جستجوی مقالات مرتبط با کلیدواژه « validation » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

Evapotranspiration (ET) is one of the most important factors in the hydrological cycle and is a key determinant of energy equations on the earth’s surface. evapotranspiration estimates are important for hydrology, irrigation, forest and rangeland, and water resources management. The evapotranspiration drives the soil water-energy balance which is largely used in general circulation models and climate modelling. Consequently, river water flow forecasting, crop yield forecasting, irrigation management systems, river/lake water quality are all dependent on evapotranspiration levels. For this reason, it is essential to accurately estimate the water budget. Numerous models have been developed to estimate evapotranspiration using remote sensing methods. The review of recent research shows that remote sensing and the use of satellite images have a high ability to estimate the amount of actual evapotranspiration.

The aim of this study is calibrating the METRIC algorithm in estimating evapotranspiration in the Sohrin-Qaracheryan Plain, which is affected by flood spreading. This method has been used by many researchers around the world to estimate evapotranspiration. On the other hand, estimating the actual evapotranspiration is of great importance in the plains affected by the flood, especially in the Sohrin-Qaracherian Plain’s flood spreading. Therefore, in this research was conducted to estimate evapotranspiration using the metric algorithm in the Sohrin-Qaracherian Plain, for the optimization management of water resources in the region and regions with similar conditions. In this research, were used of the daily and hourly meteorological data of Zanjan Airport synoptic station from 2020 to 2021. These the data included minimum and maximum temperature, minimum and maximum humidity, wind speed average, sunshine hours and air pressure. To check the application of metric algorithm, were downloaded Landsat 8 images for 2020-2021 years and were done necessary corrections and preprocessing on them. Landsat images are available at 16-day intervals with a spatial resolution of 30 m and were obtained from the United States Geological Survey website (http://glovis.usgs.gov). After the images processing, is obtained the albedo, surface emissivity, land surface temperature, plant indicators, incoming-outgoing radiation fluxes, net radiation flux and the soil heat flux. Next, the sensible heat flux is calculated by determining the hot and cold pixels. Finally, evapotranspiration maps are plotted. In addition, for a better comparison of the results, were compared of the layers related to vegetation index include soil heat flux and land surface temperature in the different stages of the growth period. After extracting these indices, the evapotranspiration map was extracted using ENVI software.

Results show that daily evapotranspiration increases is directly related with increase in vegetation density. at the initial of the growth period, the range of evapotranspiration is estimated between 0.08 and 4.97 mm.d-1, while this value in the middle and late of the growing season is estimated in the range of 0.086 to 5.56 and 0.59 to 9.57 mm.d-1 respectively. Based on the results of this research evapotranspiration obtained from the soil water balance model and METRIC model were estimated as 24115 and 25648 m3, respectively. The results validation of evapotranspiration obtained from the metric model was compared with the actual evaporation and transpiration obtained from the soil water balance model, and the error coefficient was obtained equal to 5.97%.

According to the results of this research, it was determined that the use of energy balance models using the science of remote sensing provides the possibility of estimating evaporation and transpiration regionally. On the other hand, the calculation error percentage shows that the metric algorithm is accurate enough to estimate ET in the studied area.

Floods are of great importance in studies related to the use of water resources, flood management and the construction of dams, and the safety of structures related to dams is dependent on accurate hydrological studies. This study used geostatistical methods to determine the average maximum daily rainfall in the Amuqin catchment basin in Ardabil province as an input to the WMS model in order to estimate the maximum flood discharge. The Amuqin basin, which has an area of 78 km2 is located in the central part of Ardabil province, to determine the curve number of the basin, land use maps and soil hydrological groups (B, C, D) were integrated in the GIS environment. These maps were prepared using the Idrisi32 software and Landsat 8 satellite images. The basin curve number was estimated as 78.7. Different geostatistical kriging and global cokriging methods with circular, exponential, Gaussian, and normal models, weighted distance image method (IDW), Global Polynomial Interpolation (GPI), Local Polynomial Interpolation (LPI), and Radius Axis Relation Method (RBF) using geographic information system software were used to determine the average precipitation. The results showed that the normal Cokriging method has the lowest error for different return periods, then the average maximum daily rainfall for the basin was calculated using the normal Cokriging method for different return periods. Considering the rainfall-runoff events at the regional level in the calibration (RE=7.17, RMSE=0.44) and validation (RE=2.51, RMSE=0.0042) stages, was confirmed the accuracy and validity of the flood estimation by the model.

