Search

Soil salinization is a critical and global environmental problem. Effectively mapping and monitoring the spatial distribution of soil salinity is essential. The main aim of this work was to map soil salinity in Shandong Province located on the Yellow River Delta of China using Sentinel-1/2 remote sensing data and digital elevation model (DEM) data, coupled with soil sampling data, and combined with four regression models: support vector regression (SVR), stepwise multi-regression (SMR), partial least squares regression (PLSR) and random forest regression (RFR). For these purposes, 60 soil samples were collected during the field survey conducted from 9 to 14 October 2019, corresponding to the Sentinel-1/2 and DEM data. Then we established a soil salinity and feature dataset based on the sampled data and the features extracted from Sentinel-1/2 and DEM data. This study adopted the feature importance of the RF model to screen all features. The results showed that the CRSI index made the greatest contribution in retrieving soil salinity in this region. In this paper, 18 sampling points were used to validate and compare the performance of the four models. The results reveal that, compared with the other regression models, the PLSR model has the best performance (R2 = 0.66, and RMSE = 1.30). Finally, the PLSR method was used to predict the spatial distribution of soil salinity in the Yellow River Delta. We concluded that the model can be used effectively for the quantitative estimation of soil salinity and provides a useful tool for ecological construction. [ABSTRACT FROM AUTHOR]

Short-term load forecasting plays a significant role in the operation of power systems. Recently, deep learning has been generally employed in short-term load forecasting, primarily in the extraction of the characteristics of digital information in a single dimension without taking into account of the impact of external variables, particularly non-digital elements on load characteristics. In this paper, we propose a joint MFF-SAM-GCN to realize short-term load forecasting. First, we utilize a Bi-directional Long Short-Term Memory (Bi-LSTM) network and One-Dimensional Convolutional Neural Network (1D-CNN) in parallel connection to form a multi-feature fusion (MFF) framework, which can extract spatiotemporal correlation features of the load data. In addition, we introduce a Self-Attention Mechanism (SAM) to further enhance the feature extraction capability of the 1D-CNN network. Then with the deployment of a Graph Convolutional Network (GCN), the external non-digital features such as weather, strength, and direction of wind, etc., are extracted. Moreover, the generated weight matrices are incorporated into the load features to enhance feature recognition ability. Finally, we exploit Bayesian Optimization (BO) to find the optimal hyperparameters of the model to further improve the prediction accuracy. The simulation is taken from our proposed model and six benchmark schemes by using the bus load dataset of the Shandong Open Data Network, China. The results show that the RMSE of our proposed MFF-SAM-GCN model is 0.0284, while the SMAPE is 9.453%,the MBE is 0.025, and R-squared is 0.989, which is better than the selected three traditional machine learning methods and the three deep learning models. [ABSTRACT FROM AUTHOR]