Search Results (211)

In Kenya, Rift Valley fever (RVF) outbreaks pose significant challenges, being one of the most severe climate-sensitive zoonoses. While machine learning (ML) techniques have shown superior performance in time series forecasting, their application in predicting disease outbreaks in Africa remains underexplored. Leveraging data from the International Livestock Research Institute (ILRI) in Kenya, this study pioneers the use of ML techniques to forecast RVF outbreaks by analyzing climate data spanning from 1981 to 2010, including ML models. Through a comprehensive analysis of ML model performance and the influence of environmental factors on RVF outbreaks, this study provides valuable insights into the intricate dynamics of disease transmission. The XGB Classifier emerged as the top-performing model, exhibiting remarkable accuracy in identifying RVF outbreak cases, with an accuracy score of 0.997310. Additionally, positive correlations were observed between various environmental variables, including rainfall, humidity, clay patterns, and RVF cases, underscoring the critical role of climatic conditions in disease spread. These findings have significant implications for public health strategies, particularly in RVF-endemic regions, where targeted surveillance and control measures are imperative. However, this study also acknowledges the limitations in model accuracy, especially in scenarios involving concurrent infections with multiple diseases, highlighting the need for ongoing research and development to address these challenges. Overall, this study contributes valuable insights to the field of disease prediction and management, paving the way for innovative solutions and improved public health outcomes in RVF-endemic areas and beyond. Full article

Accurate precipitation forecasting remains a critical challenge due to the nonlinear and multifactorial nature of rainfall dynamics. This is particularly important in arid regions like Tamanghasset, where precipitation is the primary driver of agricultural viability and water resource management. This study evaluates the performance of several time series models for monthly rainfall prediction, including the autoregressive integrated moving average (ARIMA), Exponential Smoothing State Space Model (ETS), Seasonal and Trend decomposition using Loess with ETS (STL-ETS), Trigonometric Box–Cox transform with ARMA errors, Trend and Seasonal components (TBATS), and neural network autoregressive (NNAR) models. Historical monthly precipitation data from 1953 to 2020 were used to train and test the models, with lagged observations serving as input features. Among the approaches considered, the NNAR model exhibited superior performance, as indicated by uncorrelated residuals and enhanced forecast accuracy. This suggests that NNAR effectively captures the nonlinear temporal patterns inherent in the precipitation series. Based on the best-performing model, rainfall was projected for the year 2021, providing actionable insights for regional hydrological and agricultural planning. The results highlight the relevance of neural network-based time series models for climate forecasting in data-scarce, climate-sensitive regions. Full article

This study investigates the influence of root cohesion spatial heterogeneity on rainfall-induced landslide distribution across the Loess Plateau, addressing limitations in existing methods that oversimplify root reinforcement. Leveraging Landsat and GaoFen satellite images, we developed a regional root cohesion inversion model that quantifies spatial heterogeneity using tree height (derived from time series Landsat imagery) and above-ground biomass (from 30 m resolution satellite products). This approach, integrated with land use-specific hydrological parameters and an infinite slope stability model, significantly improves landslide susceptibility predictions compared to models ignoring root cohesion or using uniform assignments. High-resolution pre- and post-rainfall GaoFen satellite imagery validated landslide inventories, revealing dynamic susceptibility patterns: farmland exhibited the highest risk, followed by artificial and secondary forests, with susceptibility escalating post-rainfall. This study underscores the critical role of remote sensing-driven root cohesion mapping in landslide risk assessment, offering actionable insights for land use planning and disaster mitigation on the Loess Plateau. Full article

Stream–aquifer interactions, as well as surface water/groundwater interactions within wetlands, require a solution of complex partial differential equations of flow and contaminant transport, namely a deterministic approach. Groundwater level (GWL) responses to precipitation, particularly for extreme value events such as annual maxima, require a probabilistic approach to evaluate potential climate trends. It is commonly assumed that the distribution of annual maxima series (AMS) precipitation follows the generalized extreme value distribution (GEV). If the extremes of the data are nonstationary, it is possible to incorporate this knowledge into the parameters of the GEV. This approach is also applied to the computed annual maxima of daily groundwater level data. Nonstationary versus stationary time series for both groundwater level and AMS 24-h duration precipitation are compared for National Oceanic and Atmospheric Administration (NOAA) stations with nearby wells. Predicted extreme value analysis (EVA) climate trends for wells penetrating limestone aquifers directly beneath rainfall monitoring stations at major airports indicate similar GWL response. Groundwater levels at wells located near coastlines are partially impacted by sea level rise. An extreme value analysis of the GWL is shown to be a useful tool to confirm hydrologic connections and long-term climate trends. Full article

