Stochastic Environmental Research and Risk Assessment最新文献

The present study focuses on characterizing the time–frequency aspects of seasonal temperatures in India by integrating the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm with the Hilbert–Huang transform (HHT) decomposition method. The investigation also explores the connections between maximum temperature (T_max) and minimum temperature (T_min) with global climate oscillations, such as the El Nino Southern Oscillation (ENSO), Sunspot Number (SN), and Pacific Decadal Oscillations (PDO). The findings indicate that intra and inter-decadal modes play a pivotal role in influencing temperature series across various seasons, with notable changes observed in the amplitudes of inter-decadal modes for seasonal T_min and T_max. The analysis of intrinsic mode functions (IMFs) reveals that IMF2 closely align to ENSO with a periodicity of 5–7 years, IMF3 to the sunspot cycle with a frequency of approximately 11 years, and IMF5 to PDO with a long periodicity exceeding 60 years. The association between the IMF components of T_min and T_max temperature series and the three climate indices is most evident for low-frequency modes, demonstrating a consistent evolution of trend components.

Reliable drought prediction should be preceded to prevent damage from potential droughts. In this context, this study developed a hydrological drought prediction method, namely ensemble drought prediction (EDP) to reflect drought-related information under the ensemble streamflow prediction framework. After generating an ensemble of standardized runoff index by converting the ensemble of generated streamflow, the results were adopted as the prior distribution. Then, precipitation forecast and soil moisture were used to update the prior EDP. The EDP + A model included the precipitation forecast with the PDF-ratio method, and the observed soil moisture index was reflected in the former EDP and EDP + A via Bayes’ theorem, resulting in the EDP + S and EDP + AS models. Eight basins in Korea with more than 30 years of observation data were applied with the proposed methodology. As a result, the overall performance of the four EDP models yielded improved results than the climatological prediction. Moreover, reflecting soil moisture yielded improved evaluation metrics during short-term drought predictions, and in basins with larger drainage areas. Finally, the methodology presented in this study was more effective during periods with less intertemporal variabilities.

This study attempts to develop a composite index by integrating meteorological, hydrological and agricultural droughts over semi-arid Banas River basin, Rajasthan, India. To affect this, the standardized precipitation index (SPI), streamflow drought index (SDI), and vegetation condition index (VCI) have been used at 1-, 3-, 5-, 9- and 12-month time scales using station and remote sensing-based data for the period 2000 to 2020. To identify the occurrence of above-stated droughts and most vulnerable drought period at different time scales (1-, 3-, 5-, 9- and 12-month) regarding SPI, SDI and VCI has been validated with foodgrains produced and occurrence of historical drought years. This validation has been found significant with SPI-3 (r = − 0.81), SDI-3 (r = − 0.78) and VCI-5 (r = − 0.80) time scales. Subsequently, these time scales have been coalesced using weights obtained from principal component analysis (PCA) to develop the composite drought index (CDI). The annual CDI developed this way has been further validated with foodgrains produced and occurrence of historical drought years. The results of CDI demonstrate the maximum area under mild drought (73 percent) followed by moderate (21 percent) and severe (4 percent), whereas minuscule area has been detected under wet conditions (2 percent). Finally, this study suggests that individual drought types (meteorological, hydrological, agricultural) do not appropriately arrest the drought severity, hence, the usage of multiple droughts based composite index can be more realistic for effective drought assessment and monitoring in hydrologic systems.

As a leading economic center in China and an international metropolis, Shenzhen has great significance in promoting sustainable urban development. To predict its amount of domestic waste clearance, a new multivariable grey prediction model with combinatorial optimization of parameters is established in this paper. Firstly, the new model expands the value range of the order r of a grey accumulation generation operator from positive real numbers (R +) to all real numbers (R), which enlarges the optimization space of parameter and has positive significance for improving model performance. Secondly, the dynamic background-value coefficient λ is introduced into the new model to improve the smoothing effect of the nearest neighbor generated sequences. Thirdly, with the objective function of minimizing the mean absolute percentage error (MAPE), the particle swarm optimization (PSO) is employed to optimize parameters r and λ to improve the overall performance of the new model. The new model is used to simulate and predict the amount of domestic waste clearance in Shenzhen, and the MAPE of the new model is only 0.27%, which is far superior to several other similar models. Lastly, the new model is applied to predict the amount of domestic waste clearance in Shenzhen. The results indicate the amount of domestic waste clearance in 2028 could be 9.96 million tons, an increase of 20.58% compared to 2021.This highlights the significant challenge that Shenzhen faces in terms of urban domestic waste treatment. Therefore, some targeted countermeasures and suggestions have been proposed to ensure the sustainable development of Shenzhen's economy and society.

In probabilistic coastal hazard assessments based on the Joint Probability Method, historical storm data is used to build distribution models of tropical cyclone atmospheric parameters (i.e., central pressure deficit, forward velocity, radius of maximum wind, and heading direction). Recent models have used a range of assumptions regarding the dependence structure between these random variables. This research investigates the performance of a series of joint distribution models based on assumptions of parameter independence, partial-dependence (i.e., dependence between only central pressure deficit and radius of maximum wind), and full dependence (i.e., dependence between each pair of tropical cyclone atmospheric parameters). Full dependence models consider a range of copula models, such as the Gaussian copula and vine copulas that combine linear-circular copulas with Gaussian or Frank copulas. The consideration of linear-circular copulas allows for the characterization of correlation between linear variables (e.g., central pressure deficit) and circular variables (e.g., heading direction). The sensitivity of the results to different joint distribution models is assessed by comparing hazard curves at representative locations in New Orleans, LA (USA). The stability of hazard curves generated using a Gaussian copula considering variation in the selection of the zero-degree convention is also assessed. The tail dependence between large central pressure deficit and large radius of maximum wind associated with various copula models are also compared using estimated conditional probability. It is found that the linear-circular Frank vine copula model improve the stability of hazard curves and maximize tail dependence between large central pressure deficit and large radius of maximum wind. However, the meta-Gaussian copula model exhibits performance within this study region that was generally consistent with the tested vine copulas and have the advantage of being easier to implement.