Precipitation plays an important role in climatic, water, energy and biogeochemical cycles. Several global and regional data sets currently provide historical estimates of this variable over Iran, including the MWEP and WFDEI forcing datasets and production of some institutions such as MOHC, SMHI and IITM. All these datasets provide data with different resolutions based on gage stations, satellite Images and models output. In this study, we do an inter comparison between these data sets during 1990- 2008. We also validate all ten data sets against independent ground station observations over 30 second-order basins of Iran. MSWEP and WFDEI have an acceptable compatibility with observational data on different spatial and temporal resolutions. RMSE and Bias are 5.68, 6.34 and 0.58, 2.75 for these two datasets during 228 months, respectively. However, it is needed that MSWEP improves in the western and northwestern parts of the country and WFDEI in June and September months. Our findings in this research provide valuable guidance for a variety of stakeholders, including rainfall- runoff and land-surface modelers, watershed management studies and data providers.

Iran's mostly mountainous topography, tectonic activity, high seismicity, diverse geological and climatic conditions, population growth-induced pressures on natural resources, and land-use changes in recent decades have created natural conditions for a wide range of landslides. Therefore, it is necessary to take appropriate measures to reduce the damage caused by landslides, identify their prone areas, determine the factors affecting them, and prepare their susceptibility maps. Thus, this study sought to identify the most important factors involved in the occurrence of landslides, and investigate the efficiency of the alternating decision tree models for preparing landslide susceptibility maps in the southwestern part of Kurdistan province (the communication route connecting Yozider to Degaga).

First, a distribution map with 175 landslides and 100 non-landslide locations was identified and classified into a ratio of 80% and 20% for training and model validation, respectively. Thirteen factors derived from topographic, land cover and rainfall data were selected for modeling using the Information Gain Ratio (IGR) technique. Then, the ADT algorithm was used to train and prepare the landslide susceptibility maps. Moreover, statistical criteria were used to evaluate the models for both training and validation datasets. Finally, the model's performance was evaluated in terms of the area under the receiver operating curve (AUC).

The highest IGR index values were found to belong to the distance from road, lithology, and road density, respectively. Furthermore, factors such as SPI, curvature, profile curvature, plan curvature, river density, distance from the river, and LS proved to have a negative effect on the modeling results, as zero value was assigned to these indices, which, in turn, led to the creation of noise. On the other hand, final modeling was performed and the 13 remaining factors were removed from the model due to their low impact on shallow landslides in the study area. Then, shallow landslide susceptibility maps were prepared based on the ADT algorithm, using quantile, natural breaks, and geometrical interval methods in the ArcGIS 10.2 environment. Finally, the best method was selected based on the landslides' frequency histogram in each susceptibility class of the maps. The results indicated that the natural breaks method was the best-known method, according to which the landslide susceptibility maps were divided into five classes, including very low susceptibility (VLS), low susceptibility (LS), moderate susceptibility (MS), high susceptibility (HS), and very high susceptibility (VHS).

