Search Results (3,186)

In recent years, there have been frequent extreme weather events that defy traditional understanding. Specifically, mountain flood disasters can cause significant loss of life due to their sudden onset and destructive power. The 7.21 flood event in Xingyang, Zhengzhou, China, recorded a maximum 6 h precipitation of 240.5 mm in the Suo River basin, corresponding to a 500-year return period, and causing fatalities and substantial damage. The central government of China has launched supplementary mountain flood disaster surveys and evaluations involving key towns and villages, following an initial round of surveys in riverside villages, to improve foresight and response capabilities for mountain flood disaster risks under extreme conditions. This paper introduces the contents of the national mountain flood disaster surveys and evaluations of key towns and villages, elaborating on the principles, content, and rules for auditing the national survey and evaluation results. This paper innovatively proposes professional audit criteria, such as early warning indicators, monitoring facility correlations, and hazard zoning, based on a formal audit of the data quality. The implementation of professional audit criteria improved the data accuracy by 85% and reduced false alarms by 40%, enhancing the overall effectiveness of mountain flood disaster prevention. The analysis of the audit results suggests that the audit rules for the survey and evaluation results of key towns are scientific, reasonable, and effective, achieving the expected goals of data quality control. This approach can effectively enhance the practical value of the survey and evaluation outcomes for key towns, laying a solid data foundation for transforming flood disaster prevention from merely “existing” to “optimal”. Full article

Battery cell voltage is an important evaluation index for electric vehicle condition estimation and one of the main monitoring parameters of the battery management system, and accurate voltage prediction is crucial for electric vehicle battery failure warning. Therefore, this paper proposes a novel hybrid gated recurrent unit and long short-term memory (GRU-LSTM) neural network to predict electric vehicle lithium-ion battery cell voltage. Firstly, Pearson coefficient correlation analysis is carried out to determine the input parameters of the neural network by analyzing the influence factors of the voltage parameters, and the hyperparameters of the neural network are determined through cross-validation to construct the lithium-ion battery single-unit voltage prediction model based on GRU-LSTM. Secondly, the voltage prediction accuracy and robustness of the GRU-LSTM model are verified by training the historical data of real vehicles in spring, summer, fall, and winter, combined with four different error indicators. Finally, the feasibility of the proposed method is verified by designing hierarchical warning rules based on the prediction data to realize the accurate warning of multiple voltage anomalies. Full article

Sorghum (Sorghum bicolor L.) is a globally important energy and food crop that is becoming increasingly integral to food security and the environment. However, its production is significantly hampered by various fungal phytopathogens that affect its yield and quality. This review aimed to provide a comprehensive overview of the major fungal phytopathogens affecting sorghum, their impact, current management strategies, and potential future directions. The major diseases covered include anthracnose, grain mold complex, charcoal rot, downy mildew, and rust, with an emphasis on their pathogenesis, symptomatology, and overall economic, social, and environmental impacts. From the initial use of fungicides to the shift to biocontrol, crop rotation, intercropping, and modern tactics of breeding resistant cultivars against mentioned diseases are discussed. In addition, this review explores the future of disease management, with a particular focus on the role of technology, including digital agriculture, predictive modeling, remote sensing, and IoT devices, in early warning, detection, and disease management. It also provide key policy recommendations to support farmers and advance research on disease management, thus emphasizing the need for increased investment in research, strengthening extension services, facilitating access to necessary inputs, and implementing effective regulatory policies. The review concluded that although fungal phytopathogens pose significant challenges, a combined effort of technology, research, innovative disease management, and effective policies can significantly mitigate these issues, enhance the resilience of sorghum production to facilitate global food security issues. Full article

