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Soil erosion and socio-economic interacts with each other. Soil and water loss destroys land resources, causes non-point source pollution, affects the recycling and utilization of water resources, worsens the water environment, and even causes natural disasters such as collapse, landslides, and debris flows. Soil erosion affects regional socio-economic development. On the one hand, economic and social development has caused a sharp increase in people’s demand for the development and utilization of natural resources, which has increased the occurrence of soil and water loss; on the other hand, economic and social development has promoted people’s understanding of soil erosion and soil and water conservation, enabling people to consciously change their production and lifestyle, and having sufficient funds to invest in soil and water conservation and reduce soil erosion.

Short-term load forecasting is an important part of load forecasting, which is of great significance to the optimal power flow and power supply guarantee of the power system. In this paper, we proposed the load series reconstruction method combined improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) with sample entropy (SE). The load series is decomposed by ICEEMDAN and is reconstructed into a trend component, periodic component, and random component by comparing with the sample entropy of the original series. Extreme learning machine optimized by salp swarm algorithm (SSA-ELM) is used to predict respectively, and the final prediction value is obtained by superposition of the prediction results of the three components. Then, the prediction error of the training set is divided into four load intervals according to the predicted value, and the kernel probability density is estimated to obtain the error distribution of the training set. Combining the predicted value of the prediction set with the error distribution of the corresponding load interval, the prediction load interval can be obtained. The prediction method is verified by taking the hourly load data of a region in Denmark in 2019 as an example. The final experimental results show that the proposed method has a high prediction accuracy for short-term load forecasting. [ABSTRACT FROM AUTHOR]

Accurate base station traffic data in a public place with large changes in the amount of people could help predict the occurrence of network congestion, which would allow us to effectively allocate network resources. This is of great significance for festival network support, routine maintenance, and resource scheduling. However, there are a few related reports on base station traffic prediction, especially base station traffic prediction in public scenes with fluctuations in people flow. This study proposes a public scene traffic data prediction method, which is based on a v Support Vector Regression ( v SVR) algorithm. To achieve optimal prediction of traffic, a symbiotic organisms search (SOS) was adopted to optimize the v SVR parameters. Meanwhile, the optimal input time step was determined through a large number of experiments. Experimental data was obtained at the base station of Huainan Wanda Plaza, in the Anhui province of China, for three months, with the granularity being one hour. To verify the predictive performance of v SVR, the classic regression algorithm extreme learning machine (ELM) and variational Bayesian Linear Regression (vBLR) were used. Their optimal prediction results were compared with v SVR predictions. Experimental results show that the prediction results from SOS- v SVR were the best. Outcomes of this study could provide guidance for preventing network congestion and improving the user experience., Competing Interests: The authors declare no conflict of interest