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Socio-ecological theories have long been in search of general principles to characterise anthropogenic activity-environmental change dynamics. Besides allowing for more flexible hypothesis testing, stochastic-extended IPAT and ImPACT baselines opened the door to multiple environmental applications in which solid waste generation took a growing stage. This paper surveys Waste Kuznets Curve's original foundations and underlines why the nature and shape of the hypothetical curvilinear income-waste response function tend to compare to a "black-box". It then stresses why diverging conclusions are linked to heterogeneous estimators' choices differing in their statistical assumptions and powers; whereas generic patterns hardly emerge (e.g., income elasticities of waste generation vary even when the mathematical functional form does not; population elasticities are sensitive to time-varying data and income groups). Next, we identify persisting biases of endogeneity which threaten the internal validity of WKC conclusions, if uncontrolled for (e.g., simultaneity, waste measurement errors and garbage policy confounding effect); along with other identification problems including within-panel heterogeneity with systematic slope variations and cross-sectional and spatially dependent income series. Although we propose a set of theoretically justified instrumental variables to exogenously predict income levels and ensure unbiased elasticities, we also detect and underline that additional threats to external invalidity do play out in practice (e.g., asymmetric geographical coverage and bias of case study selection due to environmental data constraints; missing policy-realm; within-waste heterogeneity hidden by widely aggregated indicators; and a non-systematic treatment of the technological effect). All prevent the waste literature from converging and should be considered by future empirical assessments. [ABSTRACT FROM AUTHOR]

Rapid urbanization has increased the demand for more urban water resources, and water scarcity has limited the sustainable development of cities and regions. In this paper, all prefecture-level cities in Shandong Province in the lower Yellow River region are studied, an evaluation index system is established based on the water poverty index, and the improved entropy value method is applied to determine the index weights. At the same time, this paper explores the indicator correlations using Pearson correlation analysis and further explores the main obstacle factors for improving water security. This study intends to assess the current situation of water security in Shandong Province and provide suggestions for the sustainable improvement of water resources and sustainable economic and social development in Shandong Province. The results show that (1) from 2000 to 2020, the water poverty index at all levels in Shandong Province showed an upward trend, and the overall water security situation in the province improved significantly, with the overall five subsystem values of the WPI system climbing significantly. (2) Water supply pipeline density and landscape area ratio are strongly positively correlated. The centralized sewage treatment rate is strongly negatively correlated with the water consumption of 10,000 Yuan GDP and the water consumption of 10,000 Yuan industrial added value. (3) The low percentage of ecological water usage is the primary barrier preventing WPI improvement, according to calculations using the barrier level model. In addition, the high water consumption of GDP of 10,000 Yuan and industrial value added of 10,000 Yuan are factors affecting urban water security. The area should work to increase the rate at which its water resources are utilized, particularly the ratio at which industrial water is used repeatedly. It ought to give ecological water compensation greater consideration and utilize more ecological water overall. [ABSTRACT FROM AUTHOR]

By reducing fossil fuel use, renewable energy improves the economy, quality of life, and environment. These impacts make renewable energy forecasting crucial for lowering fossil fuel utilization. This paper aims to mathematically improve time series forecasting literature by focusing on solar irradiance applications in Los Angeles, Denver, and Hawaii solar irradiance sites. A three-phased time series forecasting hybrid method is devised for this endeavor. The ARFIMA is used to forecast the original solar irradiance time series in phase I. Next, the dataset's residuals, are retrieved by subtracting the phase I results from the observed time series to prepare the scenario for the following phase. A novel enhanced fractional Brownian motion is used for residual forecasting in phase II. The parameter estimation in phase II is implemented adaptively to capture the dynamic statistical characteristics of the time series efficiently. Finally, the phases I and II results are numerically conglomerated to form the final forecasting results in phase III. The residual forecasting part, in phase II, reveals a substantial superiority. Also, when comparing the proposed hybrid algorithm results to other existing cutting-edge algorithms applied to the same solar irradiance applications, the output demonstrates that the suggested algorithm has a significantly improved performance. [ABSTRACT FROM AUTHOR]

Increased risks of economic policy uncertainty and overexploitation of natural resources exist in China. At the same time, the growth rate of urban residents' consumption has generally declined. The paper analyses the role of economic policy uncertainty (EPU) and natural resource exploitation on the urban residents' consumption in China. Based on the data from the first quarter of 2002 to the third quarter of 2021, the paper uses a nonlinear autoregressive distributed lag model to verify the asymmetric effects. Then the paper constructs a time-varying parameter vector autoregressive model with stochastic volatility term to analyze the nonlinear responses. Impulse response analysis was used to further explain the relationship between the three. The negative impact of rising EPU on urban residents' consumption is larger than its reduction. Negative shocks to natural resource development increase the urban residents' consumption. Positive shocks reduce the urban residents' consumption. There is a time-varying non-linear effect of EPU and natural resource development on urban residents' consumption in China. The negative impact of EPU on urban consumption has been further exacerbated by major crises such as the financial crisis, COVID-19 and the post-crisis period. The negative impact of natural resource development diminished after the government introduced industrial upgrading policies and environmental regulations. This study provides constructive suggestions for the optimization of economic policies and the improvement of urban consumption. This study also enriches consumer theory and provides new evidence for the resource curse hypothesis. [ABSTRACT FROM AUTHOR]

Air quality prediction technology can provide effective technical means for environmental governance. In recent years, due to the strong nonlinearity of data, there has been extensive research on data analysis and preprocessing techniques. This paper aims to comprehensively summarize and analyze the methods used in air quality forecasting, specifically focusing on four categories: data decomposition, dimensionality reduction, data correction, and spatial interpolation. Each method's purpose, characteristics, improvements, and implementation details are described in detail. The evaluation of data preprocessing methods is based on popularity, accuracy improvements, time consumption, maturity, and implementation difficulty. Among the existing methods, data decomposition and feature selection are commonly used and well-developed. However, outlier detection and spatial interpolation have limited applications and require further research. Furthermore, this paper discusses current challenges in applying these methods and future development trends, providing a valuable reference for future research. [ABSTRACT FROM AUTHOR]

