and the multiple influencing factors concerning the SWE spatial distribution, statistical models are not usually able to present acceptable results. Therefore, applicable methods that are able to predict nonlinear trends are necessary. In this research, to predict SWE, the Sohrevard Watershed located in northwest of Iran was selected as the case study. Database was collected, and the required maps were derived. Snow depth (SD) at 150 points with two sampling patterns including systematic random sa

Eco-hydrological estimation of event-based runoff coefficient using artificial intelligence models in Kasilian watershed, Iran

Hossein Pourasadoullah, Mehdi Vafakhah, Baharak Motamedvaziri, Hossein Eslami, Alireza Moghaddam Nia
Journal PapersStochastic Environmental Research and Risk Assessment , 2020 July 28, {Pages 14-Jan }

Abstract

In this research, estimation of the Runoff Coefficient (RC) is carried out depending on land cover. Initially, RC modeling was performed using 54 hourly rainfall and corresponding runoff data during the period 1987–2010 in the Kasilian watershed. Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) models and effective factors including rainfall intensity, Φ index (the average loss), five-day previous rainfall and Normalized Difference Vegetation Index (NDVI) were used to estimate RC. The results showed that the ANN model was more efficient than the other two models and had Mean Bias Error (MBE), Coefficient of Determination (R 2), Nash–Sutcliffe Efficiency (NSE) and Normali

Regional flood frequency analysis through some machine learning models in semi-arid regions

Pezhman Allahbakhshian-Farsani, Mehdi Vafakhah, Hadi Khosravi-Farsani, Elke Hertig
Journal PapersWater Resources Management , Volume 34 , Issue 9, 2020 July , {Pages 2887-2909 }

Abstract

The machine learning models (MLMs), including support vector regression (SVR), multivariate adaptive regression spline (MARS), boosted regression trees (BRT), and projection pursuit regression (PPR) are compared to traditional method i.e. nonlinear regression (NLR) in regional flood frequency analysis (RFFA). In this study, the Karun and Karkheh watersheds, which is located in the southwestern of Iran, with the same climatic and physiographic conditions are considered. Fifty-four hydrometric stations with a period of 21 years (1993–2013) are selected based on the instructions of U.S. Federal Agencies Bulletin 17 B were applied for RFFA. The generalized normal (GNO) probability distribution function (PDF) is selected by the L-moment metho

Flood susceptibility assessment using extreme gradient boosting (EGB), Iran

Sajjad Mirzaei, Mehdi Vafakhah, Biswajeet Pradhan, Seyed Jalil Alavi
Journal PapersEarth Science Informatics , 2020 October 15, {Pages 17-Jan }

Abstract

Flood occurs as a result of high intensity and long-term rainfalls accompanied by snowmelt which flow out of the main river channel onto the flood prone areas and damage the buildings, roads, and facilities and cause life losses. This study aims to implement extreme gradient boosting (EGB) method for the first time in flood susceptibility modelling and compare its performance with three advanced benchmark models including Frequency Ratio (FR), Random Forest (RF), and Generalized Additive Model (GAM). Flood susceptibility map is an efficient tool to make decision for flood control. To do this, the altitude, slope degree, profile curvature, topographic wetness index (TWI), distance from rivers, normalized difference vegetation index, plan cur

The Efficiency of Some Infiltration Models in Three Rangeland Conditions (Case Study: Savojbolagh Rangelands)

M Mohseni Saravi, M Vafakhah, A Kohandel
Journal Papers , 2020 January , {Pages }

Abstract

Introduction Infiltration is one of the important hydrological processes and its accurate quantification is essential for many studies. Infiltration can be either measured in the field or estimated using mathematical models which vary from empirical to physically based models, including physically based models (e. g. Green and Ampt, 1911, Richards, 1931, Philip, 1957, Morel‐Seytoux, 1978, Haverkamp et al., 1990 and Corradini et al., 1994), conceptual models (e. g. Nash, 1957 and Diskin and Nazimov, 1995), or empirical relations (e. g. Horton, 1933, Kostiakov, 1932, Holtan, 1961 and Soil Conservation Service‐USDA, 1972)(Chahinian et al., 2005). The objective of this paper is to compare the performance of three widely used infiltration mo

