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

The study of erosion and sedimentation is a crucial aspect and an integral part of watershed studies in a basin. The methods employed to prevent erosion and sedimentation play a significant role in basin management planning. However, due to the lack of accurate statistics on erosion and sediment amounts, it becomes necessary to utilize estimation models for assessing erosion and sedimentation. These models are often region-specific and may have a wide range of uncertainty in different areas. One notable benefit of modeling, regardless of its type, is its ability to analyze natural processes within the basin. On the other hand, understanding erosion and sedimentation processes in a region can be an effective step in managing, planning, and prioritizing available resources. In this research, the aim was to determine the sensitivity of the MPSIAC model to the relationships among its parameters. Additionally, the goal was to assess the model's sensitivity to changes in these parameters and their impact on sedimentation. By identifying the influential ground and atmospheric parameters, it becomes possible to prioritize their effect on water sedimentation in the region.

Through the sensitivity analysis of the model, it is possible to identify the flexibility of the model to the changes of its various parameters. It is also possible to determine the relationships between model variables and prioritize the parameters affecting the model output. Sensitivity analysis can be used in the calibration stage, in such a way that results are accurate, and time and cost are saved. In this study, to analyze the sensitivity of the MPSIAC model, first, the nine parameters of the model were examined. Then, according to the arrangement of different parameters in the structure of this model, the parameters were numbered from X1 to X9. Then, the score range of each parameter was entered in its calculation table according to the modified model of PSIAC. In this step, the average of each parameter was calculated according to its upper and lower limits. To achieve this, the nine parameters of the model were standardized, and the sedimentation rate was estimated by varying each parameter within its lower and upper limits while keeping the other parameter(s) fixed at their averages. With each change, the sediment discharge was calculated.

In the analysis of the results, the slope of the curves of the standardized parameters relative to the dependent variable (specific sediment) is considered. So that, any part of the curve that has more slope changes means that the model is more sensitive to minor changes in that parameter in that interval. In this study, the results of this sensitivity analysis reveal that the slope factor and runoff volume have a considerable influence on sediment yield. For slopes up to approximately 15%, the slope parameter has a relatively smaller impact compared to other parameters. However, as the slope value increases, its effect becomes more pronounced, indicating that high slopes (greater than 15%) have the greatest impact on sedimentation. The runoff factor exhibits a sudden increase in sensitivity at higher flow rates.

In examining the effect of different parameters on the model performance, the parameters that had the largest slope of changes can be considered as the most effective parameters in sedimentation of the basin. In other words, the model demonstrates high sensitivity to slight changes in specific peak discharge values above 20 m³/s/km2. Therefore, implementing integrated (biomechanical) management measures to control slope steepness and reduce runoff volume can be an effective strategy for mitigating sedimentation. Therefore, based on the results, the role of land factors is very important, such as slope and then runoff, in the erodibility and sediment production in basins. Of course, it should be noted that climatic parameters directly and indirectly affect the amount of peak flood discharge and should not be neglected for their importance and influence in flooding and subsequently the erodibility of the watershed.

Accurate estimation of reference crop evapotranspiration (ETo) is important in hydrological studies and water resource management that can influence the planning and policies for optimal allocation of agricultural water resources. For this purpose, in the present study, sensitivity analysis was employed to evaluate the effects of the changes in meteorological parameters (vapor pressure deficit (VPD), Mean Temperature (Tmean), net solar radiation (Rn), wind speed (u) and actual vapor pressure (ea)) on reference crop evapotranspiration (ETo) within the possible range of ±20% on a daily and growing season scale. ET0 was calculated by The Food and Agriculture Organization of the United Nations (FAO) 56 Penman-Monteith approach using long-term climate data from 13 meteorological stations of Khorasan Razavi province in northeast Iran. The sensitivity analysis indicated that ETo changes positively with vapor pressure deficit, mean temperature, net solar radiation and wind speed, while it changes negatively with actual vapor pressure. Also, over the whole period, ETo was most sensitive to vapor pressure deficit (VPD), followed by mean temperature (Tmean), net solar radiation (RN), actual vapor pressure (ea) and wind speed (WS). In the the warm growing season, ETo was most sensitive to vapor pressure deficit (VPD), followed by mean temperature (Tmean), net solar radiation (RN), wind speed (WS) and actual vapor pressure (ea) while, in the cool growing season, ETo was most sensitive to vapor pressure deficit (VPD), followed by actual vapor pressure (ea), Mean Temperature (Tmean), net solar radiation (RN) and wind speed (WS). The results showed that the sensitivity of ETo in different stations and different growth periods is different. The findings of this study showed that the importance of effective parameters on the reference crop evapotranspiration (ETo) was different between the hot and cool growing season of the region, which should be considered in sustainable planning of water resources and management of agriculture in Khorasan Razavi regions for development of adaptation strategies in arid climatic conditions.