The streamflow process is an crucial information resource for the joint optimal operation of reservoirs. As the length and representativeness of historical streamflow samples are insufficient for practice projects, streamflow stochastic generation approaches are usually used to expand the streamflow series. For the joint operation and management of the multi-reservoir system, the multisite streamflow stochastic generation (MSSG) with high-dimensional temporal-spatial correlation poses a challenge. This paper develops the generative adversarial network as a novel MSSG model. In contrast to the existing literature on MSSG, which solely focuses on a specific case study and provides a comparatively one-sided assessment, this paper evaluates multiple characteristics of streamflow at various time scales from three MSSG models in two instances. Specifically, three MSSG models, namely the seasonal autoregression (SAR) model coupled with the master station method, the Copula model coupled with the master station method, and the deep convolutions generative adversarial network (DCGAN) model, are employed to generate monthly, ten-daily, and daily streamflow series of the two-reservoir and eight-reservoir systems. This study aims to examine the performance of three models and provide recommendations for implementing MSSG approaches in practice. Results show that: (1) the priority should be given to the maximum iterations on the DCGAN model at a large time scale, while at a smaller time scale, the training of the model is directly linked to the setting of batch size; (2) the Copula model is capable for better retaining statistical characteristics of streamflow series for similarity; (3) the SAR model excels in simulating the extremes of streamflow; and (4) the DCGAN model possesses a significant advantage in capturing the temporal-spatial higher-order correlation, especially in systems comprising more than two reservoirs and with small time scales (e.g., daily streamflow). Furthermore, this study presents comprehensive and multi-scale recommendations for selecting MSSG approaches, thereby providing a theoretical foundation and practical value for MSSG in diverse scenarios. [ABSTRACT FROM AUTHOR]

Positive semidefinite kernels on spheres cross time are on demand in spatial statistics, machine learning and numerical analysis, motivated by applications in the climate, environmental and earth sciences. Most of the recent literature has focused on kernels that are isotropic on the sphere and stationary in time, but this assumption is often overly limited in quantifying spatial dependence properly. A broader class of kernels can be obtained by trading isotropy for axial symmetry, i.e., stationarity with respect to longitudes only. This paper challenges theoretical aspects by providing a spectral representation that uniquely characterizes space-time positive semidefinite kernels that are axially symmetric on the sphere and stationary in time. We also address the problem of partial Fourier inversion and show that it is feasible under mild technical conditions. The second part of the paper focuses on constructive methods to build nonseparable kernels having axial symmetry on the sphere and stationarity over time. [ABSTRACT FROM AUTHOR]

In the field of landscape ecology, the distribution of different kinds of landscape, determined the resistance in ecological processes such as species migration and energy flow. Various connection ways among them would establish diverse corridors with different resistance. Thus, it was important to choose scientific ecological network with consider of minimum resistance. Based on the land use data in 2010 and 2020, the paper firstly analyzed the spatiotemporal distribution characteristics of greenspace in Jinan, then assessed the influence of each natural and socio-economic factor on the change of greenspace by GeoDetector. The weights of each resistance factor in the ecological process were acquired based on the results of factor detector. Finally, the paper constructed an ecological network of greenspace in Jinan by the minimum cumulative resistance (MCR) method, which would provide scientific basis for decision support of urban plan and management. [ABSTRACT FROM AUTHOR]

The paper presents a specific model used to assess and select the method of heavy metal leaching from waste materials. A recommendation for the rational selection of the leaching method was made, taking into account the crucial parameters affecting the leaching process derived from the authors' research experience. The assessment models were supplemented with characteristics relating to the cost and time required to perform the tests. An approach was developed that links the weights of the assessment criteria with the degree of environmental risk from a particular heavy metal and waste type. Using multiple-criteria decision analysis, four models were developed for the assessment and selection of a test method for the leaching of heavy metals from waste. The difference is in the way the weights of the assessment criteria depend on the potential threat of the waste to the environment. A key element of the proposed approach is assigning the weights of the criterion to the toxicity of the heavy metal, the type of waste, and their management method. The results obtained prove that the current practice of only employing the batch leaching test is inappropriate. The choice of method should be justified in terms of environmental safety as well as time- and cost-effectiveness. The results of the paper show that the proposed model can be used in practice and be a recommendation for choosing the method of leaching heavy metals from waste and their further treatment, following the principles of sustainable waste management. [ABSTRACT FROM AUTHOR]

The adequate choice of a distribution that can fit a dataset, especially to its right tail (large extreme events), is a major problem in flood frequency analysis. Decision support systems (DSS) have been used in the past to define the appropriate class of distribution based on the tail behaviour of the data before its model selection. This paper investigates the tail behaviour of probability distribution of the daily streamflow data in south Indian rivers and also assesses the information related to tail risk, as it has many practical and societal consequences. In this paper, we apply and compare two DSS, (i) given by Martel et al. (J Hydrol Eng 18(1):1–9, 2013) and (ii) concentration profile–concentration adjusted expected shortfall (CP–CAES) based DSS, along with some newly developed graphical diagnostic tools, such as CP, CAES, discriminant moment ratio plot, maximum-to-sum plot, and Zenga plot to characterize the tails of probability distributions into an appropriate class. Further, the tail risk is analyzed using a novel risk management approach known as a concentration map (CM), which makes use of the concentration profiles of daily streamflow datasets. Results indicate that the proposed DSS is a potential tool for tail characterization. The study suggests the use of heavy-tailed distributions to model daily streamflow data over south Indian catchments. Neglecting heavy-tailed distributions, when found appropriate, can lead to an underestimation of the likelihood of floods and has catastrophic consequences for risk. CM is found suitable for assessing the tail risk associated with the daily streamflow dataset, which inherently represents the frequency and magnitude of extreme floods. [ABSTRACT FROM AUTHOR]