The results of IGR-based factor analysis revealed that distance from roads, lithology, and road density had the highest effects on the occurrence of landslides, which could be attributed to the existence of landslide-susceptible formations such as marl and shale, and inappropriate human-set policies including road construction and incorrect cutting off of heels, as road construction provides the grounds for more penetration of water into sensitive soil formations and saturation of these soils under force. Moreover, the soil saturation on slopes under the force of gravity can facilitate the occurrence of landslides in the study area, which is consistent with the results found by Pham et al. (2015, 2016, 2019) who reported that the existence of lithological units susceptible to landslides (i.e., marl and shale) in the middle parts of the slopes played an important role in the occurrence of landslides in such areas, considering the fact that marl and shales layers might act as a lubricant for overlying saturated soils, and, therefore,  facilitate the occurrence of landslides. However, it could be argued that the upper floors and higher altitudes of the area are less susceptible to landslides due to the presence of crystalline and basaltic units that are resistant to any mass movement, especially landslides. As for the land vegetation, it was found that landslides mostly occurred in drylands that were formerly semi-dense and grassland forests, indicating the significant role of forest degradation and land-use change in landslide occurrence.
The designed models for both the training and validation data were evaluated using the Kappa, TP, specificity, sensitivity, accuracy, and squared statistical measures. Finally, the models' performance was examined through the AUC. Accordingly, the results of model validation showed that the ADT model had a relatively moderate performance with the sub-curve level of 0.665. Furthermore, the sub-curve level of the ADT model was found to be 0.677 for the training data. Finally, the study area was divided into five classes, including the very high, high and moderate, low, and very low sensitivity. It was also found the severity of landslides increased when moving from low-sensitivity classes to the high-sensitivity ones, indicating higher chances for the occurrence of landslides in areas with high sensitivity. Therefore, considering the high influence of the roads in the model proposed in this study, it is suggested that the priority of taking appropriate measures to prevent and/or control landslides be taken into account so that the effect of road construction in the study area could be reduced. Moreover, in cases where future road development operations are considered along the route, principles of road construction and the stability of the slope must be strictly observed.

Due to the limited number of meteorological stations especially in mountainous areas, the use of satellite data to extract rainfall is very important. On the other hand, the lack of spatial appropriate rainfall data is one of the major challenges in flood or drought prediction and timely warning in this case. One of the available solutions in this case is to measure rainfall from space.

The aim of this study was to investigate the accuracy of rainfall data obtained from TRMM satellite images in Taleghan watershed on a monthly and annual time scale during the period 2010 to 2015. For this purpose, TRMM images with three-hour spatial resolution were received during the statistical period for the study area. Then using the sum of three hours rainfall, daily rainfall was estimated. In the following the adjustment coefficients were also presented to reduce the error of rainfall data, and finally the accuracy of satellite image data was compared with two common interpolation methods i.e. invers distance weighting and Kriging.

The results of error indices showed that the rainfall of TRMM images is well correlated with the data of ground stations, especially Joestan station, but in some months, there is a problem of under-estimation and over-estimation. For this reason, correction coefficients were applied to solve this problem, which on average in most months; this coefficient was calculated less than one, which indicates the overestimation of TRMM image precipitation data. Examination of the corrected data showed that by applying the estimation coefficients, in addition to solving the overestimation problem, the error rate was also reduced and the Nash-Sutcliffe performance index was somewhat improved. The root mean square error (RMSE) at Garab station also decreased from 88 to 26 mm in annual rainfall time scale by applying the adjustment coefficients, which indicates an increase in data efficiency after the application of correction coefficients. The results of comparing the modified data with interpolation methods showed that in all error indices, the modified TRMM data is more efficient in estimating rainfall.

Overall, it can be said that rainfall from TRMM images can give satisfactory results if adjustment coefficients are applied, and in areas with a shortage of meteorological and data stations, it can be a reliable source of rainfall data.

The spatial quality of the Digital Elevation Model (DEM) has a great effect on the Soil and Water Assessment Tool (SWAT) semi-distributed model. The purpose of this study was to evaluate the effect of spatial accuracy of three DEMs with spatial resolutions of 10, 50 and 200 m on the results of daily discharge simulation in the Arazkuseh subwatershed located in Gorganroud watershed, Golestan province, Iran. To do this, after-mentioned DEMs was obtained.  First, the model was run using a DEM with a spatial resolution of 10 m and climate data from 1998 to 2013. So that 1998 to 2000 were used to warm-up the model, 2001 to 2009 to calibrate and 2010 to 2013 to validate the model. In the validation period, the results of coefficient of determination ranged from 0.40 to 0.60 and Nash-Sutcliffe efficiency criteria ranged from 0.37 to 0.58. Both criteria had a decreasing value in the validation phase compared to the calibration phase. The PBIAS criteria was negative for DEMs with 10 and 50 m spatial resolution, respectively which indicates overestimation, and positive value ​​for DEM with 200 m spatial resolution, which represents the overestimation of the model. The results showed that the spatial resolution of 50m is the best spatial resolutions to simulate the discharge on a daily time scale for Arazkuseh watershed.