The accurate estimation of daily Evapotranspiration (ET) improves the efficiency of water resources management especially in areas where suffers from water scarcity. In the present study, ET was estimated using surface energy balance algorithm for land (SEBAL) and the experimental model of FAO-Penman-Monteith (FPM) and finally compared and verified with those calculated from pan evaporation method. Since many climatic factors affect the ET values, the sensitivity analysis of SEBAL inputs variables was finally cerried out to determine the key affecting parameters. In this regard, by SEBAL model and emplying the satellite data of Landsat 8 (OLI and TIRS sensors), the ET values were estimated on a daily scale for the time period 2018/07/25 to 2018/09/11. Results of SEBAL model showed that the values of SEE, RMSE and R2 indices were equal to 1.27, 0.76 and 0.77 mm /day and 0.91, 0.6 and 0.92 mm /day, while compared with those of FPM and pan evaporation methods, respectively.

Wind erosion is one of the important aspects of land degradation in arid and semi-arid areas. Countries located in arid and semiarid belt of the world including Iran have always been faced with this phenomenon. Wind erosion assessment models use different scores to determine the erosion rate in a given class. However, due to the spatial and temporal complexities and the multiplicity of factors affecting the ecological conditions of the region, it is impossible to fully rely on such results and use them for targeting and prioritizing the areas and providing suitable solutions for their management. Nevertheless, Bayesian Belief Networks (BBN) are based on probabilistic approaches which display the uncertainty in the evaluation of phenomena in terms of probability. These Networks are essentially developed as tools for analyzing decision-making strategies under uncertainty. Accordingly, this study set out to estimate the potential of the BBN as a relatively new and probable means for estimating the wind erosion and, finally, to evaluate the management scenarios for controlling wind erosion in Isfahan province.

To start the process of modeling the Bayesian Beliefs networks with regard to the purpose of the study, suitable variables were selected for modeling the BBN by reviewing the related literature and asking the experts opinions. In the next step, the relationships between the variables were determined, using the impact graph. The impact diagram shows the relationships and effects of the variables on each other and on the output node of the model (the amount of wind erosion). Finally, in order to create a model and formulate the conditional probability tables of model variables, the impact diagram was transformed into a BBN model, using the Netica software. The Bayesian Belief Network Model was validated by sensitivity analysis, the results of the analyses carried out by experts, and the comparison of the obtained map of Bayesian model with the wind erosion map in Isfahan province. The ROC curve characterizes the relative performance of each model. The area under this curve is called the AUC and the model with the highest AUC has the highest relative Function. The AUC equals 0.5 equivalent to the neutral model and the closer this value is to 1, the higher the Function of the model would be.

Having applied the final model of Bayesian’s belief network, the causal relationships between the variables affecting the rate of wind erosion were shown. The target variable in this model was wind erosion. As the findings of the study indicated, geological variables, land management, topography of the area, soil texture, rainfall and frequency of erosive winds were considered as key variables of the model. In order to run the model, information about each of the key variables was taken from the area at the province and fed into the model. Finally, the model was designed to estimate the amount of wind erosion in each sampling point. Based on the output of the model, the probability of wind erosion in each unit was used to zone the probability of wind erosion in the study area. The overall sensitivity analysis of the model also indicated that the wind erosion rate of the area had the most sensitivity to the wind velocity and speed, the frequency of erosive winds, and the protection of the earth's surface. On the other hand, the least sensitivity was reported for such variables as soil texture, geology and topography. Moreover, a high correlation between the results of the two models was found. According to the results of the ROC curve, the area under the AUC curve for Bayesian network model is 0.79, suggesting acceptable model accuracy.

