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Accurate prediction of sea level height is critically important for the government in assessing sea level risk in coastal areas. However, due to the nonlinear, time-varying and highly uncertain characteristics of sea level change data, sea level prediction is challenging. To improve the accuracy of sea level prediction, this paper uses a new swarm intelligence algorithm named the sparrow search algorithm (SSA), which can imitate the foraging behavior and antipredation behavior of sparrows, to determine optimal solutions. To avoid the algorithm falling into a local optimal situation, this paper integrates the sine–cosine algorithm and the Cauchy variation strategy into the SSA to obtain an algorithm named the SCSSA. The SCSSA is used to optimize the parameter values of the CNN-BiLSTM (convolutional neural network combined with bidirectional long short-term memory neural network) model; finally, a combined neural network model (named SCSSA-CNN-BiLSTM) is proposed. In this paper, the time series data of seven tidal stations located in coastal China are used for experimental analysis. First, the SCSSA-CNN-BiLSTM model is compared with the CNN-BiLSTM model to predict the time series data of SHANWEI Station. With respect to the training and test sets of data, the SCSSA-CNN-BiLSTM model outperforms the other models on all the evaluation metrics. In addition, the remaining six tide station datasets and five neural network models, including the SCSSA-CNN-BiLSTM model, are used to further study the performance of the proposed prediction model. Four evaluation indices including the root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R2) are adopted. For six stations, the RMSE, MAE, MAPE and R2 of SCSSA-CNN-BiLSTM model are ranged from 20.9217 ~ 27.8427 mm, 9.4770 ~ 17.8603 mm, 0.1322% ~ 0.2482% and 0.9119 ~ 0.9759, respectively. The experimental analysis results show that the SCSSA-CNN-BiLSTM model makes effective predictions at all stations, and the prediction performance is better than that of the other models. Even though the combination of SCSSA algorithm may increase the complexity of the model, indeed the proposed model is a new prediction method with good accuracy and robustness for predicting sea level change. [ABSTRACT FROM AUTHOR]

Thunderstorm gusts are a common form of severe convective weather in the warm season in North China, and it is of great importance to correctly forecast them. At present, the forecasting of thunderstorm gusts is mainly based on traditional subjective methods, which fails to achieve high-resolution and high-frequency gridded forecasts based on multiple observation sources. In this paper, we propose a deep learning method called Thunderstorm Gusts TransU-net (TG-TransUnet) to forecast thunderstorm gusts in North China based on multi-source gridded product data from the Institute of Urban Meteorology (IUM) with a lead time of 1 to 6 h. To determine the specific range of thunderstorm gusts, we combine three meteorological variables: radar reflectivity factor, lightning location, and 1-h maximum instantaneous wind speed from automatic weather stations (AWSs), and obtain a reasonable ground truth of thunderstorm gusts. Then, we transform the forecasting problem into an image-to-image problem in deep learning under the TG-TransUnet architecture, which is based on convolutional neural networks and a transformer. The analysis and forecast data of the enriched multi-source gridded comprehensive forecasting system for the period 2021–23 are then used as training, validation, and testing datasets. Finally, the performance of TG-TransUnet is compared with other methods. The results show that TG-TransUnet has the best prediction results at 1–6 h. The IUM is currently using this model to support the forecasting of thunderstorm gusts in North China. [ABSTRACT FROM AUTHOR]

It is of great significance to accurately grasp ground-level ozone concentration on a wide scale in order to cope with the increasingly serious ground-level ozone pollution problem. Currently, satellite technology can be used to monitor ground-level ozone concentration on a wide scale, but there is the problem of insufficient spatial resolution. The use of ground monitoring stations to detect ground-level ozone concentration can obtain more accurate ozone concentration around the station, but there are problems of limited number of stations and sparse distribution. Therefore, it is still a challenge to accurately grasp the ground-level ozone concentration at a large scale and high spatial resolution. To address this issue, this paper proposes a ground-level ozone concentration inversion algorithm based on deep learning methods, incorporating data from remote sensing satellites, ground monitoring stations, meteorological conditions, geography, and land use. Taking Yunnan Province in China as the experimental region and utilizing five-fold cross-validation, the model's Mean Square Error (MSE) reached 340.45 and Root Mean Square Error (RMSE) reached 18.45. Compared to machine learning models, the error decreased by 14.53%, outperforming Random Forest models, Convolution Neural Networks, and Vision Transformers. This study offers a referential approach for accurately capturing ground-level ozone concentrations at a large scale with high spatial resolution. [ABSTRACT FROM AUTHOR]