Today population growth, inaccurate management practices and land degradation processes have caused many watersheds to be severely degraded, leading to increased runoff and sediment. In this study, the aim of the study was to investigate the effect of land use change on surface water fluctuations in the Doiraj watershed. Landsat satellite imagery was used for three periods of 1995, 2006, and 2015, it was found that the major changes occurred with decreasing forest and rangelang  levels and increasing land barren. For modeling rainfall-runoff, a SWAT computer model was used. Then, using the SWAT_CUP model, The performance of the model was evaluated using statistics, NS, R2, P_FACTOR and R_FACTOR. After calibration and validation, the values of these coefficients were 0.58, 0.60, 0.81, 0.87 for the calibration period and 0.56, 0.57, 0.81 and 89 for the validation period of the model, respectively was obtained. Annual runoff estimation indicated that changes in forest, pasture and wasteland land use from 1995 to 2015 increased the outflow runoff the basin from 3.91 to 5.85 m3 / s.

Landslides are one of the major types of slope instability which cause enormous financial losses and casualties; therefore, it is imperative to consider them in the study of geomorphology, erosion and sedimentation in important watershed. The aim of the present study was to investigate and rank the landslide susceptibility using a random forest method in the Sadat Mahalleh Watershed, Sari. Elevation from the sea level, geology, land use, slope (degree, direction, shape and length), distance from linear elements such as faults, streams and roads, normalized difference vegetation index and slope form were considered as effective parameter in the landslide occurrence. Zoning landslide sensitivity was achieved by coding in the R and GIS10.3 soft wares. In order to determine the weight of each of the factors and classes effective in zoning the landslide sensitivity and validation of landslide potential prediction maps, the abundance ratio method and the receiver operating characteristics (ROC) were used. The results indicated that among the effective factors, vegetative cover and land use, had the most paramount impact on the landslide occurrence in the study area. A landslide sensitivity map was prepared using the random forest method by dividing into five class, namely: very low (3.85%), low (5.38%), moderate (23.08%), high (50%) and very high (7.69%) sensitivities. Validation of the results of the landslide sensitivity zoning maps, using the relative factor characteristics index diagram, indicated that the area under the curve (AUC) for random forest method was 0.709 and its standard error was 0.10. It is reasonable to claim that the forest method provided an acceptable accuracy for mapping the landslide sensitivity. A landslide sensitivity map provides prone complete and accurate information for natural resource managers to detect the landslide areas.

With the increase in population, the importance of groundwater resources as one of the most important sources of drinking water in the arid regions becomes apparent. In this research, in order to determine the areas with groundwater potential in the city of Torbate Jam and prioritizing the effective factors, hierarchical analysis methods and maximum entropy method using MaxEnt model and the factors of distance from fault and fault density, lithology, slope, slope direction, distance from the waterway and drainage density, elevation, land use, slope curvature, topographic humidity index and topographic position indicator was used. Also, for assessing these two methods, the Receiver Operating Characteristic (ROC) was used. From 220 sources, 30% were randomly assigned as validation data and 70% were categorized as test data in maximum entropy method. Results showed that 29.6% of the watershed had high groundwater potential according to the maximum entropy method. Based on Jack-Knife Diagram, DEM, slope, distance from fault and lithology were the most important factors affecting groundwater potential, respectively. The Area Under the Curve (AUC) in the maximum entropy method indicated a precision of 91% (excellent) at the training period and 80% (very good) in the validation period to determine areas with potential for groundwater. Based on AHP method, 34.4% of the area has groundwater potential, and the slope layers, lithology, elevation and distance from the faults were the most important factors, respectively and accuracy of this method was 73%. The results showed that applying AHP and maximum entropy methods, while saving time and cost, have a good ability to predict the potential of groundwater and the maximum entropy method has more superiority than the hierarchical analysis method.