The design flood for a coastal basin based on model simulations can be impacted by various conditions other than design storm, such as tide level. However, the design storm and tide level are usually investigated separately, and the relation between them has not been sufficiently studied, which may have unexpected impacts on model simulations. This paper presents a bivariate assessment framework that aims to evaluate the coordination between design storm and tide level using a copula-based joint distribution to derive the conditional probability of coincidences between design storm and tide level intervals. We apply this framework to the Tai Lake Basin (TLB) in the eastern coast of China and investigate what tide level along its coastline is appropriate for guiding flood control planning. Our findings reveal that the currently-used tide level along the southern coast of TLB is too low and should be raised by approximately 0.35 m and 0.12 m under maximum 1-day and 3-day storms, respectively. Meanwhile, the tide levels for the north and east coasts are appropriate. This paper provides a comprehensive understanding of the interplay between design storm and tide level in a large coastal basin, offering insights into the design of flood protection systems. [ABSTRACT FROM AUTHOR]

The recently developed regula-falsi profile likelihood (RF-PL) method has potential for quantifying uncertainty in nonstationary hydrological frequency analysis. However, its applicability to diverse distributions is constrained and lacks comprehensive evaluation. This paper extends the RF-PL method to multiple distributions (Gumbel, Generalized Logistic (GLO), Generalized Normal (GNO), Log-Normal, Log-Pearson type III, and Pearson type III (PE3)) by introducing their reparametrized log-likelihood functions. The extended multi-distribution RF-PL (MD-RF-PL) method is systematically assessed in a simulation study and practical applications, covering diverse nonstationary scenarios, distributions, L-skewness ( τ 3 ), and return periods, and compared with the bootstrap and/or the conventional PL as benchmarks. The findings indicate that the MD-RF-PL method is computationally comparable to the bootstrap method but overall superior in capturing the true quantiles with reasonably wide confidence intervals when τ 3 = 0.1 (moderate tails) and 0.3 (heavy tails). However, when τ 3 = 0.5 (very heavy tails), although the MD-RF-PL method is superior and roughly equivalent to the bootstrap method for GEV and GLO distributions, respectively, it is inferior for GNO and PE3 distributions, albeit avoiding the occasionally extremely wide confidence intervals of the bootstrap method. This low performance of the MD-RF-PL method is associated with numerical instability. Moreover, the MD-RF-PL method reduces the computational demand of the PL method by 94–99% without degrading its accuracy. Both the simulation study and the practical applications consistently support the preference of the MD-RF-PL method for distributions with moderate to heavy tails and highlight the need for improving its numerical stability for very heavy tails. [ABSTRACT FROM AUTHOR]

This paper proposes a generalization of the local bootstrap for periodogram statistics when weakly stationary time series are contaminated by additive outliers. To achieve robustness, we suggest replacing the classical version of the periodogram with the M-periodogram in the local bootstrap procedure. The robust bootstrap periodogram is implemented in the Whittle estimator to obtain confidence intervals for the parameters of a time series model. A finite sample size investigation was conducted to compare the performance of the classical local bootstrap with the one proposed in this paper to estimate 95% confidence intervals for the parameters of autoregressive and seasonal autoregressive time series. The results have shown that the robust estimator is resistant to additive outlier contamination and produces confidence intervals with coverage percentages closer to 95% and lower amplitudes than the ones obtained with the classical estimator, even for small percentages and magnitudes of outliers. It was also empirically observed that when the expected number of outliers is kept constant, the coverage percentages of the confidence intervals of the robust estimators tend to 95% as the sample size increases. An application to the daily mean concentration of particulate matter with a diameter smaller than 10 μ m (PM 10 ) was considered to illustrate the methodologies in a real data context. All the results presented here strongly motivate using the proposed robust methodology in practical situations where additive outliers contaminate weakly stationary time series. [ABSTRACT FROM AUTHOR]

Changes in ocean temperature over time have important implications for marine ecosystems and global climate change. With the development of artificial intelligence, research on ocean temperature prediction has made some progress. However, existing methods are mostly limited to temperature prediction of isolated points on ocean surface, with less vertical studies. And existing graph neural network-based methods typically use predefined graphs, which cannot adaptively capture unknown associations between data. In this paper, we propose a new adaptive spatiotemporal dynamic graph convolution network to predict three-dimensional sea water temperature. Combined with adaptive graph learning and k nearest neighbor clustering methods, the network can automatically mine unknown dependencies between sequences based on raw data without any prior knowledge. Temporal and spatial dependencies in time series are further captured using temporal convolution and graph convolution. The graph learning, graph convolution, and temporal convolution modules are jointly learned in an end-to-end framework for the three-dimensional seawater temperature prediction task. In this paper, the prediction experiment is carried out using the high-resolution three-dimensional temperature and salt datasets from the Copernicus Global Ocean Physics Reanalysis. The results showed that our method achieved the best predictive performance on all prediction scales compared to current mainstream methods, with MAE increasing by an average of 21.7% and RMSE of 23.3%. [ABSTRACT FROM AUTHOR]

100 years after the Spanish flu, the COVID-19 crisis showed that large-scale epidemics and pandemics do not belong to the past. On the report of the World Health Organization, COVID-19 is the most significant public health problem of the twenty-first century. Like previous epidemics, the current crisis is accompanied by uncertainty, mistrust, doubt and fear, and this has led to an infodemic connection to the epidemic. So not only are we fighting an epidemic, but also, we are brawling an infodemic. To reduce the social and economic consequences and harmful effects of infodemic health, and to overcome it, we need to implement strategies against infodemic. Evaluating strategies based on multiple characteristics can be considered multi-criteria decision-making (MCDM) problem. According to the literature, there is no study that aims on proposing an integrated approach to evaluate infodemic management strategies under uncertain environment. Therefore, in this paper, an integrated framework based on the extended version of best–worst method (BWM) and Combined Compromise Solution (CoCoSo) methods under a spherical fuzzy set (SFS) is developed for the first time to address the COVID-19 infodemic management strategies selection. Initially, the criteria are weighted using the developed SFS BWM which reduces uncertainty in pairwise comparisons. In the next step, the 15 selected strategies are analyzed and ranked using SFS CoCoSo. The outputs of this paper illustrate that online tools for fact checking COVID-19 information and engage and empower communities are placed in the first and second priorities, respectively. The comparison of ranking results SFS-CoCoSo with other MCDM methods demonstrates the performance of the proposed approach and its ranking stability. [ABSTRACT FROM AUTHOR]

