As more and more farmland is converted to forestry, the need for effective decision support regarding the use of land in the fragile ecological environment of the Loess Plateau hilly-gully area. The Luoyugou watershed...As more and more farmland is converted to forestry, the need for effective decision support regarding the use of land in the fragile ecological environment of the Loess Plateau hilly-gully area. The Luoyugou watershed was chosen as the study area to calculate the single dynamic degree, integrated dynamic degree, and change indexes of land use, as well as the land-use type transition matrix. This was done by interpreting the TM and SPOT images of the Luoyugou watershed in 1986, 1995, and2004 and making statistical analysis. The results of ou statistical analysis show that the conversion of slope farm land to terrace and forest land plays a dominant role in land-use changes in the Luoyugou watershed from 1986 to2004. The land-use changes are mainly driven by popula tion growth, socio-economic development, consume spending, and investment in forest ecology.展开更多
Due to the increase in the types of business and equipment in telecommunications companies,the performance index data collected in the operation and maintenance process varies greatly.The diversity of index data makes...Due to the increase in the types of business and equipment in telecommunications companies,the performance index data collected in the operation and maintenance process varies greatly.The diversity of index data makes it very difficult to perform high-precision capacity prediction.In order to improve the forecasting efficiency of related indexes,this paper designs a classification method of capacity index data,which divides the capacity index data into trend type,periodic type and irregular type.Then for the prediction of trend data,it proposes a capacity index prediction model based on Recurrent Neural Network(RNN),denoted as RNN-LSTM-LSTM.This model includes a basic RNN,two Long Short-Term Memory(LSTM)networks and two Fully Connected layers.The experimental results show that,compared with the traditional Holt-Winters,Autoregressive Integrated Moving Average(ARIMA)and Back Propagation(BP)neural network prediction model,the mean square error(MSE)of the proposed RNN-LSTM-LSTM model are reduced by 11.82%and 20.34%on the order storage and data migration,which has greatly improved the efficiency of trend-type capacity index prediction.展开更多
基金supported by the National Basic Research Program of China (2007CB407207)National Natural Science Foundation of China (30800888)
文摘As more and more farmland is converted to forestry, the need for effective decision support regarding the use of land in the fragile ecological environment of the Loess Plateau hilly-gully area. The Luoyugou watershed was chosen as the study area to calculate the single dynamic degree, integrated dynamic degree, and change indexes of land use, as well as the land-use type transition matrix. This was done by interpreting the TM and SPOT images of the Luoyugou watershed in 1986, 1995, and2004 and making statistical analysis. The results of ou statistical analysis show that the conversion of slope farm land to terrace and forest land plays a dominant role in land-use changes in the Luoyugou watershed from 1986 to2004. The land-use changes are mainly driven by popula tion growth, socio-economic development, consume spending, and investment in forest ecology.
基金supported by Research on Big Data Technology for New Generation Internet Operators(H04W180609)the second batch of Sichuan Science and Technology Service Industry Development Fund Projects in 2018(18KJFWSF0388).
文摘Due to the increase in the types of business and equipment in telecommunications companies,the performance index data collected in the operation and maintenance process varies greatly.The diversity of index data makes it very difficult to perform high-precision capacity prediction.In order to improve the forecasting efficiency of related indexes,this paper designs a classification method of capacity index data,which divides the capacity index data into trend type,periodic type and irregular type.Then for the prediction of trend data,it proposes a capacity index prediction model based on Recurrent Neural Network(RNN),denoted as RNN-LSTM-LSTM.This model includes a basic RNN,two Long Short-Term Memory(LSTM)networks and two Fully Connected layers.The experimental results show that,compared with the traditional Holt-Winters,Autoregressive Integrated Moving Average(ARIMA)and Back Propagation(BP)neural network prediction model,the mean square error(MSE)of the proposed RNN-LSTM-LSTM model are reduced by 11.82%and 20.34%on the order storage and data migration,which has greatly improved the efficiency of trend-type capacity index prediction.