The temperature-based crop water stress index (CWSI) is the most robust metric among precise techniques that assess the severity of crop water stress, particularly in susceptible crops like maize. This study used a unmanned aerial vehicle (UAV) to remotely collect data, to use in combination with the random forest regression algorithm to detect the maize CWSI in smallholder croplands. This study sought to predict a foliar temperature-derived maize CWSI as a proxy for crop water stress using UAV-acquired spectral variables together with random forest regression throughout the vegetative and reproductive growth stages. The CWSI was derived after computing the non-water-stress baseline (NWSB) and non-transpiration baseline (NTB) using the field-measured canopy temperature, air temperature, and humidity data during the vegetative growth stages (V5, V10, and V14) and the reproductive growth stage (R1 stage). The results showed that the CWSI (CWSI < 0.3) could be estimated to an R² of 0.86, RMSE of 0.12, and MAE of 0.10 for the 5th vegetative stage; an R² of 0.85, RMSE of 0.03, and MAE of 0.02 for the 10th vegetative stage; an R² of 0.85, RMSE of 0.05, and MAE of 0.04 for the 14th vegetative stage; and an R² of 0.82, RMSE of 0.09, and MAE of 0.08 for the 1st reproductive stage. The Red, RedEdge, NIR, and TIR UAV-bands and their associated indices (CCCI, MTCI, GNDVI, NDRE, Red, TIR) were the most influential variables across all the growth stages. The vegetative V10 stage exhibited the most optimal prediction accuracies (RMSE = 0.03, MAE = 0.02), with the Red band being the most influential predictor variable. Unmanned aerial vehicles are essential for collecting data on the small and fragmented croplands predominant in southern Africa. The procedure facilitates determining crop water stress at different phenological stages to develop timeous response interventions, acting as an early warning system for crops. Full article

SCADA data, due to their easy accessibility and low cost, have been widely applied in wind turbine gearbox condition monitoring. However, the high-dimensional and nonlinear nature of the collected data, along with the insufficient spatiotemporal feature capabilities of existing methods and the lack of consideration of the physical mechanisms of wind turbine operation, limit the accuracy of monitoring models. In this paper, a multivariate spatiotemporal feature fusion network is proposed for wind turbine gearbox condition monitoring. First, by analyzing the operational mechanism of wind turbines and the correlation between sensor data, the time series data are transformed into graph data. Then, graph convolutional networks and temporal convolutional networks are used to extract spatial and temporal features, respectively. Next, long short-term memory networks are employed to fuse the extracted temporal and spatial features, further capturing long-term spatiotemporal dependencies. Finally, the proposed method is validated using real data from two wind turbines. Experimental results show that the proposed method reduces the RMSE by 29.67% and 17.61% compared to the best-performing models. Moreover, the proposed method provides early warning signals 188.6 h and 133.67 h in advance, achieving stable and efficient early anomaly detection for wind turbines. Full article

The pine bark beetle is a devastating forest pest, causing significant forest losses worldwide, including 25% of pine forests in Honduras. This study focuses on Dendroctonus frontalis and Ips spp., which have affected four of the seven native pine species in Honduras: Pinus oocarpa, P. caribaea, P. maximinoi, and P. tecunumanii. Artificial intelligence (AI) is an essential tool for developing susceptibility models. However, gaps remain in the evaluation and comparison of these algorithms when modeling susceptibility to bark beetle outbreaks in tropical conifer forests using Google Earth Engine (GEE). The objective of this study was to compare the effectiveness of three algorithms—random forest (RF), gradient boosting (GB), and maximum entropy (ME)—in constructing susceptibility models for pine bark beetles. Data from 5601 pest occurrence sites (2019–2023), 4000 absence samples, and a set of environmental covariates were used, with 70% for training and 30% for validation. Accuracies above 92% were obtained for RF and GB, and 85% for ME, along with robustness in the area under the curve (AUC) of up to 0.98. The models revealed seasonal variations in pest susceptibility. Overall, RF and GB outperformed ME, highlighting their effectiveness for implementation as adaptive approaches in a more effective forest monitoring system. Full article