It was shown that the BBN presented the probability of different wind erosion rates for each sampling point in the study area. In BBN, the results are expressed in the probability language and managers are to choose and implement timely and appropriate management decisions to reduce the risk of wind erosion in the region. The designed model in this study could be implemented in all regions. However, depending on the conditions of each region, the number of variables in the model may be increased or reduced. The study used Bayesian belief networks in the critical areas of wind erosion in Isfahan province. This model well demonstrates the importance of implementing wind erosion control projects to assist in sustainable land development to prevent migration, foster agricultural conservation, increase industry development and bio-resources in desert areas with desertification control.

Estimating the runoff in the basins lacking statistics is always considered by researchers And managers in planning, development and implementation of many projects of water Resource. One of the methods for estimating the runoff is to use geomorphology instantaneous unit hydrograph which estimates the hydrograph of flood based on quantitative geomorphology factors. In this study, first the quantitative geomorphology of the Nash and Rousseau methods including k (scale parameter), n (shape parameter) and speed (Meters per second) were obtained. In the next step, instantaneous unit hydrograph was obtained by GIUH method and ultimately flood hydrograph was created for 20 selected events from 1991 to 2007 in kasilian region and compared to the observational hydrographs. Through sensitivity analysis, infiltration Index (φ) had higher sensitivity analysis, infiltration Index (φ) had higher sensitivity and n (Parameter of shape) were less sensitive. Also, by comparing peak discharge values and computational hydrograph widthsin the state where the discharge of hydrographs is 50 and 75% of peak discharge,observational data revealed that the Rousseau method and lower error than the Nash method and Nash method had a higher accuracy than Rousseau in terms of the volume and time.

Parks and green spaces are one of the most important elements of cities. The design and function of urban parks should be in line with the requirements of urban life and in response to the needs of citizens, as this can be used to create a healthy urban environment. The purpose of this research is to model the satisfaction of urban parks visitors using the artificial neural network. In this study, a multi-layer perceptron network was used to process the data with the intelligent neural network tool. First, 103 urban parks were selected in Karaj and Tehran, and information about regional, service and aesthetic variables was collected in all parks. Then, the collected data was considered as network input and the results of satisfaction level assessment as network output. The value of determination coefficient (R2) in this study was 0.72 which indicates the suitability of artificial neural network for satisfaction modeling in urban parks. The results of sensitivity analysis showed that variables of landscape quality, number of sports fields, food centers, and barbeque have had the most impact on satisfaction of urban parks. Therefore, in planning and managing public places such as urban green spaces, consideration of users' perceptions of the environment should be highlighted.

Spur dikes are one of the common methods to protect rivers against erosion. Scouring around the spur dike is an important factor that can disorder the structural performance. Using protective spur dike is proper technique reduce the scour amount. In this research, the GMDH and GEP model used in order to evaluate and estimate the effect of various parameters of protective spur dike on scour depth around the main spur dike. Important parameters consist of protective spur dike angle (Ө), protective spur dike length ( ), main spur dike length ( ), distance from main spur dike (X), flow intensity ( ) and Froude number ( ) are considered as the model inputs. Results of training set and testing set indicate that GMDH model is better than GEP model as the MAE and RMSE error in the testing set data are reduced from 0.063 and 0.086 (in GEP model) to 0.045 and 0.061 (in GMDH model) respectively. Also, the Nash-Sutcliffe criteria increased from 0.51 (in GEP model) to 0.75 (in GMDH model). In the following, using the GMDH and GEP models and according to the nature of the problems, the equations are suggested to predict the scour depth reduction in the first main spur dike. The results of sensitivity analysis indicate that the most effective parameter in decreasing the scour depth around the first main spur dike is ( ).

