Stochastic Environmental Research and Risk Assessment最新文献

Predicting landslide displacement is crucial for the prevention and mitigation of landslide disasters. This study proposes a method based on a stacking ensemble learning strategy to predict landslide displacement, incorporating distinct yet effective individual models: the Voight model, the GM(1,1) grey model, and the backpropagation neural network (BPNN). These models are respectively emblematic of empirical, statistical, and nonlinear approaches to modeling. The stacking ensemble learning method marries creep theory, grey system theory, and nonlinear theory to accurately capture the statistical trends and step-like behavior characteristic of landslide displacement. A key feature of this approach is the tailored use of non-cross-validation, partial cross-validation, and 5-fold cross-validation for the Voight, GM(1,1), and BPNN models, respectively. This ensures that the conditions for model applicability are satisfied while fully leveraging their strengths, allowing the ensemble method to enhance prediction performance. The method is demonstrated through its application to the Xintan landslide in Zigui County, Hubei, China. Comparative analysis of the Voight, GM(1,1), BPNN, and the proposed stacking ensemble learning model reveals that the ensemble model achieves superior accuracy, underscoring its effectiveness in predicting landslide displacement. This promising method can effectively capture the landslide evolution process and be promoted to predict displacement in other landslide scenarios.

Global climate change leads to an increase in the frequency and intensity of extreme rainstorms. At present, in China, which is experiencing rapid urbanization, urban flooding triggered by heavy rainstorms has emerged as a prominent issue, exerting far-reaching influences on socio-economic development, ecology, and people’s livelihoods. In response to this, China has put forward the concept of sponge cities and has shifted from pilot projects to comprehensive and systematic citywide implementation, with the aim of strengthening urban resilience in rainstorm management. This article takes the Chaoyang River area in Nanning City, South China as an example and proposes a dynamic risk assessment framework integrating hydrodynamic models and ABM to reflect flooding and pedestrian response to rainstorms. The research results show that under the design rainstorm scenarios with return periods of 5 years and 30 years, the rainstorm-induced flooding process in the study area presents a dynamic evolution pattern. It develops rapidly to the extreme or severe hazard grade within 1–2 h, and then declines slowly and persists until 8 h. The exposure and sensitivity of pedestrian mobility to flooding disasters extend across most areas of the study area within 1–5 h during the rainstorm-induced flooding process, with medium, high, or extreme risk levels observed during the 2–3 h period.Among the affected sensitive pedestrians, the gender ratio is roughly equal, and the proportion of the elderly and child populations is as high as 46.5%. The overall disaster resilience capacity of the study area is significantly insufficient, leading to a predominance of medium, high, or extreme risk levels for pedestrian mobility during the 2–3 h period. In conclusion, the study area should accelerate the construction of various sponge facilities on the underlying surface and comprehensively enhance diverse emergency management measures for excessive flooding disasters. The dynamic disaster risk simulation and assessment techniques proposed in this article can serve as essential scientific support for the construction of a digital twin system in China’s sponge cities, reflecting both virtual and real scenarios and facilitating comprehensive resilience capabilities such as forecasting, warning, simulation, and contingency planning for intelligent rainstorm management.

The service life of downstream dams, river hydraulics, waterworks construction, and reservoir management is significantly affected by the amount of sediment load (SL). This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee Colony (ABC) optimization techniques for the estimation of monthly SL values in the Çoruh River in Northeastern Turkey. The estimation of SL values was achieved using inputs of previous SL and streamflow values provided to the models. Various statistical metrics were used to evaluate the accuracy of the established hybrid and stand-alone models. The hybrid model is a novel approach for estimating sediment load based on various input variables. The results of the analysis determined that the ABC-ANN hybrid approach outperformed others in SL estimation. In this study, two combinations, M1 and M2, with different input variables, were used to assess the model's accuracy, and the best-performing model for monthly SL estimation was identified. Two scenarios, Q(t) and Q(t − 1), were coupled with the ABC-ANN algorithm, resulting in a highly effective hybrid approach with the best accuracy results (R² = 0.90, RMSE = 1406.730, MAE = 769.545, MAPE = 5.861, MBE = − 251.090, Bias Factor = − 4.457, and KGE = 0.737) compared to other models. Furthermore, the utilization of FA and ABC optimization techniques facilitated the optimization of the ANN model parameters. The significant results demonstrated that the optimization and hybrid techniques provided the most effective outcomes in forecasting SL for both combination scenarios. As a result, the prediction outputs achieved higher accuracy than those of a stand-alone ANN model. The findings of this study can provide essential resources to various managers and policymakers for the management of water resources.