In this study SWMM software has been calibrated with real meteorological and hydrometric data at the North of Tehran basins and simulation parameters have been obtained. For this purpose, five rainfall events and runoff data related to these rainfalls, recorded at the outlet of Zargandeh catchment were used. This model is calibrated with three events and verified with two other events. Also, in this simulation the peaks of flood, outflow hydrographs, runoff volumes and peak flood times is obtained. The root mean square error are obtained for outlet hydrographs for the first to fifth events were 0.05, 0.22, 0.4, 0.37 and 0.16, and the Nash-Sutcliffe coefficient are obtained 0.91, 0.94, 0.93, 0.9 and 0.94, respectively. Also, the percentage of difference of the flood discharge peak modeling and observations for first to fifth events are calculated 7.33%, 9.69%, 5.8%, 5.6% and 9.93% and for runoff volume, this percentage difference are calculated -8.82%, -3.08%, 8.8%, -19.43% and 5.11%, respectively. Based on these results, the performance and application of this model to simulate runoff in this area is acceptable and can be used to manage and control urban runoff.

One of the most important measures for flood management is preparation of a flood susceptibility map. The purpose of this study was to identify susceptible areas to flooding using the maximum entropy model in the Tashan Watershed, Khuzestan Province. A flood inventory map was prepared for the statistical analysis. Of the 169 flooding occurrences, 70% were used for the model calibration and 30% were used for validation. Ten flooding factors, namely: altitude, slope aspect, distance from river, drainage density, slope angle, land use, distance from road, topographic wetness index, plan curvature and lithology were used. The effect and contribution of each environmental parameter was calculated using the response curves and the Jackknife method. Finally, a flooding susceptibility map was prepared in four classes. The receiver operating characteristic curve was used to evaluate the accuracy of modeling. The results indicated that the maximum entropy model had a very good accuracy (AUC=0.885) in identifying the prone areas to flooding, and the land use and distance from road with 50.5% and 20.6% contribution, respectively, had the most impact on the occurrence of flood inundation. Also, about 25% of the watershed area was highly sensitive to flooding. Regarding to very good prediction accuracy of maximum entropy model in detecting susceptible areas to flooding, it is recommended to apply this model for the preparation of the flooding map in other watersheds, especially areas lucking hydrometric stations.

Simulation and monitoring of future trends of land use changes is an important challenge for researchers and decision makers. This study uses the spatio-temporal models to simulate and evaluate the future trend in land use changes of the Kozehtopraghi Watershed in the Province of Ardabil. Landsat ETM+ (2000), TM (2010) and OLI (2018) images were used. The CA-Markov model was used to simulate land use changes in the future period (the year 2036). To assess the validity of a CA-Markov model, the predicted land use map of 2018 was compared with the classified map of 2018. Based on the Kappa accuracy coefficient with the value of 0.8, the accuracy of the simulated results was acceptable. The land use map of 2036 has been predicted using the CA-Markov model. In order to analyze and detect the changes in 2000 to 2036, the LCM model was employed. The results showed that the areas of irrigated farming, pastures and orchards will increase by 80.52, 36.90, and 5.76 percent, respectively. On the other hand, the rainfed agriculture and water bodies areas will decrease by 43.43 and 91.40 percent, respectively. In addition, based on the results of the change process, extreme changes in rainfed agriculture, orchard, pasture and irrigated agriculture will happen in the central, south-west, south-west and downstream parts of the Kozehtopraghi Watershed.