Due to the rapid urbanization in Iran, different issues have emerged in its major cities such as street flooding, dispersal of dangerous chemicals and flood-related risks due to the lack of a proper drainage network and disorders in the flood channels; and, Mashhad is not exempt from these issues. The aim of this study is to determine the most important parameters affecting (area of impermeable surfaces has the highest impact, followed by the Manning roughness coefficient in permeable areas, the equivalent width, slope, height of storage in impermeable areas, and impermeable areas without surface storage) runoff generation in District 9 of Mashhad city and to evaluate the performance of the SWMM model. The SWMM model was used to simulate runoff and identify critical areas. In order to determine the duration of the design storm, time of concentration was measured and used as a surrogate. Sensitivity analysis showed that among the eight considered parameters, area of impermeable surfaces has the highest impact, followed by the Manning roughness coefficient in permeable areas, the equivalent width, slope, height of storage in impermeable areas, and impermeable areas without surface storage. The model calibration results showed a good agreement between the simulations of the four incidents with a NS higher than 0.5. The obtained NS confirms the SWMM model’s performance and accuracy. The RMSE was measured 0.6, 0.65, 0.58 and 0.91 respectively for the incidents dated 2015/1/10, 2015/2/15, 2015/2/21 and 2015/3/6, as well as 0.61 and 1.14 for the incidents dated 1394/1/7 and 1394/1/11, which suggest an acceptable model performance. Given the results, there is a good agreement between the simulated and actual runoff volumes which is an indication of the good performance of the SWMM model in predicting urban flood and the model could then be used in urban flood management projects and drainage network planning in Mashhad.

Babolroud watershed and Mazandaran province of such as are that in recent years, different extreme events have been happened. On this basis, emphasize necessity to investigate further on impact of climate change on watershed runoff. This work is done by climate change and rainfall-runoff models that able to simulate and calculate of climate changes impact on hydrologic components Including precipitation, temperature, evapotranspiration and runoff. In this study, using this method, the data model HadCm3 general circulation of the atmosphere with the use of LARS WG model according to A2 (pessimist), B1(optimist) two scenarios for the time periods 2046-2065 and 2080-2099 be downscaled. Then predicted variables were introduced to SIMHYD rainfall-runoff model. The simulated daily runoff in the period 1982-2011, were selected the best period of calibration and verification with regard to the duration and optimizing statistical parameters and model sensitivity analysis process, in order to minimize the simulation error. The results showed a reasonable match of the runoff changes pattern between the observed and simulated. So that relatively high values of coefficients of determination (R2=0.73) and Nash-Sutcliffe (0.53) during calibration, validation, indicated the model efficiency to simulating common and minimal flow. The results, showed some changes in the average annual rate SIMHYD, +23 to +58 percent that the highest increase rate in October and November and the largest decline in July and August in the future years are. The situation of low rainfall months will be shift to more drought and rainy months toward the flooding.

Application of continuous hydrological models and its calibration methods development using geographic information systems are increasingly expand. The purpose of this study is calibration and validation of SMA model and simulation of continuously river flow in Neka basin. In this study, firstly the temperature and precipitation variables prepared for the model and then SMA parameters were calculated for different period of calibration. The parameter estimates needed analyzed in GIS for the different period and finally validation and its accuracy was estimated. Generated hydrograph from the scenarios set of model parameters was compared in Gelvard observation station. Annual calibration and validation results showed that the observed and simulated correlation coefficient was 0.80 and 0.75, respectively. Also, for the validation period the objective function of AEPM (10.17) had a better matching than the calibration period (18.26). By simulating the flow river using climatic events which continuously over a long period of time, the daily discharge is obtained and showed the satisfactory results. Using this model for estimating of stream flow, the study of climate change, changes in land use and therefore better management of water resources is possible.

Prediction of flood peak discharge and runoff volume is one of the major challenges in the management of watersheds. The present study was carried out to estimate event flood peak discharge and runoff volume using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in Kasilian watershed, Iran. For this purpose, 15 rainfall characteristics were considered for 60 storms from 1975 to 2009. Statistical indices of mean square error (RMSE), coefficient of efficiency (CE) and the coefficient of determination (R2) were used to assess models performance. The results showed that flood peak discharge variable, ANFIS with RMSE=1.28m3s-1, CE=%82 and R2=0.86 has better performance than ANN with RMSE=1.22m3s-1, CE=%82 and R2=0.95 and for runoff volume variable, ANFIS with RMSE=2369.54 m3, CE=%99 and R2=0.99 has better performance than ANN with RMSE=10282.82m3, CE=%98 and R2=0.98. Also, the results of the sensitivity analysis indicated that the most sensitive factor is excess rainfall for runoff flood peak discharge and runoff volume estimation.