In the literature classical properties concerning the class of covariance functions are well illustrated. A recent analysis has provided the conditions under which the difference between two covariance functions is still a covariance function. In this paper the properties of these new classes of models have been explored; in particular, the present analysis has been given for isotropic covariance functions, because of their importance in many applied areas; moreover, isotropic covariance functions can be considered the starting point to construct anisotropic models. It has been pointed out that these new families of models are more flexible than the traditional ones because the same models, according to the values of their parameters, are able to select covariance functions which are always positive in their domain, as well as covariance functions which could be negative in a subset of their field of definition. Moreover, within the same class of models, it is possible to select covariance models which present a parabolic behaviour near the origin from covariance models which present a linear behaviour in proximity of the origin. Apart from the theoretical importance related to the new aspects presented throughout the paper, it is relevant to underline the practical aspects, since these new classes of isotropic covariance models are characterized by an extremely simple formalism and can be easily adapted to several case studies, hence they result very useful for many practitioners. [ABSTRACT FROM AUTHOR]

Accurate reference evapotranspiration (ET0) estimation has an effective role in reducing water losses and raising the efficiency of irrigation water management. The complicated nature of the evapotranspiration process is illustrated in the amount of meteorological variables required to estimate ET0. Incomplete meteorological data is the most significant challenge that confronts ET0 estimation. For this reason, different machine learning techniques have been employed to predict ET0, but the complicated structures and architectures of many of them make ET0 estimation very difficult. For these challenges, ensemble learning techniques are frequently employed for estimating ET0, particularly when there is a shortage of meteorological data. This paper introduces a powerful super learner ensemble technique for ET0 estimation, where four machine learning models: Extra Tree Regressor, Support Vector Regressor, K-Nearest Neighbor and AdaBoost Regression represent the base learners and their outcomes used as training data for the meta learner. Overcoming the overfitting problem that affects most other ensemble methods is a significant advantage of this cross-validation theory-based approach. Super learner performances were compared with the base learners for their forecasting capabilities through different statistical standards, where the results revealed that the super learner has better accuracy than the base learners, where different combinations of variables have been used whereas Coefficient of Determination (R2) ranged from 0.9279 to 0.9994 and Mean Squared Error (MSE) ranged from 0.0026 to 0.3289 mm/day but for the base learners R2 ranged from 0.5592 to 0.9977, and MSE ranged from 0.0896 to 2.0118 mm/day therefore, super learner is highly recommended for ET0 prediction with limited meteorological data. [ABSTRACT FROM AUTHOR]

The Yangtze River Economic Belt (YREB) is an important strategic area in China. However, frequent extreme precipitation events have caused great economic losses and human casualties in this region. In this article, we explore the spatial and temporal links between extreme precipitation events and Sunspot Number (SSN), El Niño Southern Oscillation (ENSO), Arctic Oscillation (AO) and Pacific Decadal Oscillation (PDO) in this important economic belt. According to the research findings, all of the extreme precipitation indices (EPIs) except for consecutive dry days (CDD) and consecutive wet days (CWD) showed an upward trend in the YREB over the 59 years. The spatial distributions of very wet days (R95p), extremely wet days (R99p), max 1-day precipitation amount (Rx1day) and max 5-day precipitation amount (Rx5day) had similar distribution patterns, showing decreasing trends from east to west. The EPIs generally had a 2–4-year band, suggesting stronger and more elusive changes. The wavelet coherence (WTC) spectra suggested that SSN, ENSO, AO, and PDO have different effects on extreme precipitation events during different time periods. Before 1985, the SSN, ENSO, AO, PDO and extreme precipitation events shared similar oscillation periods, but after 1985, their oscillation periods were no longer consistent with each other. In addition, solar activity and the AO mainly had negative correlations with extreme precipitation events, while the ENSO and PDO had predominantly positive correlations with the EPIs. This paper provides a reference for national economic strategic planning and natural resource management in the YREB. [ABSTRACT FROM AUTHOR]

Post-processing using deep learning algorithms can be conducted to improve accuracy of hydrologic predictions and quantify their uncertainty. In this paper, a revised version of the Local uncertainty estimation model (LUEM-R) and the circular block bootstrap (CBB) method have been used to improve the accuracy of the Variable Infiltration Capacity's (VIC) streamflow simulation and quantify its uncertainty. We used the simulated and observed streamflow at the gauge located in North CAPE Fear basin, USA from the Dayflow dataset. In the LUEM-R method, a combination of Gaussian Mixture Model and Long Short-Term Memory (LSTM) networks, which are able to capture the dependencies in time series, were used to construct the upper and lower prediction limits (PLs) for the 90% confidence level. In the CBB method, a circular block resampling technique was used to account for the dependencies in the time series (CBB-LSTM). The improved streamflow, calculated as the mean of the LSTM outputs for 200 bootstrap realizations, showed a very high correlation with observations, with coefficient of determination values of 0.97 and 0.87 for the training and testing periods, compared to the 0.77 and 0.74 values for the initial VIC simulations. For the CBB-LSTM method, the 90% PLs were constructed by fitting the best distribution at each time step, accordingly. The PLs bracketed 90% and 70% of observations in the training and testing periods, while being significantly narrower than the LUEM-R bands, which contained 92 and 91 percent of observations in the training and testing periods. Ordinary bootstrapping was also conducted using the Random Forest model (OB-RF). Comparison of the results indicates the superiority of CBB-LSTM method to the OB-RF in improving the accuracy and quantifying the uncertainties in hydrological model simulations. Overall, the CBB-LSTM was successful in improving the deterministic accuracy of VIC model simulations and quantifying its uncertainty. Also, the LUEM-R method could be efficiently utilized in quantification of uncertainty of VIC model simulations. [ABSTRACT FROM AUTHOR]