Algal bloom is a major ecological and environmental problem caused by abnormal algal reproduction in water, and it poses a serious threat to the aquatic ecosystem, drinking water safety, and public health. Because of the high dynamic and spatiotemporal heterogeneity of bloom outbreaks, the process often presents significant changes in a short time. Therefore, it has important scientific research value and practical application significance to construct an accurate and effective bloom warning model. This study constructs an integrated model combining sequence features, attention mechanisms, and random forest using machine learning algorithms for bloom prediction, based on watercolor geostationary satellite observations and meteorological data from GOCI in South Korea. In the process, high spatial resolution Sentinel-2 satellite data is also utilized for sample extraction. With a 10-m resolution, Sentinel-2 provides more precise spatial information compared to the 500-m resolution of GOCI, which significantly enhances the accuracy of the model, especially in monitoring local water body changes. The experimental results demonstrate that the model exhibits excellent accuracy and stability in the spatiotemporal prediction of water blooms. The average AUC value is 0.88, the F1 score is 0.72, and the accuracy is 0.79 when identifying the dynamic change of water bloom on the hourly scale. At the same time, this study summarized four typical diurnal change modes of effluent bloom, including dispersal mode, persistent outbreak mode, dispersal-regression mode, and subsidence mode, revealing the main characteristics of diurnal dynamic change of bloom. The research results provided strong technical support for water environment monitoring and water quality safety management and showed a good application prospect. Full article

Smart cities are confronted with a variety of disaster threats. Among them, natural fires pose a serious threat to human lives, the environment, and asset security. In view of the fact that existing research mostly focuses on the analysis of accident precursors, this paper proposes a dynamic risk-modeling method based on Stochastic Petri Nets (SPN) and Bayesian theory to deeply explore the evolution mechanism of urban natural fires. The SPN model is constructed through natural language processing techniques, which discretize the accident evolution process. Then, the Bayesian theory is introduced to dynamically update the model parameters, enabling the accurate assessment of key event nodes. The research results show that this method can effectively identify high-risk nodes in the evolution of fires. Their dynamic probabilities increase significantly over time, and key transition nodes have a remarkable impact on the emergency response efficiency. This method can increase the fire prevention and control efficiency by approximately 30% and reduce potential losses by more than 20%. The dynamic update mechanism significantly improves the accuracy of risk prediction by integrating real-time observation data and provides quantitative support for emergency decision making. It is recommended that urban management departments focus on strengthening the maintenance of facilities in high-risk areas (such as fire alarm systems and emergency passages), optimize cross-departmental cooperation processes, and build an intelligent monitoring and early-warning system to shorten the emergency response time. This study provides a new theoretical tool for urban fire risk management. In the future, it can be extended to other types of disasters to enhance the universality of the model. Full article

In recent years, the combined effects of rapid urbanization and climate change have led to frequent floods in urban areas. Rainstorm flood risk warning systems play a crucial role in urban flood prevention and mitigation. However, there has been limited research in China on nationwide urban flood risk warning systems based on rainfall predictions. This study constructs a two-level early warning system (EWS) at the national and urban levels using a two-dimensional hydrological–hydrodynamic model considering infiltration and urban drainage standards. A methodology for urban rainstorm flood risk warnings is proposed, leveraging short-term and high-resolution rainfall forecast data to provide flood risk warnings for 231 cities in central and eastern China. Taking Beijing as an example, a refined rainstorm flood warning technique targeting city, district, and street scales is developed. We validated the methodology with monitoring data from the “7.31” rainstorm event in 2023 in Beijing, demonstrating its applicability. It is expected that the findings of this study will serve as a valuable reference for the urban rainstorm flood risk warning system in China. Full article

Algal blooms are a major risk to aquatic ecosystem health and potable water safety. Traditional statistical models often fail to accurately predict algal bloom dynamics due to their complexity. Machine learning, adept at managing high-dimensional and non-linear data, provides a superior predictive approach to this challenge. In this study, we employed support vector machine (SVM), random forest (RF), and backpropagation neural network (BPNN) models to predict the severity of algal blooms in the Anzhaoxin River Basin based on an algal density-based grading standard. The SVM model demonstrated the highest accuracy with training and test set accuracies of 0.96 and 0.92, highlighting its superiority in small-sample learning. The Shapley Additive Explanations (SHAP) technique was utilized to evaluate the contribution of environmental variables in various predictive models. The results show that TP is the most significant environmental factor affecting the algal bloom outbreak in Anzhaoxin River, and the phosphorus management strategy is more suitable for the management of the artificial water body in northeast China. This study contributes to exploring the potential application of machine learning models in diagnosing and predicting riverine ecological issues, providing valuable insights and support for the protection and management of aquatic ecosystems in the Anzhaoxin River Basin. Full article