Plains are one of the most important Geomorphological units, and different parameters for classification are presented in plain areas. One of The classification methods that are used in studies of natural resources in Iran, The plain division are in three types: bare plains, Apandazh and covered plain .The qualitative and quantitative factors are used to distinguish them from each other. In this study the geomorphometrical parameters used for plain classification by using artificial neural networks and sensitivity analysis. These parameters extracted by using mathematical equations and the corresponding relations on digital elevation models. Geomorphometric parameters that using in this study included Percent of slope, Plan Curvature, Profile Curvature, Minimum Curvature, the Maximum Curvature, Cross sectional Curvature, Longitudinal Curvature and Gaussian Curvature. These parameters calculated in area 125000 hectare and sample point are 1500; the result with ground truth map was compared and calibrated. Sampling method in this study is Latin Hyper cube that is kind of stratified random sampling. Result of this study shows the importance Geomorphometric parameters to classify desert plains include: Plan Curvature, Profile Curvature that is more sensitivity in plains types. As the topography is reduced, role and importance of these factors are reduced And highest parameters of the parameters is bare plain and lowest performance are covered plains.

Soil erosion by wind is a widespread problem in the arid lands of Iran. This research estimated and assessed the severity of wind erosion in Segzi desertification hotspot located in the eastern part of Isfahan township, focusing on criteria used in the IRIRF (Iranian Research Institute of Forest and Rangelands) model. Bayesian belief networks (BBNs) were also used to convert the IRIFR model to a predictive cause-and-effect model. The severity of wind erosion was calculated using the nine indicators of the IRIFR model on 17 geomorphological faces. The results indicated that three geomorphological faces located in the southern and central parts of the region had high potential for wind erosion. According to the results of the IRIFR model, 24%, 16%, 15% and 45% of the study area were classified as low, moderate, high and very high severity of wind erosion, respectively. Using the IRIFR indicators and conditional probability tables of these indicators, a BBN model was developed and its accuracy was assessed by sensitivity analysis and scenario analysis. Sensitivity analysis for both models showed that wind speed and condition, spatial extent and type of aeolian deposits and erosion features of the soil surface were the most important indicators responsible for wind erosion in the study area. The determination coefficient between the outputs of the IRIFR and BBN models (R2 > 0.80) indicated that the results of both models were significantly correlated (α = 5%). These results indicate that the application of the BBN model for wind erosion assessment can appropriately accommodate the uncertainty of the IRIFR model and help managers to make better decisions for upcoming land management projects.

Watershed outflow has influenced by different factors such as climatic, human and physical aspects and this Variability of effective factors can cause complex conditions, difficulty of flow forecasting and it mainly originates by different local and temporal scales of these factors. Also, some remote meteorological signals can cause changes in meteorological conditions in different regions. Hablehrud river flow has a vital role in regional development, especially for agricultural section. Thus research of river flow forecasting should be done for water resources management especially when there are drought and climate change conditions in order to facilitate sustainable development. In this study four nonlinear models of artificial neural networks including Generalized Feed Foreward Networks (JFNNs), Jordan/Elman Networks(JENs), Time Lag Recurrent Networks(TLRNs) and Radius Basis Function Networks(RBF) was used to modeling Hablehrud river flow(Bonkuh station) during 1982 to 2011. Input variables after sensitivity analysis were used in 4 models and 4 scenarios. Ten teleconnection indexes were used as input of the model to evaluate their roles in model capability. Results indicated that in the test stage Jordan/Elman Networks represented lower error compared with selected models (RMSE for 4 scenarios are5.57, 4.9, 5.35 and 4.62 respectively). In general error showed decreasing trend from first scenario to the last. Error was decreased of 15 to 31 percent by using teleconnection patterns as inputs (GFFN=%26, JEN=%15.8, TLRN=%25.5 and RBF=%31.7). Totally using teleconnection indexes as inputs in the modeling stage can diminish error of flow forecasting, although selected models indicated different results due to its variable topologies.

