Comparative comparison of data mining models in downscaling rainfall and temperature (Case Study: Bazoft-e- Samsami Watershed)
Background And ObjectivesTemperature and rainfall are two important meteorological variables, especially in arid and semi-arid areas. As a result, determining the value of these variables, their changes and prediction of these phenomena are necessary for more precise planning in the management of agricultural, economic and social sectors. Nowadays, incompatibility of temporal and spatial scales required in investigated models on the effect of climate change with GCM outputs and the need to assess the change trend in meteorological threshold variables at the regional scale has led to develop various downscaling methods. So, the aim of this study is the comparative comparison of data mining models in downscaling of rainfall and temperature based on data of NCEP general circulation model.
MethodsThe study area in this research is bazoft- e- Samsami watershed. This basin is one of the northern Karun sub-basins located in the northwest of Chaharmahal and Bakhtiari province. Marghak rain gauge and hydrometric stations are located at its outlet.
In this study, the performance and efficiency of four methods including decision tree (M5), Nearest Neighbor (KNN), Multilayer Perceptron (MLP) and Simple linear regression (SLR) were evaluated for modeling monthly rainfall and temperature of Marghak station during the training period of 1971-1990 and The 1991-2000 test period using NCEP output parameters.
ResultsMonthly rainfall modeling results using mentioned models showed that the output of all models except the KNN model provides negative values for rainfall. The rainfall prediction by M5 model in January, March, April and December is lower than the observed values (P). This situation is also somewhat seen in other models. Also, given that the minimum rainfall is zero, it can be concluded from the low predicted values rather than observed values that the maximum limit of rainfall with these models is not well predicted. The prediction of rainfall by all models in all months except May has a lower standard deviation than the observed values (P).
The predicted results of monthly temperature also showed that only MLP output provides negative values for the temperature, which can be due to the extrapolation and generalizationin in MLP method. Also, The standard deviation obtained from all models in January, February, March, April, July, August, October, November and December is more than standard deviation of observed temperature. The results of statistical analyzes also showed that M5 than the other models in the test stage according to RMSE, MBE and R2 have better estimates for rainfall and monthly temperature. Although the results of determination coefficient (R2) in the test stage for monthly temperature estimation are weaker than monthly rainfall.
ConclusionThe results of the efficiency of four models of KNN, M5, SLR and MLP in monthly rainfall and temperature modeling in Marghak meteorological station with NCEP output data showed that these models were weak in downscaling the monthly rainfall and temperature. Therefore, despite the relative superiority of M5 model compared to other models, the use of these data mining models is not recommended to predict rainfall and temperature variables in Margak station.
Water and Soil Conservation, Volume:24 Issue: 5, 2018
227 - 240
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