Flooding is the most frequent natural hazard that accompanies hardships for millions of civilians and substantial economic losses. In Sri Lanka, fluvial floods cause the highest damage to lives and properties. Ratnapura, which is in the Kalu River Basin, is the area most vulnerable to frequent flood events in Sri Lanka due to inherent weather patterns and its geographical location. However, flood-related studies conducted based on the Kalu River Basin and its most vulnerable cities are given minimal attention by researchers. Therefore, it is crucial to develop a robust and reliable dynamic flood forecasting system to issue accurate and timely early flood warnings to vulnerable victims. Modeling the water level at the initial stage and then classifying the results of this into pre-defined flood risk levels facilitates more accurate forecasts for upcoming susceptibilities, since direct flood classification often produces less accurate predictions due to the heavily imbalanced nature of the data. Thus, this study introduces a novel hybrid model that combines a deep leaning technique with a traditional Linear Regression model to first forecast water levels and then detect rare but destructive flood events (i.e., major and critical floods) with high accuracy, from 1 to 3 days ahead. Initially, the water level of the Kalu River at Ratnapura was forecasted 1 to 3 days ahead by employing a Vanilla Bi-LSTM model. Similarly to water level modeling, rainfall at the same location was forecasted 1 to 3 days ahead by applying another Bi-LSTM model. To further improve the forecasting accuracy of the water level, the forecasted water level at day t was combined with the forecasted rainfall for the same day by applying a Time Series Regression model, thereby resulting in a hybrid model. This improvement is imperative mainly because the water level forecasts obtained for a longer lead time may change with the real-time appearance of heavy rainfall. Nevertheless, this important phenomenon has often been neglected in past studies related to modeling water levels. The performances of the models were compared by examining their ability to accurately forecast flood risks, especially at critical levels. The combined model with Bi-LSTM and Time Series Regression outperformed the single Vanilla Bi-LSTM model by forecasting actionable flood events (minor and critical) occurring in the testing period with accuracies of 80%, 80%, and 100% for 1- to 3-day-ahead forecasting, respectively. Moreover, overall, the results evidenced lower RMSE and MAE values (<0.4 m MSL) for three-days-ahead water level forecasts. Therefore, this enhanced approach enables more trustworthy, impact-based flood forecasting for the Rathnapura area in the Kalu River Basin. The same modeling approach could be applied to obtain flood risk levels caused by rivers across the globe. Full article

Exposure to antibiotic-resistant microbial communities in coastal waters is an important threat to human health. Through a ten-year coastal time series, we used metagenomics from 236 time points to provide a comprehensive understanding of the seawater resistome, temporal distribution, and factors influencing frequencies of specific resistance types. Here, we predicted that antibiotic resistance gene frequencies would increase during the winter due to increased rainfall, with terrestrial and enteric taxa serving as the primary carriers of resistance genes in coastal waters. We found that seasonal and interannual trends of antibiotic resistance genes vary by gene and the taxa carrying them, as opposed to a general increase in most resistance genes during specific seasons. However, we found that precipitation and Enterococcus levels may be accurate indicators for total antibiotic resistance gene levels in Newport Beach coastal water. Resistance genes were primarily carried by marine taxa, though some terrestrial taxa and opportunistic pathogens also harbored these genes. Non-marine taxa can be introduced through rain, human activity, or sewage spills. By using metagenomics, we were able to elucidate the antibiotic-resistant bacterial communities in Newport Beach coastal water and demonstrate both seasonal and multiannual trends in their abundance with important implications for local health and safety. Full article

With an increase in the intensity and frequency of extreme rainfall events, there is a pressing need for accurate rainfall nowcasting applications. In recent years, precipitable water vapor (PWV) data obtained from GNSS observations have been widely used in rainfall prediction. Unlike previous studies mainly focusing on rainfall occurrences, this study proposes a transformer-based model for hourly rainfall prediction, integrating the GNSS PWV and ERA5 meteorological data. The proposed model employs the ProbSparse self-attention to efficiently capture long-range dependencies in time series data, crucial for correlating historical PWV variations with rainfall events. Additionally, the adoption of the DILATE loss function better captures the structural and timing aspects of rainfall prediction. Furthermore, traditional rainfall prediction models are typically trained on datasets specific to one region, which limits their generalization ability due to regional meteorological differences and the scarcity of data in certain areas. Therefore, we adopt a pre-training and fine-tuning strategy using global datasets to mitigate data scarcity in newly deployed GNSS stations, enhancing model adaptability to local conditions. The evaluation results demonstrate satisfactory performance over other methods, with the fine-tuned model achieving an MSE = 3.954, DTW = 0.232, and TDI = 0.101. This approach shows great potential for real-time rainfall nowcasting in a local area, especially with limited data. Full article

