The development of ecological restoration projects is unsatisfactory, and soil erosion is still a problem in ecologically restored areas. Traditional soil erosion studies are mostly based on satellite remote sensing data and traditional soil erosion models, which cannot accurately characterize the soil erosion conditions in ecological restoration areas (mainly plantation forests). This paper uses high-resolution unmanned aerial vehicle (UAV) images as the base data, which could improve the accuracy of the study. Considering that traditional soil erosion models cannot accurately express the complex relationships between erosion factors, this paper applies convolutional neural network (CNN) models to identify the soil erosion intensity in ecological restoration areas, which can solve the problem of nonlinear mapping of soil erosion. In this study area, compared with the traditional method, the accuracy of soil erosion identification by applying the CNN model improved by 25.57%, which is better than baseline methods. In addition, based on research results, this paper analyses the relationship between land use type, vegetation cover, and slope and soil erosion. This study makes five recommendations for the prevention and control of soil erosion in the ecological restoration area, which provides a scientific basis and decision reference for subsequent ecological restoration decisions.

Prediction of groundwater quality is an essential step for sustainable utilization of water resources. Most of the related research in the study area focuses on water distribution and rational utilization of resources but lacks results on groundwater quality prediction. Therefore, this paper introduces a prediction model of groundwater quality based on a long short-term memory (LSTM) neural network. Based on groundwater monitoring data from October 2000 to October 2014, five indicators were screened as research objects: TDS, fluoride, nitrate, phosphate, and metasilicate. Considering the seasonality of water quality time series data, the LSTM neural network model was used to predict the groundwater index concentrations in the dry and rainy periods. The results suggest the model has high accuracy and can be used to predict groundwater quality. The mean absolute errors (MAEs) of these parameters are, respectively, 0.21, 0.20, 0.17, 0.17, and 0.20. The root mean square errors (RMSEs) are 0.31, 0.29, 0.28, 0.27, and 0.31, respectively. People can be given early warnings and take measures according to the forecast situation. It provides a reference for groundwater management and sustainable utilization in the study area in the future and also provides a new idea for coastal cities with similar hydrogeological conditions.

Soil erosion poses significant ecological and economic challenges, necessitating precise and effective identification methods. Traditional models frequently overlook the intricate relationships between erosion factors and multispectral remote sensing data. To enhance these traditional methods, a novel dual-input gated fusion Convolutional Neural Network (CNN) has been developed, integrating channel and spatial attention mechanisms. This innovative model strengthens the connection between multispectral images and erosion factors, improving the accuracy and generalizability of erosion predictions. The model utilizes data collected from unmanned aerial vehicles (UAVs) equipped with high-precision multispectral sensors. By processing both spectral images and erosion factor data, the model effectively captures complex soil spatial distributions. The dual-input gated fusion mechanism allows the network to extract high-level semantics while suppressing redundant information, ensuring robust performance even in heterogeneous terrains. Experimental results indicate that this framework significantly enhances the performance of traditional models, providing superior predictions for small and medium-sized areas. Experimental results indicate that the presented framework can achieve better accuracy (96.92%) compared with other machine learning approaches, such as Random Forest (89.64%), VGGNET (91.52%), and RESNET (90.18%). Moreover, the proposed method can improve accuracy by 26.59% compared to the traditional RUSLE model. This improvement is critical for applications in ecological restoration and sustainable development. The integration of deep learning techniques with UAV-based data collection offers a powerful tool for environmental monitoring and management.

Petroleum hydrocarbon pollution causes significant damage to soil, so accurate prediction and early intervention are crucial for sustainable soil management. However, traditional soil analysis methods often rely on statistical methods, which means they always rely on specific assumptions and are sensitive to outliers. Existing machine learning based methods convert features containing spatial information into one-dimensional vectors, resulting in the loss of some spatial features of the data. This study explores the application of Three-Dimensional Convolutional Neural Networks (3DCNN) in spatial interpolation to evaluate soil pollution. By introducing Channel Attention Mechanism (CAM), the model assigns different weights to auxiliary variables, improving the prediction accuracy of soil hydrocarbon content. We collected soil pollution data and validated the spatial distribution map generated using this method based on the drilling dataset. The results indicate that compared with traditional Kriging3D methods (R² = 0.318) and other machine learning methods such as support vector regression (R² = 0.582), the proposed 3DCNN based method can achieve better accuracy (R² = 0.954). This approach provides a sustainable tool for soil pollution management, supports decision-makers in developing effective remediation strategies, and promotes the sustainable development of spatial interpolation techniques in environmental science.

This paper presents a deep learning model based on an active learning strategy. The model achieves accurate identification of vegetation types in the study area by utilizing multispectral data obtained from preprocessing of unmanned aerial vehicle (UAV) remote sensing equipment. This approach offers advantages such as high data accuracy, mobility, and easy data collection. In active learning, the minimum confidence scoring method and a sampling technique based on a data pool are employed to reduce labeling costs. The deep learning model incorporates a semantic segmentation gated full fusion module that integrates a dual attention mechanism. This module enhances the capture of detailed texture information, optimally allocates spectral weights, and improves the model’s ability to distinguish between similar categories. At a labeling cost of 20%, the average accuracy of the model is 93.2%. Compared with other models, the proposed model achieved the highest classification accuracy in the case of limited training samples. At full annotation cost, the average accuracy is 95.32%, with only a difference of about 2%, but saving 80% of annotation cost. Therefore, active learning strategies can filter out high-value samples that are beneficial for model training, greatly reducing the annotation cost of samples Finally, the recognition results of surface vegetation cover types in the study area are presented, and the model’s accuracy is verified through field investigation.

When confronted with different influent conditions, WWTPs often lack targeted and effective operational control strategies. For the three typical scenarios of low C/N, low water temperature and high water temperature, 441 carbon source dosage and DO concentration coordination control strategies were designed under the premise of ensuring the effluent water quality meets the standard. The purpose was to provide clear operational guidance for the efficient operation of WWTPs in different scenarios. To determine the optimal strategy, the effluent quality prediction model based on LSTM and GRU was constructed for testing. The results showed that: (1) the LSTM-GRU model is better than SVR and RF in predicting effluent COD and TN; (2) In the low C/N scenario, the carbon source dosage should be controlled between 0.23 t/h and 0.26 t/h, with the DO concentration ranging from 2.0 mg/L to 2.6 mg/L; (3) In the low water temperature scenario, the carbon source dosage should be controlled between 0.25 t/h and 0.27 t/h, with the DO concentration ranging from 2.6 mg/L to 2.8 mg/L; (4) In the high water temperature scenario, the carbon source dosage should be controlled between 0.20 t/h and 0.27 t/h, with the DO concentration ranging from 2.0 mg/L to 2.5 mg/L.