Prediction and Analysis of Flood Zones under Climate Change Conditions based on CanESM2 Model’s Scenarios

Sajjad Mirzaei, MEHDI VAFAKHAH, Biswajeet Pradhan, SEYED JALIL ALAVI
Journal Papers , Volume 7 , Issue 2, 2020 January 1, {Pages 551-562 }

Abstract

This study aimed to predict flood zone in climate change conditions based on the fifth assessment report of the intergovernmental panel on climate change (IPCC) scenarios in the Talar watershed (Zirab city). To investigate the effect of climate change from six synoptic stations were used. Among the general circulation models (GCM), CanESM2 based on RCP 2. 6, RCP4. 5, and RCP8. 5 scenarios were applied for statistical downscaling of the maximum daily rainfall. To hydrologic and hydraulic simulation of flood were used from HEC-HMS and HEC-RAS models in the recent decades and the future. The results indicated that maximum daily rainfall will increase in the watershed. The results also showed that the increase in maximum daily rainfall in humid

Assessment of non-monetary facilities in Urmia Lake basin under PES scheme: a rehabilitation solution for the dry lake in Iran

Alireza Daneshi, Mostafa Panahi, Saber Masoomi, Mehdi Vafakhah, Hossein Azadi, Muhammad Mobeen, Pinar G?kcin Ozuyar, Vjekoslav Tanaskovik
Journal PapersEnvironment, Development and Sustainability , 2020 November 3, {Pages Jan-32 }

Abstract

The decline in Urmia Lake basin’s water resources has resulted in a severe drought of the lake. The drought of this hyper-saline lake has put lives of 6.4 million inhabitants at risk. This study was conducted to assess the technical and economic employability of a payment for ecosystem services (PES) method as a policy tool to improve water resources management of Siminehroud river basin which is the most important tributary of Urmia Lake basin. For this purpose, the target areas were identified after the development of a land-use map for the basin. Then, by recruiting the integrated interview method and distributing 398 questionnaires, the required data were collected to assess the employability of the proposed PES method. Among various

دروس نیمسال جاری

  • دكتري
    مدل هاي بارش - رواناب ( واحد)
    دانشکده منابع طبیعی و علوم دریایی، گروه آبخيز داري
  • كارشناسي ارشد
    آبخيز داري شهري ( واحد)
  • كارشناسي ارشد
    آبخيز داري شهري ( واحد)
  • كارشناسي ارشد
    برف و بهمن ( واحد)
  • كارشناسي ارشد
    برف و بهمن ( واحد)

دروس نیمسال قبل

  • كارشناسي ارشد
    مديريت و كنترل سيلاب ( واحد)
    دانشکده منابع طبیعی و علوم دریایی، گروه آبخيز داري
  • كارشناسي ارشد
    مديريت و كنترل سيلاب ( واحد)
  • دكتري
    هيدرولوژي پيشرفته ( واحد)
  • 1397
    صفايي ايوري, جواد
    ارزيابي عملكرد روش هاي توسعه كم پيامد بر مديريت سيلاب با استفاده ازSWMM در شهر كاشمر
  • 1398
    نقدي, مريم
  • 1398
    طاوسي, محمد
  • 1394
    اله بخشيان فارساني, پژمان
    ارزيابي كارايي روش هاي يادگيري ماشين براي تحليل منطقه اي فراواني سيلاب تحت سناريوهاي تغيير اقليم
  • 1395
    دبيري, دانيال
  • 1396
    جاني زاده, سعيد
  • 1396
    ايوبي ايوبلو, سارا
  • 1397
    نصيري خياوي, علي
    داده ای یافت نشد
    داده ای یافت نشد

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