Time series prediction of natural calamities is effectively solved with deep neural networks due to their ability to automatically assimilate the temporal linkages in time series data. This research develops a hybrid stacked deep learning with one-dimensional Convolution–Stacked Bidirectional Gated Recurrent Unit (Conv1D-SBiGRU) algorithm, unifying the predictive advantages of one-dimensional Convolution (Conv1D) and Bidirectional Gated Recurrent Unit (BiGRU) using hydro-meteorological and atmospheric data to build and evaluate a flood prediction model in forecasting the phenomenon of forthcoming flood events. The one-dimensional Convolution model effectively obtains valuable information and learns the time series cognitive representation. The stacked BiGRU model efficiently identifies and models the data sequence with temporal dependencies due to their ability to learn from past and future moments. The developed predictive model uses statistically significant predicted rainfall value to estimate the daily Relative Departure Index (RDI) which is used to predict floods. The proposed work was trained and evaluated for predicting floods on the real-world data of Alappuzha district, Kerala, India. The findings demonstrate the preeminence of the Conv1D-SBiGRU-based flood model with around 33% reduced MAE and RMSE and 9% improved R² over the benchmark and some hybrid techniques. The outcomes showed the efficiency of Conv1D-SBiGRU in precisely forecasting floods during extreme weather events with an accuracy of 98.6%.

With the goal of forecasting streamflow time series with sufficient lead time, we evaluate the efficiency and accuracy of data-based models ranging from relatively simple to complex. Based on this, we systematically explain the model construction and selection process according to lead time, type and amount of data, and optimization method. This analysis involved optimizing the inputs and hyperparameters of four unique data-driven models: Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Transformer (TRANS), which were applied to the Soyang watershed, South Korea. The type and amount of model inputs are determined through a fine-tuning process that samples based on a correlation threshold, correlation to predictand, and autocorrelation to historical data and evaluates the simulated objective function. Hyperparameters are simultaneously optimized using three conventional optimization methods: Bayesian optimization (BO), particle swarm optimization (PSO), and gray wolf optimization (GWO). The experimental results provide insight into the role of input predictors, data preparations (e.g., wavelet transform), hyperparameter optimization, and model structures. From this, we can provide guidelines for model selection. Relatively simple models can be used when the dataset is small or there are few input variables, when only the near future is predicted, or when the selection of optimization methods is limited. However, a more complex model should be selected if the type and amount of data are sufficient, various optimization methods can be applied, or it is necessary to secure more lead time. More parameters, more complex model structures, and more training materials make this possible.

Failures in the integration of solar energy into smart grids can have significant implications for energy reliability and environmental sustainability, resulting in a greater dependence on conventional energy sources and increased carbon emissions. These failures can impact system functionality, efficiency, and long-term cost savings. Therefore, failure analysis plays a crucial role in identifying the underlying causes, devising appropriate solutions, and enhancing the performance of solar integration within smart grid systems. The conventional method of failure mode and effects analysis (FMEA) is widely utilized to identify failure modes in various processes. However, the Risk Priority Number (RPN) scoring system employed in FMEA has faced criticism due to its limitations. To overcome this challenge, our hybrid FMEA approach integrates cost and time considerations into the RPN calculation, thereby enhancing the assessment of failure factors. In the second step of our methodology, we utilize the Spherical Fuzzy Step-Wise Weight Assessment Ratio Analysis (SF-SWARA) technique and expert insights to determine the weightage of the five underlying factors. Lastly, in the third phase, we propose the Spherical Fuzzy Weighted Aggregated Sum Product Assessment (SF-WASPAS) method to prioritize risks based on the outcomes of the previous phases, while taking into account the uncertainty in the determinants and assigning varying weights to them. According to SF-WASPAS, the highest-rated failure is connectivity and cybersecurity, underscoring the critical importance of ensuring secure and reliable connections in solar systems. Additionally, the FMEA results indicate that overheating or fire ranks as the most significant risk, emphasizing the need for effective fire prevention and mitigation strategies.

Streamflow forecasting holds a pivotal role in the effective management of water resources, flood control, hydropower generation, agricultural planning, and environmental conservation.

This study assessed the effectiveness of a stacked Multilayer Perceptron-Random Forest (MLP-RF) ensemble model for short- to medium-term (7 to 15 days ahead) daily streamflow forecasts in the UK. The stacked model combines MLP and RF, enhancing generalization by capturing complex nonlinear relationships and robustness to noisy data. Stacking reduces bias and variance by aggregating predictions and addressing differing sources of bias and variance in MLP and RF. Furthermore, this ensemble model is computationally inexpensive. The study also examined the impact of different meta-learner algorithms, Elastic Net (EN), Isotonic Regression (IR), Pace Regression (PR), and Radial Basis Function (RBF) Neural Networks, on model performance.

