Department of Watershed Management Engineering (1999 - Present)
Watershed Science and Engineering
, University of Tehran, Iran
Watershed
, Tarbiat Modares University,
Natural resources engineering, pasture and water management
, Gorgan University of Agricultural Sciences and Natural Resources,
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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
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
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
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
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
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
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
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
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 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
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
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
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
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