The accurate estimation of forest aboveground biomass is of great significance for forest management and carbon balance monitoring. Remote sensing instruments have been widely applied in forest parameters inversion with wide coverage and high spatiotemporal resolution. In this paper, the capability of different remote-sensed imagery was investigated, including multispectral images (GaoFen-6, Sentinel-2 and Landsat-8) and various SAR (Synthetic Aperture Radar) data (GaoFen-3, Sentinel-1, ALOS-2), in aboveground forest biomass estimation. In particular, based on the forest inventory data of Hangzhou in China, the Random Forest (RF), Convolutional Neural Network (CNN) and Convolutional Neural Networks Long Short-Term Memory Networks (CNN-LSTM) algorithms were deployed to construct the forest biomass estimation models, respectively. The estimate accuracies were evaluated under the different configurations of images and methods. The results show that for the SAR data, ALOS-2 has a higher biomass estimation accuracy than the GaoFen-3 and Sentinel-1. Moreover, the GaoFen-6 data is slightly worse than Sentinel-2 and Landsat-8 optical data in biomass estimation. In contrast with the single source, integrating multisource data can effectively enhance accuracy, with improvements ranging from 5% to 10%. The CNN-LSTM generally performs better than CNN and RF, regardless of the data used. The combination of CNN-LSTM and multisource data provided the best results in this case and can achieve the maximum R2 value of up to 0.74. It was found that the majority of the biomass values in the study area in 2018 ranged from 60 to 90 Mg/ha, with an average value of 64.20 Mg/ha. [ABSTRACT FROM AUTHOR]

The growth of global environmental concerns has prompted a worldwide movement towards sustainable development. In recent years, China's rapid economic growth has been reflected in increased environmental degradation, exacerbated by rapid urbanization, the urban–rural divide, and a government commitment to urban ecological preservation at the expense of rural areas. At the same time, rural communities require increased cultural awareness of environmental conservation and sustainable practices. To address the indiscriminate depletion of natural resources endangering China's rural landscapes, this paper presents a novel framework that combines deep convolutional neural networks (CNNs) with rural ecological governance. A thorough understanding of rural ecological dynamics is made possible by the integration of cutting-edge CNN technology, which facilitates complex data processing and image recognition from satellite and sensor data. Utilizing the Eurostat dataset, it carries out an extensive sustainability analysis of rural ecological governance by applying fundamental theories of environmental governance, such as polycentricity and symbiosis, that are adapted for rural environments. The pastoral environmental governance issues are systematically addressed by means of a multi-stage PPS hierarchical approach. The framework also suggests a novel approach to household waste management for isolated communities, focusing on natural sewage treatment and waste recycling as ways to reduce environmental impact. This all-encompassing strategy seeks to transform rural ecological management by providing customized solutions to protect resources and promote sustainable development in China's rural areas. The experimental results show a fantastic accuracy rate of 98.5% of the CNN model. This compares favorably to recognized architectures such as ResNet16, ResNet32, and ResNet64, demonstrating CNN's ability to handle complex features of rural landscapes. A comparative analysis with RNN and LSTM highlights CNN's capacity to extract hierarchical features and capture spatial dependencies. In addition, the work reveals that rural residents have an overall satisfaction rate of 85.2% with environmental governance, of which 45.6% provide high levels of satisfaction. [ABSTRACT FROM AUTHOR]