This study aimed to compare and evaluate three satellite-based precipitation estimation products: Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Early Run (IMERG-Early Run), Climate Prediction Center MORPHing technique Real Time (CMORPH-RT), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Dynamic Infrared Rain rate Now (PDIR-Now) to identify the optimal integration strategies to improve the extreme rainfall estimation during Super Typhoon Yagi (September, 2024) in Hanoi, Vietnam, using validation data from 25 ground stations. In-depth analysis of three extreme rainfall series during Typhoon Yagi (6–9 September 2024), examining 93 extreme rainfall events at the 95th percentile precipitation threshold (R95p = 21.78 mm/h), combined with statistics at lower percentile thresholds (R1p, R5p, R10p, and R90p) and upper percentile threshold (R99p), revealed IMERG-Early best captured the peak rainfall, CMORPH-RT achieved highest total rainfall accuracy, while PDIR-Now offered the best spatial analysis. However, limitations included time lags, inability to detect rainfall events above R99p (41.69 mm/hour), and low detection rates (8–12%) in areas first impacted by the typhoon. This study identified that integration strategies combining different satellite products based on their strengths at specific time scales showed potential for improved rainfall estimation: PDIR-Now with IMERG-Early (1–3 h) and IMERG-Early with CMORPH-RT (6–12 h). These integration approaches accounted for each product’s unique capabilities in capturing different aspects of extreme rainfall during super typhoon events. Full article

Ground subsidence is a critical factor affecting the structural integrity and operational safety of high-speed railways, especially in areas with widespread soft ground. This study applies Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) techniques to monitor ground deformation along the Honam High-Speed Railway in South Korea. By processing a time series of 29 high-resolution SAR images from 2016 to 2019, the analysis yielded continuous, millimeter-level measurements of surface displacement. Maximum subsidence rates exceeding −12 mm/year were detected in embankment zones with soft subsoil conditions Validation using leveling data and corner reflectors showed strong agreement (R² > 0.93), confirming the accuracy and reliability of PS-InSAR-derived results. The study also revealed seasonal variation in settlement patterns, highlighting the influence of rainfall and pore water pressure. The findings underscore the utility of PS-InSAR as a sustainable and cost-effective tool for long-term infrastructure monitoring. This study further contributes to the development of predictive maintenance strategies and highlights the need for future research integrating PS-InSAR with geotechnical, hydrological, and construction-related variables to enhance monitoring precision and expand its practical applicability in infrastructure management. Full article

The rapid development of urban infrastructure has accelerated the construction of large foundation pit projects, posing challenges for deformation monitoring and safety. This study proposes a novel approach integrating time-series InSAR data with a multivariate LE-Transformer model for deformation prediction. The LE-Transformer model integrates Long Short-Term Memory (LSTM) to capture temporal dependencies, Efficient Additive Attention (EAA) to reduce computational complexity, and Transformer mechanisms to model global data relationships. Deformation monitoring was performed using PS-InSAR and SBAS-InSAR techniques, showing a high correlation coefficient (0.92), confirming the reliability of the data. Gray relational analysis identified key influencing factors, including rainfall, subway construction, residential buildings, soil temperature, and hydrogeology, with rainfall being the most significant (correlation of 0.838). These factors were incorporated into the LE-Transformer model, which outperformed univariate models, achieving a mean absolute percentage error (MAPE) of 2.5%. This approach provides a robust framework for deformation prediction and early warning systems in urban infrastructure projects. Full article

Accurately predicting road traffic flows is a primary challenge in the development of smart cities, providing a scientific basis and reference for urban planning, construction, and traffic management. Road traffic flow is influenced by various complex features, including temporal and weather conditions, which introduce challenges to traffic flow prediction. To enhance the accuracy of traffic flow prediction and improve the adaptability across different weather conditions, this study introduced a traffic flow prediction model with explicit consideration of weather factors including temperature, rainfall, air quality index, and wind speed. The proposed model utilized grey relational analysis (GRA) to transform weather data into weighted traffic flow data, expanded input variables into a new data matrix, and employed one-dimensional convolutional neural networks (CNNs) to extract valuable feature information from these input variables, as well as bidirectional long short-term memory (BiLSTM) to capture temporal dependencies within the time-series data. Bayesian optimization was employed to fine-tune the hyperparameters of the model, offering advantages such as fewer iterations, high efficiency, and fast speed. The performance of the proposed prediction model was validated using the traffic flow data collected at an intersection in China and on the M25 motorway in the United Kingdom. The results demonstrated the effectiveness of the proposed model, achieving improvements of at least 9.0% in MAE, 2.8% in RMSE, 2.3% in MAPE, and 0.06% in R² compared to five baseline models. Full article