Accurate spatial estimation of extremes is an increasingly important topic in environmental research and risk assessment. Conditional bias (CB)-penalized kriging (CBPK) improves such estimation by minimizing linearly weighted sum of error variance and variance of Type-II error. However, CBPK requires skillful prescription of the weight for the CB penalty which is a significant challenge in practice. In this paper, we describe an extension of CBPK, referred to herein as adaptive conditional bias-penalized kriging (ACBPK), which objectively prescribes the weight for improved estimation of extremes without deteriorating performance in the unconditional mean squared error sense. For comparative evaluation in the real world, cross validation experiments were carried out for precipitation estimation using hourly rain gauge data in the Arkansas-Red River Basin (AB), central Texas (TX) and southeastern US (SE) areas. The results show that CB is detected for about 26, 24 and 25% of all data points in the AB, TX and SE cases, respectively, and that, given detection of CB, ACBPK reduces root mean square error of hourly precipitation exceeding 12.7 mm by 15, 21 and 9% and hourly precipitation exceeding 25.4 mm by 14, 26 and 10% relative to ordinary kriging (OK) for the AB, TX and SE cases, respectively. The overall findings indicate that, if accurate spatial estimation in the tails of the distribution is important or accurate modeling of spatiotemporally-varying correlation structure is a challenge, ACBPK should be favored over OK. [ABSTRACT FROM AUTHOR]

The shear-wave velocity V S a crucial parameter for determining small-strain soil stiffness characteristics and site classification. However, directly measuring V S in the field can be challenging, and requires specific equipment. As a result, researchers have conducted numerous studies on V S correlation, and extensive research has demonstrated that the results from cone penetration test (CPT) and standard penetration test (SPT) data are strongly related to the shear-wave velocity. Due to the uncertainty of the transformation model, the accuracy of the V S derived from the empirical equations are unsatisfactory. The purpose of the present paper is to propose a Bayesian framework for determining the probabilistic characteristics of V S while considering the transformation uncertainty. The Bayesian framework considers both the in-situ test data (SPT, CPT) and prior information, and the results show that the framework considering two in-situ tests accurately predicts the shear-wave velocity. There are several advantages of using the Bayesian method described in this study: (1) The Bayesian framework incorporates both the inherent uncertainty of the shear-wave velocity and the transformation uncertainty. (2) Prior information and field data can be combined to improve the accuracy of predictions. (3) In the framework, the statistical characteristics of V S can be ascertained from small samples of field test data. [ABSTRACT FROM AUTHOR]

The complex topology of river networks and the numerous factors influencing streamflow make it challenging to forecast streamflow in large river basins. Improving the accuracy of streamflow forecasting in these basins is of great importance for water resource planning and management. In this paper, considering the spatial variability of impacts of human activities on streamflow, a streamflow forecasting method with a hybrid physical process-mathematical statistic is proposed to obtain better results by realizing the complementary advantages of conventional methods and the streamflow into Hongze Lake in China is forecasted by the proposed method. Firstly, the physical process-based Soil and Water Assessment Tool (SWAT) is used to forecast the streamflow of tributaries with few water storage projects such as reservoirs in the catchment area. Secondly, considering the correlation between mainstream streamflow and tributaries streamflow as well as the correlation between mainstream streamflow and its previous streamflow, streamflow forecasting models based on the vine copula function are constructed to forecast the mainstream streamflow and its confidence intervals, where there are many water storage projects in the catchment area. Then, the total streamflow into Hongze Lake and its confidence intervals are obtained by coupling the outputs of the above two parts. Finally, statistical indicators are chosen to evaluate the forecasting effects in terms of the credibility of deterministic forecasting as well as the reliability and acuity of probabilistic forecasting. The results demonstrate that the proposed method outperforms existing SWAT and long and short-term memory (LSTM) neural network methods in terms of forecasting performance. Consequently, it presents an effective alternative for addressing complex hydrological forecasting tasks in large river basins. [ABSTRACT FROM AUTHOR]

Several classes of space-time correlation models have been proposed by various authors in the last years. However, most of these families utilize non negative covariance functions to be adapted to different case studies: indeed, the traditional classes of covariances, such as the Whittle–Matern class and the several families constructed by applying the classical properties are not so flexible to describe covariance functions characterized by negative values. A recent analysis, regarding the difference between two isotropic covariance functions, has underlined that these new families of models are more flexible than the traditional ones because the same models are able to select covariance functions which are always positive in their domain, as well as covariance functions which could be negative in a subset of their field of definition. Moreover, within the same class of models, it is possible to select covariance models which present different behaviours in proximity of the origin. In this paper several families of isotropic space-time covariance functions, among the ones proposed in the literature, have been reviewed in order to enrich the same families including models characterized by negative values in a subset of their domain. Furthermore, the definition of separability has been revised in order to enlarge the classical definition. Apart from the theoretical importance related to the new aspects, these new classes of covariance models are characterized by an extremely simple formalism and can be easily adapted to several case studies. [ABSTRACT FROM AUTHOR]

The modeling of sub-daily extreme rainfall has long constituted a challenge for hydrologists in view of the limited availability of data, both in time and space. In this context, the scale invariance principle, which formally links precipitation intensities across a range of time scales under a rigorous theoretical underpin, comprises a useful and parsimonious tool for deriving probabilistic models for the referred stochastic variables. This paper aims at reviewing the history of application of the scale invariance principle to the marginal distributions of intense precipitation, as well as presenting recent developments in this field. We first address the basic concepts and the mathematical formalism of scale invariant models, discussing distinct scaling regimes across durations and estimation procedures. Next, we present a comprehensive review on at-site and regional stationary scaling models applied in several regions of the world, with focus on their underlying assumptions and main limitations. Finally, we address extensions of the rationale for nonstationary models as a means of accommodating potential effects of climate change in extreme short-duration rainfall. While discussing each of these frameworks, we indicate potential research gaps and modeling developments which could further advance the understanding of scaling behavior of extreme precipitation and improve statistical inference. Hence, this review may constitute a useful guide for practitioners and motivate future research in the modeling of short-duration extreme rainfall. [ABSTRACT FROM AUTHOR]