This study proposes a novel method, which is a U‐shaped convolutional neural network that combines non‐local attention mechanisms, Res2net residual modules, and terrain information (UNR‐Net). The original U‐Net method and the linear regression (LR) method are conducted as benchmarks. Generally, the UNR‐Net has demonstrated promise in performing a 10× downscaling for daily 2‐m temperature over North China with lead times of 1–7 days and shows superiority to the U‐Net and LR methods. To be specific, U‐Net and UNR‐Net demonstrate higher Nash‐Sutcliffe Efficiency coefficient values compared to LR by 0.052 and 0.077, respectively. The corresponding improvements in pattern correlation coefficient are 0.013 and 0.016, while the root mean square error values are higher by 0.22 and 0.338, respectively. Additionally, the structural similarity index metric is higher by 0.033 and lower by 0.015. Furthermore, regions with significant errors are primarily distributed in complex terrain areas such as the Taihang Mountains, where UNR‐Net exhibits noticeable improvements. In addition, the 12 components‐based error decomposition method is proposed to analyze the error source of different models. Generally, the smallest errors are observed during the summer season and the sequence error component is proven to be the main source error of 2‐m temperature forecasts. Furthermore, UNR‐Net consistently demonstrates the lowest errors among all 12 error components. Therefore, combining the numerical weather prediction model and deep learning method is very promising in downscaling temperature forecasts and can be applied to routine forecasting of other atmospheric variables in the future. Plain Language Summary: This research proposes a new method for downscaling using deep learning. The method uses a specific type of neural network called UNR‐Net, which combines attention mechanisms, residual modules, and terrain information. The performance of UNR‐Net is compared to two other methods: U‐Net and LR. In the study, UNR‐Net shows promise in performing a 10× downscaling of the daily 2‐m temperature in North China. The UNR‐Net demonstrates the best overall performance among all the comprehensive indicators (NSE, pattern correlation coefficient, root mean square error, and structural similarity index metric). Errors in the predictions are mainly found in complex terrain areas like the Taihang Mountains, but UNR‐Net shows noticeable improvements in these regions. The study also proposes a 12 components‐based error decomposition method to analyze the error sources of different models. All in all, it is found that the smallest errors are observed during the summer season and the main source error is the sequence error component. Additionally, when considering lead times of 1–7 days, UNR‐Net consistently shows the lowest errors among all 12 error components. Based on these findings, combining numerical weather prediction models with deep learning methods holds great promise for generating high‐resolution temperature forecasts. Key Points: This paper presents a novel deep learning downscaling method, UNR‐Net, capable of downscaling daily 2‐m temperature by a factor of 10The overall performance of the UNR‐Net method surpasses the U‐Net method and linear regression methodThe 12 components‐based error decomposition method is proposed to analyze the error source of different models [ABSTRACT FROM AUTHOR]

Due to the periodic changes of climate, cracks are widely developed in expansive soils, leading to the destruction of soil integrity, the deterioration of physical strength, and eventually the instability of the expansive soil slope and other disasters. In this paper, a deep learning-based modeling method was proposed for soil crack networks characterization through steps of segmentation, quantification and simulation. Inspired by the U-Net convolutional neural network, a dilated convolution module was added to the backbone to enhance the crack segmentation capability and a subpixel edge detection algorithm was followed for accurate crack edge detection. Then, a deterministic or stochastic method was designed for crack network simulation. A case study of an expansive soil slope on the bank of Wadong Main Canal in the Pi-Shi-Hang Irrigation District, China, was conducted. Results show that the dilated U-Net model gained 0.24 and 0.25 improvement in F1-score and IoU (Intersection over Union) comparing to the conventional segmentation method (Otsu) and crack edge precision was further improved by 5.38%. The performances of proposed method including the image labeling, effect of crack thickness and environmental conditions, etc., are also explored. To validate the simulated crack network, crack areas at different depths were measured through X-ray images. Comparing with the simulation result, mean error rate of 1.05% was achieved. Thus, the proposed method can efficiently characterize soil crack networks using the field acquired crack images. [ABSTRACT FROM AUTHOR]