This study investigated a time series model using a moving average (MA) for predicting rainfall trend in seven areas (Panggang, Gedangan, Kedung Keris, Ngawen, Wanagama, Tepus, and Playen) located in the Gunung Kidul Province, Yogyakarta, Indonesia. A database with daily rainfall data covering the period of 2010–2019 obtained from the Central Statistical Body of Gunungkidul District (BPS), Indonesia, were analysed. In this study, the MA was developed using Microsoft Excel. Six performance indicators, including Root Mean Square Error (RMSE) and Index of Agreement (IA), were used to evaluate the goodness-of-fit of the time series model. The results specify that the MA is a reliable model, with the RMSE and IA values in the ranges of 10.7–18.3 and 0.80–0.85, respectively. Small error and high agreement rates between the observed and predicted values indicates that prediction using MA method has good potential to be used as one of the prediction tools for rainfall modelling. Full article

In order to investigate the changes in winter wheat yield and the factors influencing it, five meteorological factors—such as rainfall and soil moisture—collected from the experimental area between 2010 and 2022 were used as characteristic features. A combined model of GNN (Graph Neural Network), based on the Fourier transform and the Random Forest algorithm was developed to predict winter wheat yield. Matrix multiplication in Fourier space was performed to predict yield, while the Random Forest algorithm was employed to quantify the contribution of various yield factors to winter wheat yield. The combined model effectively captured the dynamic dependencies between yield factors and time series, improving predictive accuracy by 5.00%, 10.00%, and 27.00%, and reducing the root mean square error by 26.26%, 29.31%, and 88.20%, respectively, compared to the StemGNN, Informer, and Random Forest models. The predicted outputs ranged from 520 to 720 g/m², with an average error of 2.69% compared to the actual measure outputs. Under the insufficient real-time irrigation mode, winter wheat yield was highest at 90% irrigation upper limit and 70% irrigation lower limit, with a medium fertilization level (850 mg/kg). The yield showed an overall decreasing trend as both irrigation limits and fertilizer application decreased. Rainfall and soil moisture were the most significant factors influencing winter wheat yield, followed by air temperature and evapotranspiration. Solar radiation and sunshine duration had the least impact. The results of this study provide a valuable reference for accurately predicting winter wheat yield. Full article

Urban green spaces, including green roofs (GRs), are vital infrastructure for climate resilience, retaining water in city landscapes and supporting ecohydrological processes. Quantifying the hydrologic performance of GRs in the urban environment for future climate scenarios is the original contribution of this research developed within the URCA! project. For this purpose, a continuous modelling approach is undertaken to evaluate the hydrological performance of GRs expressed by means of the runoff volume and peak flow reduction at the event scale for long data series (at least 20 years). To investigate the prediction of GRs performance in future climates, a simple methodological approach is proposed, using monthly projection factors for the definition of future rainfall and temperature time series, and transferring the system parametrization of the current model to the future one. The proposed approach is tested for experimental GR sites in Genoa and Rende, located in Northern and Southern Italy, respectively. Referring to both the Genoa and Rende experimental sites, simulation results are analysed to demonstrate how the GR performance varies with respect to rainfall event characteristics, including total depth, maximum rainfall intensity and ADWP for current and future scenarios. Full article

Monitoring and analyzing reservoir landslides are essential for predicting and mitigating geohazards, which are crucial for maintaining sustainability and supporting socio-economic development in reservoir areas. High spatiotemporal resolution is vital for effective reservoir landslide monitoring and analysis. For this purpose, we improved the resolution of the differential interferometric synthetic aperture radar (DInSAR) technique by fusing two-path deformation results from an overlapping Sentinel-1 area. First, we summarized the mathematical ratio relationship between deformation from the two paths. Second, time-series linear interpolation and time-reference difference removal were applied to the two separate deformation results of time-series DInSAR. Third, a ratio algorithm was adopted to fuse the deformation of the two paths into one integrated time-series result. The standard deviations of the deformation before and after fusion were similar, confirming the accuracy of the fusion results and feasibility of the method. From the integrated deformation, we analyzed the hydraulic impact, mechanisms, and physical processes associated with four reservoir landslides in the Three Gorges Reservoir area of China, accounting for rainfall and water-level data. The comprehensive analysis presented herein provides new insights on the hydraulic mechanisms of reservoir landslides and verifies the efficacy of this new integrated method for landslide investigation and monitoring. Full article