Recently, several papers have been published regarding the use of preprocessing models, such as Discrete Wavelet, in Data-Driven Forecasting Frameworks (DDFF). However, these models face unresolved issues, including the use of future data, boundary-affected data, and incorrect selection of decomposition level and wavelet filter, which can lead to inaccurate results. In contrast, the Wavelet-based Data-Driven Forecasting Framework (WDDFF) overcomes these problems. To address the first two issues, we can use Maximal Overlap Discrete Wavelet Transform (MODWT) and a-trous algorithm (AT). Although there is currently no definitive solution for selecting the decomposition level and wavelet filter, we propose a novel approach using Entropy to address these issues. By utilizing the concept of predictability of time series using entropy, we can determine the optimal decomposition level and suitable filter to develop the Maximal Overlap Discrete Wavelet-Entropy Transform (MODWET) and apply it to WDDFF accurately. This study, demonstrates the effectiveness of MODWET through three real-world case studies on the CAMELS data set. In these studies, we will forecast the streamflow of specific stations one month ahead to prove the effectiveness of using preprocessing algorithms for forecasting models. The proposed model combines Input Variable Selection (IVS), preprocessing model, and Data-Driven Model (DDM). We will conclude that MODWET-ANN is the most effective model and highlight how entropy can accurately identify the optimal decomposition level and filter, resolving the concerns associated with using WDDFF in hydrological forecasting problems. [ABSTRACT FROM AUTHOR]

Partitioning algorithms, and in particular K-means clustering, are widely used in time series analysis. K-means clustering is intrinsically related to the use of the Euclidean distance as a measure of dissimilarity. When other dissimilarity measures, such as dynamic time warping, are involved, K-means clustering is usually replaced by the optimisation of a sums-of-the-stars clustering criterion, giving rise to an algorithm other than that of K-means, such as K-medoids. Another common restriction in the implementation of K-means concerns the need to estimate the average as the cluster prototype, which may represent a drawback for this method in time series when elastic measures such as dynamic time warping are used. In this paper, we propose a multidimensional scaling based K-means clustering algorithm that enables the use of K-means clustering together with any dissimilarity measure, and in particular with dynamic time warping, without requiring us to estimate cluster prototypes for the time series. This procedure is a true K-means clustering algorithm that searches for the partition in an equivalent auxiliary configuration, usually in a dimension lower than the time series length. The approach proposed is of particular interest when dynamic time warping is used in the analysis of series of unequal length and/or when some data are missing, and hence Euclidean distances cannot be used. The performance of our procedure is tested by conducting an extensive Monte Carlo experiment, comparing the results with those obtained by K-medoids. The procedure is also illustrated with the analysis of carbon dioxide emissions from 133 countries. [ABSTRACT FROM AUTHOR]

This paper proposes a risk assessment methodology for evaluating the surface water quality of hydrological basins based on physico-chemical parameter concentrations. Considering the Douro River basin in Portugal and monthly recorded dissolved oxygen and conductivity parameter measurements in 18 water sampling stations from January 2002 to December 2013, the work intends to answer the research question of identifying the riskiest periods for water pollution in the year and classifying the water sampling stations in terms of risk for water pollution. The methodology consists first in determining the pollution risk implied by the physico-chemical parameters, based on the monthly water station measurements, using six different risk measures, namely mean, variance, loss probability, entropy, mean excess loss and value at risk. The risk values are ordered according to each risk measure and a final ranking is established through a ranking aggregation method. The final ranking permitted identifying the high risk period as ranging from May to October and the low risk period from November to April. Furthermore, July was classified as riskiest month concerning the dissolved oxygen concentration, and August as riskiest month regarding the conductivity levels. On the other hand, the ranking allowed classifying the water sampling stations, previously grouped in clusters, in terms of similar risk for water pollution: six sampling stations in the west of the basin formed the riskiest cluster in the dry period considering the dissolved oxygen concentrations, and four of those stations formed also the riskiest cluster concerning the conductivity levels. [ABSTRACT FROM AUTHOR]

Urban flood risk management has been a hot issue worldwide due to the increased frequency and severity of floods occurring in cities. In this paper, an innovative modelling approach based on the Bayesian convolutional neural network (BCNN) was proposed to simulate the urban flood inundation, and to provide a reliable prediction of specific water depth. To develop the model, a series of historical rainfall data during the last 20 years were collected in Rushan China and the responding flood events were reproduced using physically based hydraulic model. The flood condition factors used in modeling include spacial factors and precipitation factors. The results showed that the BCNN model not only inherits the powerful ability of aggregating spacial information from CNNs to perform high level of accuracy and computational efficiency in predicting 2D urban flood inundation maps, but also offers a measure of uncertainty in the form of predictive variance, providing insights into the confidence and reliability of its predictions. The proposed BCNN method offered a new perspective for the analysis of surrogate model regarding real-time forecasting of flood inundation. [ABSTRACT FROM AUTHOR]

Given the critical role of the clean energy market in the global economy and environmental sustainability, this paper investigates the impact of the U.S. Business Conditions Index (ADS) on the risk of segmented clean energy markets across different time scales and market conditions, as well as its spillover mechanisms. By using wavelet coherence and wavelet quantile analysis, we examine how the Aruoba–Diebold–Scotti (ADS) Business Conditions Index affects the risk levels of segmented clean energy indices under varying market conditions. To further understand this impact mechanism, we also employ the quantile Granger causality test to analyze the spillover effects of ADS on the clean energy market. The results show that the ADS index significantly influences the risk levels of segmented clean energy markets, with notable differences across various time scales and market conditions. The contributions of this study include: (1) segmenting the measurement of clean energy market risk into the Solar Index (SOLAR), Renewable Energy Index (RE), Biomass Index (BIO), Wind Energy Index (WIND), and Clean Energy Index (WILDER); (2) providing new evidence on the impact of the ADS Business Conditions Index on segmented clean energy market risk; and (3) offering new perspectives for different clean energy market participants to better navigate complex business environments and develop effective risk management strategies. [ABSTRACT FROM AUTHOR]

An urban water demand with high accuracy and reliability plays fundamental role in creating a predictive water supply system. This is necessary to implement mechanisms and systems that can be used to estimate water demands. The paper aims to forecast the amounts of clean water given to metropolis with single multiplicative neuron model artificial neural network. For this purpose, a regular data set of Istanbul metropolis was selected as a model and utilised in the application process. Single multiplicative neuron model artificial neural network is a neural network that does not have the hidden layer unit number problem that many shallow and deep artificial neural networks have. In this study, the black hole optimization algorithm is used for the first time in the literature for the training of the single multiplicative neuron model artificial neural network. In line with the analysis results, it is concluded that the proposed new approach achieved better prediction results than the other compared methods for the time series of amounts of clean water given to metropolis. [ABSTRACT FROM AUTHOR]