Although a small portion of the Earth's surface is covered by the mountains, but it has a large impact on watershed hydrological perspective Because of the water crisis in arid and semi-arid regions of Iran, monitoring of the amount of snow in these areas is very important. Usually, access to the spatial distribution of snow water equivalent is limited to small scale using sampled data. However, due to the limitations of the mountainous, snow sampling of area is difficult and sometimes impossible in the large basins. Thus, the development of methods in order to estimate snow water equivalent at the un-sampled locations is essential. In this research, an area of 16 ha area in Yazd province was selected and snow water equivalent was measured at 216 points using a Mt. Rose snow sampler. Then the application of artificial neural network method was evaluated using 31 geomorphometric parameters and the digital map of snow water equivalent was obtained. The results showed that the artificial neural network can estimate the snow water equivalent by a R2=0.83 and RMSE= 3.55.The results of the sensitivity analysis are also showed that among the ANN parameters used in the prediction of snow water equivalent, Plan Curvature, Profile Curvature, Curvature, Wind Effect, Slope, Multiresolution ridge top flatness index (MRRTF), Catchment slope and Multi resolution index of valley bottom flatness (MRIVBF) are the effective parameters to predict snow water equivalent, respectively.

The spatially distributed hydrologic model WetSpa is applied to the Dinvar river basin (1717 km2) located in upstream of the Karkheh Dam. Daily hydrometeorological data from 1382 to 1389, including precipitation data from 9 stations, temperature and evaporation data measured at 4 stations are used as input to the model. The spatial characteristic of the basin is described by three base maps, i.e. DEM, land use and soil type, in GIS form using 100 m cell size. Results of the simulations show a good agreement between calculated and measured hydrographs at the outlet of the basin. The model predicts the daily discharge values with a good accuracy, i.e. about 66% according to the Nash-Sutcliff criterion. Sensitivity and uncertainty analysis of the model parameters is performed using a model-independent parameter estimator, PEST. It is found that the correction factor for calculating the groundwater flow recession coefficient has the highest relative sensitivity and the parameters kss and kep located in two and three ranks of the relative sensitivity. Parameter uncertainty analysis shows that parameters ki; kep, kss and kg have high certainty and as well as parameters krain, kmax have low certainty.

WEPP model needs a great deal of input data. Identifying the model’s sensitive parameters and their prioritization increases the accuracy and efficiency of the model. On the other hand, WEPP model can simulate processes affecting on runoff, erosion and sediment throughout the year. Thus, model sensitivity must vary based on the storm occurrence time and parameters value in different sections of the year. To prove this assumption, two spring and autumn storm events related to 2008 were selected and sensitivity analysis of the WEPP model was done in three plots with different conditions in Sanganeh watershed. For sensitivity analysis, the OAT method was used and sensitivity degree of parameters was calculated. Obtained results show that the rate of sand is the most sensitive parameter of WEPP model. This parameter was followed by other parameters like clay percent, effective hydraulic conductivity, height and intensity of rainfall, day degree of growing, growing season and percent of growing season when leaf area index decreases. Most variations are observed in prioritization of sensitive parameter in the plant/ management file. In most cases, sensitivity degree of these parameters in autumn event comparing to the spring event has significantly reduced in all plots. In general, obtained results show that the rate of sensitivity of the WEPP model to different parameters varies during the time. Hence, for using this complex model in regions with data limitation, the user must be aware to this issue that regarding storm time, which parameter is more sensitive in the pilot area and need to be carefully measured in the field.

Studies of river engineering and River cross structures design such as bridges, diversion dams, Water Intake, etc. have closely related with the accurate calculation of depth, flow velocity and it has estimated moment of maximum flood discharge. The Estimated of flow velocity is important in hydraulic and hydrology studies .where no enough measured data is available, empirical approaches are usually applied to estimate flow velocity and maximum flood discharge. The most of these methods have been proposed for a certain area with specific physical and climatic conditions .to use these methods in other areas with different conditions ,evaluation and analysis of sensitivity of their parameters seems to be necessary. In this research ,new simple and efficient method is used to carry on sensitivity analysis of 2 flow velocity estimation methods including Manning and chezy that both of them are the most famous flow velocity existing. The results show chezy empirical equation is more sensitive Manning equation in estimating flow velocity. At the Manning equation, Model sensitivity is more to the less roughness values and when the energy line slope value (surface water) is high, flow velocity sensitive is increases. In other word, small changes in high slope, the flow velocity has changed dramatically. Sensitivity of Output’s model to hydraulic radius parameter is higher than while the area of catchment increases. Results show that roughness coefficient (c) in chezy equation is the most sensitive parameter and it need to more attention and correction.