Relationships between river water quality parameters and physical, geochemical and biological processes carried between basin resources (soil, vegetation, geology, land use, etc.), meteorological variables (temperature, precipitation, snowmelt, etc.), River hydrological variables (flow discharge), as well as human interventions are often very complex, nonlinear and non–deterministic in a way that makes their complete understanding impossible. In this situation, the use of computational intelligence (such as artificial neural networks) is a useful tool in simulating and estimating river water quality variables such as suspended sediment load. In the present study, by combining open source GIS libraries and neural network models (with and without supervisor), an intelligent GIS system has been designed and coded that can estimate daily suspended sediment load under univariate or multivariate conditions. The results of applying this system to Mazaljan River Watershed at Razin hydrometric station showed that this system is able to simulate suspended sediment load with proper performance and validation  (with root mean square error of 1033 tonday-1, mean absolute error of 455 tonday-1 and Nash-Sutcliffe efficiency of 0.89 for the test data set). In general, this system can be used as a national infrastructure in the simulation and management of suspended sediment in all hydrometric stations in the country by relevant organizations.

Watersheds are a vast area with different conditions that simulation and studying the processes of runoff and sediment production in these area are critical for managing and maintaining resources. The Kozatopraghi watershed with an area of 766 km2 is located in the south and upstream of the Garesou basin, Ardebil province. Between the hydrological models, based on the capabilities of the SWAT model, this model was used to simulate the hydrological processes of the area in sub watershed scale. Also, for a more accurate examination the rainfall simulator was used in the hydrologic response units (HRUs), the obtained results were generalized to the sub watershed scale. The results of SWAT model were assessment with statistical parameters R2, NSE and RMSE. In the calibration and validation periods these coefficients for simulation runoff obtained 0.8-0.74, 0.71-0.69 and 0.32-0.33, respectively, and for simulation sediment yield obtained 0.75-0.66, 0.72-0.64 and 35.27-26.39, respectively. Assessment the obtained results of rainfall simulator showed that Loamy soils, located in 8 HRUs have high effect in sediment yield production. The maximum runoff and sediment yield by rainfall simulator and SWAT model were observed in sub watersheds 13 and 1, that indicating poor and inappropriate conditions in these subwatersheds. Comparing the study methods showed that the SWAT model have better performance than rainfall simulator for simulation runoff. Also, the estimated sediment yield by this model is lower than rainfall simulator that is close to observed values.

Identification of areas that prone to subsidence and estimation of its rate plays an important role in the control management of this phenomenon. Differential interferometry radar technique (D-InSAR) with very high accuracy is one of the most suitable ways for identify and measure the rate of subsidence. In this study, to identify and measure the subsidence in Mahyar Plain differential radar interferometry techniques have been used in the period of 2004 to 2010. For this purpose, eight pair images of time series were used from ASAR sensor in C-band radar in ascending passage. The method used in this study is based on laboratory-field surveys. For validation of technique, survey data such land use and geology maps and data of observation wells in the region were used. As a result, maximum rate of annual subsidence in the area was 6.4 cm yr-1. Also, results showed that the highest amount of subsidence occurred in areas under cultivation and due to excess extraction of groundwater and subsidence of aquifer surface. The rate of subsidence was obtained 0.384 cm for each two cm drop of water table according to the relationship between subsidence and the changes of piezometric wells surface.

Water harvesting and surface runoff control systems are the important components of urban planning and development and ignoring these issues is likely to raise crisis. In order to decrease the urban flood damages, the urban runoff is needed to be evaluated correctly. Today some of models are developed for urban runoff simulation. One of the most important models in evaluating and management of urban runoff is SWMM. The aim of this study is to evaluate SWMM efficiency on urban runoff simulation in Izeh urban basin. To define design rain, concentration time was computed while considering the duration of the cloudburst as equal to this time. Also three performance indexes of Nash-Sutklif, errors sum of squares and Bias were used in order to model calibration and validation. Moreover, areas of high susceptibility were determined for two, five, 10, 20 and 50 years of return periods. Later, it was found that the principle reason of inundation is the lack of sufficient capacity of water ways. In some points, even with sufficient capacity, inundation occurs, confirmed by model. In these cases the causes stem from the improper design and construction of bridges which has lessen the size of water ways and caused junk clogging. Three rainfall events were recorded on March 13, 2017, March 28, 2017 and April 6, 2017 which were considered in order to calibrate and evaluate the model performance. Along with that, the discharge, depth and velocity of water at the outlet were considered as well. The results of the SWMM application gave indication of a good matchup between discharge, depth and the velocity of runoff for observed and estimated data. In this case, this model could be utilized to well predict the inundation hazard, design and the estimation of the cost and volume of drainage systems, management of watershed and prioritization of region to address flooding issues.