Accurate measurement and inference of phase difference between two time series are critical across several fields, including signal processing, economic dynamics, and air pollution research. Wavelet methods offer advantages over traditional approaches by allowing time–frequency localization and adaptability to non-stationary signals, which makes them widely used for phase difference estimation. However, existing methods do not provide a statistical test to determine whether a measured phase difference reflects a true underlying relationship between the signals or is merely an artifact of measurement errors or randomness. In this paper, we propose a bootstrap method to fill this gap. Our method is particularly suited to the analysis of non-standard data distributions and complex temporal dependencies. Extensive simulations demonstrate its desirable power and control of type-I error. Furthermore, we apply the method to study air pollution dispersion in China and elucidate the factors influencing phase differences. [ABSTRACT FROM AUTHOR]

In the field of groundwater, accurate delineation of contaminant plumes is critical for designing effective remediation strategies. Typically, this identification poses a challenge as it involves solving an inverse problem with limited concentration data available. To improve the understanding of contaminant behavior within aquifers, hydrogeophysics emerges as a powerful tool by enabling the combination of non-invasive geophysical techniques (i.e., electrical resistivity tomography—ERT) and hydrological variables. This paper investigates the potential of the Ensemble Smoother with Multiple Data Assimilation method to address the inverse problem at hand by simultaneously assimilating observed ERT data and scattered concentration values from monitoring wells. A novelty aspect is the integration of a Convolutional Neural Network (CNN) to replace and expedite the expensive geophysical forward model. The proposed approach is applied to a synthetic case study, simulating a tracer test in an unconfined aquifer. Five scenarios are compared, allowing to explore the effects of combining multiple data sources and their abundance. The outcomes highlight the efficacy of the proposed approach in estimating the spatial distribution of a concentration plume. Notably, the scenario integrating apparent resistivity with concentration values emerges as the most promising, as long as there are enough concentration data. This underlines the importance of adopting a comprehensive approach to tracer plume mapping by leveraging different types of information. Additionally, a comparison was conducted between the inverse procedure solved using the full geophysical forward model and the CNN model, showcasing comparable performance in terms of results, but with a significant acceleration in computational time. [ABSTRACT FROM AUTHOR]

In this paper we present a framework to aid in the selection of optimal environmental indicators for detecting and mapping extreme events and analyzing trends in heatwaves, meteorological and hydrological droughts, floods, and their compound occurrence. The framework uses temperature, precipitation, river discharge, and derived climate indices to characterize the spatial distribution of hazard intensity, frequency, duration, co-occurrence, and dependence. The relevant climate indices applied are Standardized Precipitation Index, Standardized Precipitation and Evapotranspiration Index (SPEI), Standardized Streamflow Index, heatwave indices based on fixed (HWI S ) and anomalous temperatures (HWI E ), and Daily Flood Index (DFI). We selected suitable environmental indicators and corresponding thresholds for each hazard based on estimated extreme event detection performance using receiver operating characteristics (ROC), area under curve (AUC), and accuracy, which is defined as the proportion of correct detections. We assessed compound hazard dependence using a Likelihood Multiplication Factor (LMF). We tested the framework for the case of Sweden, using daily data for the period 1922–2021. The ROC results showed that HWI S , SPEI12 and DFI are suitable indices for representing heatwaves, droughts, and floods, respectively (AUC > 0.83). Application of these indices revealed increasing heatwave and flood occurrence in large areas of Sweden, but no significant change trend for droughts. Hotspots with LMF > 1, mostly concentrated in Northern Sweden from June to August, indicated that compound drought-heatwave and drought-flood events are positively correlated in those areas, which can exacerbate their impacts. The novel framework presented here adds to existing hydroclimatic hazard research by (1) using local data and historical records of extremes to validate indicator-based hazard hotspots, (2) evaluating compound hazards at regional scale, (3) being transferable and streamlined, (4) attaining satisfactory performance for indicator-based hazard detection as demonstrated by the ROC method, and (5) being generalizable to various hazard types. [ABSTRACT FROM AUTHOR]

As precision agriculture (PA) advances, the demand for accurate and high-resolution weather forecasts becomes critical for optimizing agricultural management practices. Despite improvements in Numerical Weather Prediction (NWP) models, they lack the granularity and efficiency needed for PA. Data-driven models offer a promising alternative by integrating predictive capabilities closer to IoT edge data sources, but their efficacy requires evaluation. Here, this paper evaluates six models from three data-driven eras (statistical, machine learning, and deep learning) using agrometeorological data from an Automatic Weather Station (AWS) in Sidi Rahal, East Marrakech, central Morocco, covering 2013–2020 at half-hour intervals, including air temperature, solar radiation, and relative humidity. First, the data is quality-controlled through imputation using ERA5-Land. Then, the dataset was split into training (2013–2019) and evaluation (2020) sets, with validation horizons of 1 day, 3 days, and 1 week. Statistical models generally perform well in air temperature forecasting, occasionally surpassing other models. However, the Temporal Convolutional Neural Network (TCNN) consistently demonstrates superior performance for challenging variables, balancing low RMSE and high R2 across various horizons, with some exceptions. Specifically, for relative humidity, the linear regression model achieves slightly lower RMSE (3,96% and 6,05%) compared to TCNN (4,00% and 6,79%) for 1 day and 3 days, respectively. Additionally, CatBoost outperforms TCNN for 1-week forecasts. In terms of training time, the Transformer requires the longest, followed by AutoARIMA and CatBoost. Uncertainty analysis of stochastic models using solar radiation showed the stable performance of TCNN with 0,80 and 0,01 for the RMSE and R2 standard deviations, respectively. Considering the trade-off between performance, training time, and capturing complex relationships, TCNN emerges as the optimal choice. ANOVA, Tukey's HSD and Mann-Whitney U statistical tests also confirmed TCNN's performance. Finally, a comparison with the Global Forecast System (GFS) reveals TCNN's clear superiority in all metrics, particularly evident for the RMSE of 3 days air temperature forecasts (TCNN: 1,96 °C, GFS: 3,59 °C). [ABSTRACT FROM AUTHOR]