Groundwater level (GWL) prediction is important for ecological protection and resource utilization; it helps in formulating policies for artificial groundwater recharge, modifying the number of extraction wells, etc., and can support sustainable human development as well as inform water resource management decisions. However, climate change, anthropogenic impacts, and the complex coupling between surface water and groundwater increase the difficulty of predicting groundwater levels. The model proposed in this paper combines external data as well as multiple models. The method leverages long and short-term memory (LSTM) and convolutional neural network (CNN) models, combined with secondary modal decomposition and slime mould algorithm (SMA), together with an adaptive weight module (AWM). The study applies this method to predict GWL for three different hydrological conditions in China, specifically for the Jinan Baotu Spring, Heihu Spring, and Zhongtianshe watershed of Taihu Lake. A comparison of metrics such as mean absolute error and Nash efficiency coefficient for single and hybrid models shows that the model in this paper is more advantageous than the single model and other hybrid models. The interpretability of the model is enhanced by SHAP values that demonstrate the degree of contribution of the input variables. This paper uses SHAP analyses to identify the key drivers affecting groundwater levels. These factors must be detected in order to develop groundwater resource protection measures. [Display omitted] • Multivariate fusion data including hydrology and meteorology are used as model input. • A secondary modal decomposition module for historical groundwater level data was utilized. • The neural network hyperparameters are optimized using the slime mould algorithm. • Aggregate subsets of prediction with adaptive modules rather than linear summation. • The interpretable SHAP model measures the degree of influence of external variables. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new deformable convolutional network with attention mechanism to deal with the Chinese character of various dynasties. These ancient Chinese characters are hieroglyph with special spatial structure and gradually evolving into modern Chinese characters. However, because of the intrinsic limitations of traditional convolutional networks in the model, the geometric structure of the convolutional module is fixed. Therefore, we build a new architecture called attention-based deformable networks for this task. Besides, we construct a new dataset called the Chinese characters from Various Dynasties Dataset (CCDD), which includes the evolution of Chinese characters between major dynasties. Deformable convolution based on attention mechanism can obtain those more important offsets, resulting in better performance than the original deformable convolution. In the experimental stage, extensive experiments validate the performance of our approach. Firstly, the effectiveness of this network was verified on two text datasets, Mnist(99.38%) and notMnist(98.22%) both comparing with several modules. Finally, the best performance was achieved on the CCDD dataset(91.50%) compared to other classical deep learning networks. This is the first paper to apply deep learning methods to recognize and classify the characters of major dynasties in China. And archaeological workers need to be able to recognize Chinese characters from various periods in China, and our algorithm can assist them in completing this task to a certain extent. It can also help cultural relic enthusiasts roughly determine the age of cultural relics. [ABSTRACT FROM AUTHOR]

Estimating the total wind power output from the meteorological information at a province level (called Provincial Regional Wind Power Conversion Model, PRWPCM) plays vital and fundamental roles in energy modeling community and regional wind power forecasting. How to construct a reliable PRWPCM is a real challenge, since PRWPCM involves a large number of widely distributed wind turbines, massive meteorological data across the whole province, and complex nonlinear correlations. This paper proposes a lightweight PRWPCM by integrating Geographic Information System (GIS) analysis technology and Convolutional Neural Network (CNN). First, we conduct the land suitability analysis for wind turbine sites through the multi-criteria GIS layer overlay method to make the provincial wind turbine land suitability map (WTLSM) with scored divisions from the least suitable to the most suitable areas. On this basis, a new fusion mechanism for geographic and meteorological information is proposed, through which the raw meteorological data matrix can be reconstructed to filter and amplify the meteorological information that is more relevant to the total wind power output of the province, and avoid the time-consuming and labor-intensive data collection and processing, large-size model construction and validation. Second, a CNN-based regression architecture is designed to further capture the mapping relationship between the reconstructed meteorological data and total wind power output of the province; each type of meteorological factor is considered as an input channel and the attention modules are introduced to adaptively enhance useful channels and suppress less useful ones. Numerical experiments based on the wind power operation data of Yunnan Province, China, are conducted to validate the superiority of the proposed PRWPCM via benchmarking against 13 classical methods. • Mapping relationship between wind power output and meteorological information at a province level is recognized by integrating GIS and CNN. • A new fusion mechanism for geographic and meteorological information is proposed to achieve lightweight modeling. • Comparative experimental studies are conducted in Yunnan province, China. [ABSTRACT FROM AUTHOR]