For suitable programming and management of water resources, access to perfect information from the discharge at the watershed outlet is essential. In most watersheds, the hydrometric station is not available; then, different models are used to simulate the discharge within watersheds without data. The selection of preferred model for rainfall- runoff simulation depends to the purpose of modeling and available data. In this research the conceptual rainfall- runoff models, SimHyd and AWBM and neural network models, MLP and RBF (by regard the less need to measured data) were used to model of rainfall- runoff process in Bar-Aryeh watershed of Nishabur. The length of data was 30 years (1983-2012) and the length of calibration and validation periods was 5 and 7 years, respectively. RRL and SPSS programs software were used for simulation of runoff. Nash - Sutcliff (ENS), coefficient of determination (R2), the root mean square error (RMSE) used to evaluate the models. Results showed that hydrological models simulate rainfall- runoff process in Bar Aryeh watershed of Neyshabur better than neural network models. Between mentioned models, the SimHyd with ENS, R2 and RMSE equal to 0.632, 0.8 and 0.02 respectively in the calibration period and 0.541, 0.74 and 0.08 in the validation period has better performance than other models which used in this research. The results showed that the Rosenbrock's search optimizer for the hydrological models and the function of tangent hyperbolic for the neural network models have more accurate operations than other optimizers. In addition, used models simulate the minimum and average values of the flow with an acceptable accuracy but the simulation of maximum values did not do well. Because these models do not regard the type and intensity of precipitation, lag time from snowmelt and concentration time of watershed.

Gully erosion is types of water erosion that its occurrence and development lead to the loss of fertile agricultural soil, depletion of soil and groundwater. The objectives of this study were to investigate gully erosion and determine the sensitive areas in terms of gully erosion on the Sorkh Abad Watershed. Information layers such as slope, land use, distance from faults, aspect, distance from the stream network, distance from roads, lithology, rainfall and altitude above the mean sea level were prepared, classified and weighted using the Arc GIS software. Analytic network process (ANP) was used in the data analysis and modeling. The DEMATEL method was used to design the network structure among the criteria. The receiver operating characteristics (ROC) and the area under curve (AUC) were used for validation of the gully erosion map. The results indicated that four criteria: distance from roads, lithology, land use and precipitation were the most important factors leading to cause gully erosion. The results of validation of the gully erosion zonation map showed that the AUC with 0.684 had an appropriate function to determine susceptibility of the localities to gully erosion in the studied watershed.

Today, increase in flood frequency and intensity, and flash flood events have caused environmental issues and increased human and economic losses. As a result, numerous tools and new programs for flood forecasting systems and risk management programs are being used in the relevant period. There are models that make it easy to be used for this purpose. In this research, MISDc model was applied to investigate the runoff of Kashkan Afrine basin in Lorestan province. MISDc is one of the semi-distributed rainfall runoff models that is implemented to estimate floods. In order to simulate daily river flow using the MISDc model, daily rainfall, temperature and flow data of the basin, for a 12 year period (2004-2015) were used. In the calibration period (first 9 years), by calibrating 9 parameters of the model, the optimum values of river flow were obtained, and were used in the validation period (the last 3 years data). In order to evaluate the simulation accuracy of the model, Nash-Sutcliff (NS) and R2 criterea were used. According to the results, simulation accuracy based on evaluation criteria values (NS and R2) were 0.68 and 0.56 for the calibration period (2004-2012) and 0.74 and 0.57 for the validation period (2013-2015), rspectively. These results indicate the ability of the model to simulate the runoff of the basin. Moreover, the results of observed and simulated hydrographs comparison show that the model can well simulate the flow of the studied basin, especially peak peaks.