For 1-day ahead forecasts, all models performed well (Kling Gupta efficiency, KGE, from 0.921 to 0.985, mean absolute percentage error, MAPE, from 3.59 to 13.02%), with minimal impact from the choice of meta-learner. At 7-day ahead forecasts, satisfactory results were obtained (KGE from 0.876 to 0.963, MAPE from 11.53 to 24.55%), while at the 15-day horizon, accuracy remained reasonable (KGE from 0.82 to 0.961, MAPE from 18.31 to 34.38%). The RBF meta-learner generally led to more accurate predictions, particularly affecting low and peak flow rates. RBF consistently outperformed in predicting low flow rates, while EN excelled in predicting flood flow rates in many cases. For estimating total discharged water volume, all models exhibited low relative error (< 0.08).

Climate change is worsening existing vulnerabilities in developing countries such as Yemen. This study examined the spatial distribution trends of extreme climate indices defined by ETCCDI (Expert Team on Climate Change Detection and Indices), for precipitation and temperature, from 1988 to 2021. It employed both the classical Mann–Kendall (MK) test as well as its modified (MMK) version that accounts for long-term persistence in hydroclimatic time series, that could otherwise impact the significance of the identified trends. It represents the first country-level investigation of climate extremes in Yemen using ERA5 reanalysis data to overcome the limitations of station data. Results found widespread increases in temperature indices, indicating significant warming nationwide. Minimum temperatures amplified more than maximums, particularly TNn (the minimum of the minimum temperature), with an increasing trend of more than 0.7℃ per decade. Inland cities exhibited more substantial warming than coastal cities. Precipitation trends displayed higher spatial variability, with intensity indices declining across most areas, raising drought concerns. However, Socotra Island presents an exception, with increased precipitation intensity and heightened flood risks. Furthermore, spatial heterogeneity in precipitation indices underscored Yemen’s complex terrain. Fewer trends were significant when applying the MMK test versus MK, confirming the impact of climate variability over the region. This research identifies the most climate-vulnerable regions to prioritise focused adaptation actions. Adaptation strategies are urgently needed, including efficient irrigation, flood assessments for Socotra Island, and investigation of projected climate changes and their implications under diverse topographic and climatic influences.

The escalating climate instability and extreme weather events significantly jeopardize food security. The study assessed the impact of long-term climatic variables and extreme weather events on soybean and wheat yields in rainfed central India. To address inherent spatial variability, the study area was divided into homogeneous zones based on rainfall and soil parameters. Crop yields were correlated with a comprehensive set of driving variables at seasonal and monthly scales within each zone. Machine learning algorithms, including Random Forest Regression (RFR) and Neural Networks (NN), were employed to analyze crop yield anomalies caused by climate and weather extremes. The Sobol’ index was utilized for global sensitivity analysis to identify key parameters. Results showed significant negative correlations between thermo-meteorological parameters and yields of both monsoon soybean and winter wheat across multiple districts. Soybean yield exhibited a notable positive correlation with hydro-meteorological parameters, while wheat yield displayed a significant positive correlation with cold temperature extremes. RFR and NN demonstrated similar performance, with Root Mean Square Error (RMSE) values ranging from 0.27 to 0.39 t/ha for soybean and 0.4 to 0.6 t/ha for wheat. The Sobol’ index highlighted the high sensitivity of soybean yield to rainfall and rainy days during July and August, corresponding to the crop development and flowering stages. In contrast, wheat yield was primarily influenced by temperature extremes, particularly cold nights and hot days during the reproductive-maturity stage. These crop- and growth-stage-specific analyses of meteorological parameters are essential for devising effective strategies to adapt and mitigate climate emergencies.

Dam failure may lead to significant casualties among downstream residents. Therefore, it is crucial to study a reliable method to quantitatively assess the loss of life (LOL) caused by dam failure for emergency response to dam failure incidents. Based on a statistical analysis of typical dam failure accidents in China and the research on the formation mechanism of LOL, the study quantified the factors influencing LOL using fuzzy theory and constructed a quantitative database for the LOL. Then, it proposed an innovative algorithm integrating the grey wolf optimization (GWO) algorithm and the random forest (RF) model. Finally, a data-driven assessment model for the LOL caused by dam failure was developed by combining the gray correlation analysis of the factors. The performance of the GWO-RF model was validated using a dataset of the LOL caused. The proposed model was used to assess the LOL in typical dam failure events. The results indicate that the model has higher accuracy, with an average absolute error of approximately 945 persons, significantly lower than 2529 persons in the Graham method. Thus, it can effectively estimate the LOL caused by dam failure. This study developed a novel method for quantitatively assessing the LOL caused by dam failure, which could also serve as a reference for modeling disaster consequences in other fields.