Prediction Of Grassland Biomass Based On Time Series Model And SVM

It is one of the most important issues for scientific use of grassland resources to study the relationship between grassland biomass and environmental factors,and to predict grassland biomass accurately.Accurate prediction of grassland biomass can control the growth of grassland in growing season.Forecast the growth of grassland systematically,which is of great significance for grassland resource protection and planning utilization,grazing intensity and stocking capacity.At present,there are few studies on biomass prediction,mainly using the method of integral regression,through the historical data to fit the trend line,and in accordance with the trend of a future time to predict the status of grassland resources,this method is difficult to predict grassland biomass fully and accurately.In order to forecast the distribution of future biomass,to provide dynamic grassland growth information and grassland biomass data,to realize the healthy development of grassland ecosystem and the scientific management of animal husbandry,in this paper,firstly,we compared different time series models.Then,we used support vector machine(SVM)model developed the optimal aboveground biomass model for Qinghai Lake Basin and generate the biomass data with 500 meter resolution and 8 day interval in 2015.Eventually,we analyzed the grassland trend in Qinghai Lake basin.The main content and results of this paper are as follows:(1)We were using the synthetic NDVI series(8 days,500 meters,2000-2014)as the input data,and using the time series model to generate the prediction NDVI(8 days,30 meters,2015).Select multiple time series model: including trend moving average method,second exponential smoothing method(ES),self-adaptive filtering method,modified exponential curve method and auto regressive integrated moving average(ARIMA)were used to predict NDVI in 2015.Based on the above models,it was found that the ARIMA model had higher precision and can reflect the distribution of NDVI in Qinghai Lake basin in 2015.(2)Based on the forecast NDVI dataset,the regression model was established and the accuracy analysis was carried out.The NDVI data sets were obtained by obtaining the optimal NDVI correction scheme.Based on the forecasted NDVI dataset and the climate data,we established a regression optimization model and compared.Different correction schemes were selected to obtain the NDVI dataset.There were three schemes :1)we use ARIMA model to generate prediction NDVI;2)We use ARIMA model to generate prediction NDVI,and then optimized by regression model to generate new prediction NDVI 3)We use ARIMA model to generate prediction NDVI,precipitation and temperature data,then optimized by regression model to generate new prediction NDVI.The results show that the accuracy of scheme III(R2 = 0.8912,RMSE = 0.0263)is higher than that of scheme II(R2 = 0.8867,RMSE = 0.0267)and Scheme 1(R2 = 0.8879,RMSE = 0.0271).(3)Based on the optimized NDVI surface data,we generated the forecast biomass data set of Qinghai Lake Basin(2015,8 day,500m)by using SVM.The distribution of biomass is mainly concentrated in the northern part of Qinghai Lake,the southern part and the alpine meadow area of the basin.The forecasting biomass in the study area in 2015 is mainly distributed in 120 ~ 180 g / m~2.