In this study, an ANN-derived innovative model was developed for estimating the failure soil depths of rainfall-induced shallow landslide events, named the SM_EFD_LS model. The proposed SM_EFD_LS model was created using the modified ANN model via the genetic algorithm calibration approach (GA-SA) with multiple transfer functions (MTFs) (ANN_GA-SA_MTF) with a significant number of failure soil depths and corresponding rainfall factors. Ten shallow landslide-susceptible spots in the Jhuokou watershed in southern Taiwan were selected as the study area. The associated 1000 simulations of rainfall-induced shallow landslide events were used in the model’s development and validation. The model validation results indicate that the validated failure soil depths are mainly located within the resulting 60% confidence intervals from the proposed SM_EFD_LS model. Moreover, the estimated failure depths resemble the validated ones, with acceptable averages of the absolute error (RMSE) and relative error (MRE) (11 cm and 0.06) and a high model reliability index of 1.2. In the future, the resulting probabilistic-based failure soil depths obtained using the proposed SM_EFD_LS model could be introduced with the desired reliability needed for early landslide warning and prevention systems. Full article

Introduction: Breast cancer (BC) awareness and preventive practices are critical for the early detection and effective management of the disease. This study aimed to assess the level of BC awareness among women residing in Al Baha, Saudi Arabia. Methods: A cross-sectional study was conducted using the Breast Cancer Awareness Measure (BCAM) survey tool to evaluate BC awareness among female residents of Al Baha between June and July 2023. The sample was obtained through the snowball sampling technique. Results: A total of 1007 women participated in the study, with a mean age of 29 ± 10.9 years. Overall awareness of BC warning signs and risk factors was low, with 45% of participants demonstrating poor awareness. Significant positive associations were found between BC awareness and factors such as level of education (p = 0.020), employment status (p = 0.023), field of study for students (p < 0.0001), and average monthly family income (p = 0.001). Furthermore, 75% of participants rarely or never practiced breast self-examination, and only 37% of those invited to the Ministry of Health’s screening program had attended. Conclusions: The results highlight a significant lack of awareness and knowledge about BC among women in Al Baha. These findings underscore the urgent need for targeted educational initiatives and awareness campaigns to address this knowledge gap and promote preventive practices. Full article

Background: Dengue, transmitted by Aedes spp. mosquitoes, poses significant public health challenges in Burkina Faso. This study investigated outbreak thresholds, utilizing historical data since 2018 to explore the climatic impacts on dengue transmission and address knowledge gaps. Methodology: This retrospective ecological study utilized historical and contemporary data from Burkina Faso’s Public Health Ministry (2018–2024) to model dengue outbreak thresholds. A combination of epidemic channel analysis, joinpoint regression, climate–disease relationship analysis, and negative binomial regression was employed to provide comprehensive insights into the factors driving dengue outbreaks. Principal Findings: The incidence of probable dengue cases remained stable, mostly below 5 cases per 100,000 people, except for a sharp surge in week 40 of 2023, peaking at 38 cases per 100,000. This surge was brief, normalizing by week 47, but coincided with a marked increase in mortality, reaching 90 deaths in week 45. Joinpoint regression identified key thresholds, an alert at 2.1 cases per 100,000 by week 41 and an intervention threshold at 19.1 cases by week 44, providing a framework for timely public health responses. Climatic factors significantly influenced dengue transmission, with higher temperatures (RR = 2.764) linked to increased incidence, while higher precipitation (RR = 0.551) was associated with lower case numbers, likely due to disrupted mosquito breeding conditions. Additionally, intermediate precipitation levels showed a complex relationship with higher incidence rates. Conclusions: This study established evidence-based epidemiological thresholds for dengue outbreak detection in Burkina Faso (2018–2024), demonstrating temperature as a primary transmission driver while precipitation showed inverse relationships. Analysis of the 2023 outbreak identified a critical five-week intervention window (weeks 40–45), providing a framework for climate-sensitive early warning systems. These findings advance the understanding of dengue dynamics in West Africa, though future research should integrate geographical and socioeconomic variables to enhance predictive modeling and outbreak preparedness. Full article

