جستجوی مقالات مرتبط با کلیدواژه "الگوریتم cart" در نشریات گروه "جغرافیا"

Understanding and determining the amount of carried sediment by river have been considered by experts because of the importance and the role of sediment transfer in water-shed basins subject , among them is rivers organizing that carry out for erosion and sedimentation control or river-bed stabilization and flood eject. The current study aims at determining the efficiency of decision tree model in estimating suspended sediment of Meimeh river. The data used in the study includes sediment ,water , rain fall , and daily discharges related to the statistic periods from 1967-68 to 2009-2010. After data processing 554 records with accessible statistic discharges and their corresponding sediment were chosen and the obtained results were compared using sediment rating curve. To this aim, statistical criteria of R2 , RMSE, MAT , R and Bias were applied. To investigate, the rain fall effects and daily discharges on precise measuring of sediment by tree model, the data related to the rain fall and daily discharges were added to the model. The results showed that the decision tree model has presented reasonable results for the simulation of the suspended load in the present study station. So that based on criteria RMSE, MAE, r2 and Bias decision tree with less error than the sediment rating curve deposits is estimated. The results did not change much and sediment discharge rate is most correlated with the corresponding discharge of sediment.

There are several ways to study drought. Method of analysis rainfall data, Public Sector analysis methods is drought. Therefore, accurate prediction and before the outbreak precipitation could provide the conditions for assessing the drought situation. The purpose of this study is investigating the effect of data preprocessing on the performance of the decision tree model to predict drought in synoptic station in Sanandaj. In this study, CART algorithms (Classification and regression tree) has been used as variety of decision tree regression in order to predict precipitation forecast of12months. The data used in this study are the monthly precipitation, relative humidity, the maximum temperature, the average temperature, wind direction and wind speed in a specific statistical period(1970 - 2010). To assess the created trees in this study, different statistical measures have been used which in the end results show that in synoptic station in Sanandaj, decision tree regression model is a relatively efficient model to predict drought in which using a moving averages compared to other states led to Increasing the efficiency of decision tree mode land providing thread just mint in the range of changes, the input data with a high reliability is able to estimate the amount ofprecipitation12months before it occurs which in the simulation carried outing this study, when the five-year moving average of the data has been used to implement the model ,combination of previous rainfall, maximum temperature has been identified as the most appropriate states. The findings shows that applying moving average to the original data, dramatically improves the performance of the model. In these circumstances, the decision tree method regression in Sanandaj station with high reliability level estimate the occurrence of precipitation in 12 months ago.

Rainfall is one of the main components of the water balance and its prediction is useful in managing agricultural water supply and managing water resources in reservoirs. Decision tree model as being one of the prediction models, has several functions in rainfall modelling and results in law making. In this study, to evaluate the performance of the decision tree model for the precipitation to be predicted in Kermanshah synoptic stations, and the algorithm CART (Classification and regression tree), being a kind of the regression decision trees, was used to predict the rainfall for the next 30 months. The data used in this study collected from the monthly rainfall stats, evaporation, relative humidity, maximum temperature, average temperature and wind speed in the statistical period of 1970 to 2010. To assess the created trees in this study, different statistical criteria were used. Finally, the results show that in Kermanshah synoptic stations, the regression decision tree is a relatively efficient model in predicting rainfall in which the use of moving average leads to a significant increase in the performance of the model than other modes. And in the case of modification in the range of changes in the input data, it is able to precisely estimate the rainfall 30 months prior to its occurrence, which in the simulations done, whenever the average five-year movement is used to reinforce the data, the combination of the previous rain and the maximum temperature is identified as the most proper status.