Amid economic policy uncertainty, recognizing green bonds as stabilizing instruments underscores the imperative to address climate change. Existing research assesses the asymmetric effect of economic policy uncertainty on green bonds in the top 10 green bond-issuing countries (China, USA, Spain, France, Japan, Canada, Germany, the Netherlands, the UK, and Sweden). While past investigations have predominantly used panel data methodologies to probe the correlation between economic policy uncertainty and green bonds, it often overlooked the unique disparities among various economies. Contrarily, our approach utilizes the ‘Quantile-on-Quantile’ methodology, which offers a comprehensive global yet country-specific viewpoint for each economy. The study reveals a significant reduction in green bond prices associated with economic policy uncertainty across various quantile levels in most selected economies. Furthermore, our findings underscore the discrepancies in the connections among our variables across different countries. These discoveries stress that policymakers must manage thorough assessments and execute efficient tactics to manage fluctuations in economic policy uncertainty and green bonds at various levels.

Abstract

Synergizing air pollution control and carbon emission reduction has been widely proposed and highlighted. Evaluating the synergy of air pollution and carbon emissions has been the primary concern and essential support for synergistic control. Current research and works have attempted to assess synergy from multiple perspectives, but the informativeness and comprehensiveness of the synergy of air pollution and carbon emissions have been limited. This study develops a framework evaluating the synergy of PM_2.5, ozone, and CO₂ emission from the correlation and aggregate perspectives based on the large-scale and deep exploitation of the correlation and additivity of the data samples. A case study on the monthly synergy of air pollution and CO₂ emission has been performed in major Chinese urban agglomerations at the city level. The results informatively present the seasonal and city-level characteristics and heterogeneity of synergy for PM_2.5-ozone-CO₂ while providing partitioned and classified recommendations for synergistic control. A comprehensive synergy typology of synergy, bare, aggregate, and correlation for air pollution and CO₂ emission provides a reference for planning short-period synergistic control strategies.

In this article, we take a look at an Ordinary Differential Equation model that describes the bacteria’s role in anaerobic biodegradation dynamics of domestic garbage in a landfill. A nonlinear Ordinary Differential Equation system is used to describe biological activities. In the current study, the Levenberg–Marquardt Backpropagation Neural Network is used to locate alternate solutions for the model. The Runge–Kutta order four (RK-4) method is employed to produce reference solutions. Different scenarios were looked at to analyse our surrogate solution models. The reliability to verify the equilibrium of the mathematical model, physical quantities such as the half-saturation constant ((K_S)), the maximum growth rate ((mu _m)), and the inhibition constant ((K_I)), can be modified. We categorise our potential solutions into training, validation and testing groups in order to assess how well our machine learning strategy works. The advantages of the Levenberg-Marquardt Backpropagation Neural Network scheme have been shown by studies that compare statistical data based on Mean Square Error Function, efficacy, regression plots, and error histograms. From the whole process we conclude that Levenberg–Marquardt Backpropagation Neural Network is accurate and authentic.

Abstract

In regions with limited flow and catchment data needed for the configuration and calibration of hydraulic and hydrological models, employing spatial flood modeling and mapping enables authorities to predict the spatial extent and severity of floods. This study leveraged flood inventory data, coupled with various conditional variables, to formulate a novel Ensemble model. This ensemble model combined four hybridized models based on Support Vector Machine (SVM), Naïve Bayes (NB), Decision Classification Tree (DCT), and Artificial Neural Network (ANN), all of which were optimized using the metaheuristic Symbiotic Organisms Search algorithm (SOS). The precision of the flood inundation map generated by the four hybrid models and the ensemble model was assessed using standard metrics. The results demonstrated that the ensemble model outperformed other models, with an accuracy metric of 0.99 Area Under the Curve (AUC) during the training stage and 0.96 during the testing stage. This underscores the effectiveness of the ensemble approach in flood preparedness and response applications. Furthermore, a comparison was conducted, comparing the performance of the developed ensemble model against other studies within the state of West Bengal. The findings highlighted a significant improvement in the ensemble model's performance with an AUC score of 0.96 in validation compared to studies in similar areas within West Bengal with AUC score ranged from 0.73 to 0.92. In conclusion, the methodology employed in this study holds promise for application in other regions worldwide that face challenges related to limited data availability for accurate flood inundation mapping.

The objective of the study was to analyze the occurrence of forest fires in a conservation unit (CU) of the Brazilian savannah using remote sensing techniques and statistical methods developed for spatial punctual processes. To conduct the spatial analysis of fires, fire polygons mapped using Landsat 8 satellite images were used. The fires were considered into size classes to better illustrate the spatial patterns. The analysis of the spatial distribution of fires utilized Ripley's K-function, in addition to the Kcross function to verify spatial interaction. The results show that the year 2015 had the highest number of fires and burned area. Smaller fires represent a greater number of occurrences, located mostly on CU boundaries. The spatial distribution of forest fires is not random and can cluster on a scale of approximately 6 km. There is a strong spatial interaction between forest fires and traditional communities, particularly with fires smaller than 100 hectares. However, these communities are not responsible for large fires. These results contribute to better-targeted forest fire prevention and combat policies, serving as management tools for the protected area.

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