It is imperative to assess and comprehend the hydrological processes of the river basin in light of the potential effects of land use/land cover and climate changes. The study’s main objective was to evaluate hydrologic response of water balance components to the projected land use/land cover (LULC) and climate changes in the Omo–Gibe River Basin, Ethiopia. The study employed historical precipitation, maximum and minimum temperature data from meteorological stations, projected LULC change from module for land use simulation and evaluation (MOLUSCE) output, and climate change scenarios from coupled model intercomparison project phase 6 (CMIP6) global climate models (GCMs). Landsat thematic mapper (TM) (2007) enhanced thematic mapper plus (ETM+) (2016), and operational land imager (OLI) (2023) image data were utilized for LULC change analysis and used as input in MOLUSCE simulation to predict future LULC changes for 2047, 2073, and 2100. The predictive capacity of the model was evaluated using performance evaluation metrics such as Nash–Sutcliffe Efficiency (NSE), the coefficient of determination (R2), and percent bias (PBIAS). The bias correction and downscaling of CMIP6 GCMs was performed via CMhyd. According to the present study’s findings, rainfall will drop by up to 24% in the 2020s, 2050s, and 2080s while evapotranspiration will increase by 21%. The findings of this study indicate that in the 2020s, 2050s, and 2080s time periods, the average annual Tmax will increase by 5.1, 7.3, and 8.7%, respectively under the SSP126 scenario, by 5.2, 10.5, and 14.9%, respectively under the SSP245 scenario, by 4.7, 11.3, and 20.7%, respectively, under the SSP585 scenario while Tmin will increase by 8.7, 13.1, and 14.6%, respectively, under the SSP126 scenario, by 1.5, 18.2, and 27%, respectively, under the SSP245 scenario, and by 4.7, 30.7, and 48.2%, respectively, under the SSP585 scenario. Future changes in the annual average Tmax, Tmin, and precipitation could have a significant effect on surface and subsurface hydrology, reservoir sedimentation, hydroelectric power generation, and agricultural production in the OGRB. Considering the significant and long-term effects of climate and LULC changes on surface runoff, evapotranspiration, and groundwater recharge in the Omo–Gibe River Basin, the following recommendations are essential for efficient water resource management and ecological preservation. National, regional, and local governments, as well as non-governmental organizations, should develop and implement a robust water resources management plan, promote afforestation and reforestation programs, install high-quality hydrological and meteorological data collection mechanisms, and strengthen monitoring and early warning systems in the Omo–Gibe River Basin. Full article

The dynamics of large-scale components of the Earth climate system (tipping elements), particularly the identification of their possible critical transitions and the proximity to the corresponding tipping points, has been attracting considerable attention recently. In this paper, we focus on one specific tipping element, namely ocean anoxia. It has been shown previously that a sufficiently large, ‘over-critical’ increase in the average water temperature can disrupt oxygen production by phytoplankton photosynthesis, hence crossing the tipping point, which would lead to global anoxia. Here, using a conceptual mathematical model of the plankton–oxygen dynamics, we show that this tipping point of global oxygen depletion is going to be preceded by an additional, second tipping point when the Oxygen Minimum Zones (OMZs) start growing. The OMZ growth can, therefore, be regarded as a spatially explicit early warning signal of the global oxygen catastrophe. Interestingly, there is growing empirical evidence that the OMZs have indeed been growing in different parts of the ocean over the last few decades. Thus, this observed OMZ growth may indicate that the second tipping point has already been crossed, and hence, the first tipping point of global ocean anoxia may now be very close. Full article