Microseismic technology has been widely used in geological hazard monitoring. However, the effective identification and classification of microseismic events in mines have always been a challenge in hazard monitoring. In this paper, a new method combining the empirical mode decomposition (EMD) algorithm and artificial neural network is proposed for the classification and identification of microseismic waveforms. This is the first study to explore such a model in the microseismic monitoring of coal mines. The data for this study were collected from a coal mine in northern China. A total of 2768 microseismic events and 2435 non-microseismic events were manually selected from a large amount of data. Firstly, the microseismic waveform reconstruction was performed using the 4th to 8th Intrinsic Mode Function (IMF) obtained by the EMD algorithm. Secondly, the peak factor, clearance factor, impulse factor, kurtosis, skewness, and waveform factor of each reconstructed microseismic waveform were used as features. Subsequently, the convolutional neural network (CNN) was used to classify microseismic events. Among them, data from 3600 microseismic events were used as the training set, and data from 1603 microseismic events were used as the test set. Finally, the original waveform data were used as input and compared with the classification results processed with the EMD algorithm. The classification methods of the BP neural network and Radial Basis Function (RBF) neural network were used for verification. The results of the processed features showed a significant increase in accuracy. The classification accuracy of CNN, BP neural network, and RBF neural network are 97.37%, 91.99%, and 94.23%, respectively. The results show that the utilization of the feature processing technique and CNN algorithm in this study demonstrates superior efficacy in microseismic event classification, which can be used in practice. • The EMD algorithm and ANN was constructed to classify microseismic waveforms. • Six features of the reconstructed waveform were selected as the classification basis. • The EMD algorithm was used to make the six waveform characteristics more significant. [ABSTRACT FROM AUTHOR]

Various satellite sensors have provided a huge amount of observations of Earth's environment at variable spatial and temporal resolutions. Many global coarse-resolution land products have been generated from single-satellite data, but global temporally regular land products at fine spatial resolutions (say 10-30 m) are scarce because of infrequent observations. An ideal inversion framework can estimate multiple global continuous land variables at different spatial resolutions by combining all sources of satellite data. This paper proposes a new framework that can estimate five land variables simultaneously from the top-of-atmosphere (TOA) reflectance acquired by seven satellite sensors based on a multi-scale and multi-depth convolutional neural network (MSDCNN). This framework enables us to estimate temporally regular land variables at as fine as 10 m spatial resolution by transforming information from satellite data at coarser spatial resolutions. The estimated land variables include Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), shortwave albedo, visible albedo, and spectral reflectance. Seven sensors that acquire data at different spatial resolutions, include Visible Infrared Imaging Radiometer Suite (VIIRS) (750 m), Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m), Fengyun-3 (FY-3) Medium Resolution Spectral Imager (MERSI) (250 m), China-Brazil Earth Resources Satellite 04 (CBERS-04) Wide Field Imager (WFI) (73 m), Landsat 8 Operational Land Imager (OLI) (30 m), Gaofen-1 (GF-1) Wide-Field-of-View (16 m) and Sentinel 2 A/B Multispectral Imager (MSI) (10 m). This framework is mainly composed of four steps. First, a Shuffled Complex Evolution (SCE) optimization method is adopted to estimate these variables from VIIRS TOA reference. Second, a joint-output random forest regression (RF) method is used to link the satellite observations and the estimated values from step 1. Third, the six-type multi-resolution satellite observations are used to downscale the VIIRS TOA reflectance to six different spatial resolutions by using the MSDCNN. Finally, the downscaled six-resolution VIIRS TOA reflectance is fed into the multiple-variable RF model to estimate land variables. The framework was validated, and the results based on high-resolution reference maps from ImagineS network and time series shortwave albedo field values from Surface Radiation (SURFRAD) and Integrated Carbon Observation System (ICOS) network show that the retrieved variables had high validation accuracy, with root mean square error (RMSE) ranges of 0.361–0.489 (LAI), 0.023–0.120 (FAPAR), 0.013–0.026 (snow-free shortwave albedo), respectively. Comparison results of the retrieved multi-scale variables with the Sentinel 2 A/B (10 m), Landsat 8 (30 m), Global LAnd Surface Satellite (GLASS, 250 m, 500 m), MODIS (500 m), and VIIRS (750 m) products show that their values were close, with RMSE ranges of 0.107–0.273 (LAI), 0.015–0.027 (FAPAR), 0.003–0.007 (shortwave albedo), 0.001–0.007 (visible albedo), and 0.003–0.025 (spectral reflectance), respectively. The results of the direct validation as well as the product intercomparison show that this novel framework has the potential to be used in estimating global land variables at various spatial scales using a variety of satellite data sources. • Exploring a deep learning method for seven-resolution land surface variable estimation. • The finest resolution of estimated variable is 10 m and the temporal continuity is improved. • Estimated variables at seven resolution have consistent spatial distribution. • The estimated multi-resolution variables have high accuracy based on extensive validation. [ABSTRACT FROM AUTHOR]