This paper presents an integrated simulation of future hydrological droughts using different climate scenarios for the Piancó River basin. Streamflow and sediment yield are estimated using the Soil and Water Assessment Tool (SWAT) model, and hydrological droughts are calculated using the Streamflow Drought Index (SDI). The study incorporates two future climate scenarios: RCP 4.5 (optimistic) and RCP 8.5 (pessimistic). The SDI is determined based on a series of simulated future flows to identify drought events within the basin. The results show that the model calibration and validation demonstrated excellent agreement with observed data (R² = 0.83, NSE = 0.82 for calibration and R² = 0.89, NSE = 0.77 for validation). When comparing the scenarios, reductions in flow volume by −75.86% and sediment yield by −86.5% are noted, indicating a significant decrease in sediment contribution to the outlet. Interestingly, the most critical years related to drought events, as determined by the SDI, do not coincide across the pessimistic and optimistic scenarios. [ABSTRACT FROM AUTHOR]

Accurate and efficient identification of pollution sources is a key process that assists in the treatment of water pollution incidents. The ensemble Kalman filter (EnKF) has been proven to be an effective approach for identifying pollution source parameters (e.g., source location, release time, and mass released). In this paper, a method involving multiple observations of reconstruction (MOR) is proposed for reconstructing multidimensional state vectors for assimilation based on pollutant concentration monitoring techniques. The newly reconstructed state variables have dimensionless characteristics that decouple the source mass from the parameter group to be identified before assimilation is performed. This approach can mitigate the interference of assimilation caused by nonmain source parameters. As a result, the pollution sources and material dispersion coefficients can be simultaneously identified at limited observation sites. Then, a set of synthetic numerical examples with 7 scenarios is assembled to investigate and compare the unique characteristics of the derived state variables during assimilation. A laboratory experiment for unknown parameter identification based on monitoring the chemical oxygen demand (COD) concentration is carried out in an annular flume to verify the applicability of the method in real events. The results show that the EnKF combined with the MOR method based on the decoupling pattern performs well in identifying pollution sources and dispersion coefficients simultaneously. The method can still perform excellently in identifying parameters in practice when some data in the observation sequences are lost, with relative errors of pollution source parameters being controlled within 4%. The relative errors of the identified transverse and longitudinal dispersion coefficients are 39% and 12%, respectively. Overall, by evaluating the original data, reconstructing the dataset, and combining it with the EnKF method, it is proven that the MOR–EnKF method is an effective measure for identifying high-dimensional unknown parameter groups. [ABSTRACT FROM AUTHOR]

Gaussian processes are popular in spatial statistics, data mining and machine learning because of their versatility in quantifying spatial variability and in propagating uncertainty. Although there has been a prolific research activity about Gaussian processes over Euclidean domains, only recently this research has extended to non-Euclidean manifolds. This paper digs into vector-valued Gaussian processes defined over the product of a hypersphere and a Euclidean space of arbitrary dimension, which are of interest in various disciplines of the natural sciences and engineering. Under mild regularity conditions, we establish a surprising one-to-one correspondence between matrix-valued kernels associated with vector Gaussian processes over the product space, and what we term partial ultraspherical and Fourier transforms that are taken over either the sphere or the Euclidean subspace. The properties of our approach are illustrated in terms of new parametric classes of matrix-valued kernels for product spaces of a hypersphere crossed with a Euclidean space. We also provide two algorithms that allow for fast simulation of approximately Gaussian (in the sense of the central limit theorem) processes in such product spaces. [ABSTRACT FROM AUTHOR]

Rain gauge networks are usually redesigned to improve the accuracy of spatial and temporal estimates and reduce the monitoring costs. In addition to the rain-gauge data, satellite-based data can be used for the optimal redesign of rainfall monitoring networks. Despite high spatial and temporal resolutions of products of meteorological satellites, they must be calibrated using ground-based data. In this paper, a new methodology is presented for an optimal redesign of rain-gauge networks. In this methodology, after the bias correction of some satellite-based rainfall products (i.e., PERSIANN-CCS, TRMM3B42RTV7, and CMORPH), they are combined using a Bayesian information fusion technique. The spatiotemporal rainfall maps, which are developed using the fusion model, are used in an optimization model. The optimization model provides the optimal locations of the rain-gauge stations, considering the objectives of minimizing both the monitoring cost and the average standard deviation of estimation errors. To illustrate the applicability and efficiency of the proposed methodology, it is applied to the Khorasan Razavi Province in Iran. The results show that by using the satellite data and the Bayesian information fusion technique, the summation of standard deviations of estimation errors is significantly reduced from 18,787.9 to 778.28 mm. [ABSTRACT FROM AUTHOR]

The choices that researchers make while conducting a statistical analysis usually have a notable impact on the results. This fact has become evident in the ongoing research of the association between the environment and the evolution of the coronavirus disease 2019 (COVID-19) pandemic, in light of the hundreds of contradictory studies that have already been published on this issue in just a few months. In this paper, a COVID-19 dataset containing the number of daily cases registered in the regions of Catalonia (Spain) since the start of the pandemic to the end of August 2020 is analysed using statistical models of diverse levels of complexity. Specifically, the possible effect of several environmental variables (solar exposure, mean temperature, and wind speed) on the number of cases is assessed. Thus, the first objective of the paper is to show how the choice of a certain type of statistical model to conduct the analysis can have a severe impact on the associations that are inferred between the covariates and the response variable. Secondly, it is shown how the use of spatio-temporal models accounting for the nature of the data allows understanding the evolution of the pandemic in space and time. The results suggest that even though the models fitted to the data correctly capture the evolution of COVID-19 in space and time, determining whether there is an association between the spread of the pandemic and certain environmental conditions is complex, as it is severely affected by the choice of the model. [ABSTRACT FROM AUTHOR]

The increasing carbon emissions have been a major concern for most countries around the world. And as a result, every country is concerned about developing appropriate strategies to curtail it. As a major economy and the largest carbon emitter in the world, China has pledged to reduce the carbon intensity by 60–65% by 2030, compared with 2005 levels, and achieve carbon neutrality before 2060. Therefore, the analysis of the impact of China's carbon intensity is becoming an increasing important topic. Due to the spatial heterogeneity of carbon intensity, various spatial econometric models have been applied in this field. However, the existing literatures failed to consider the cross-products of relevant factors. This paper constructs our dynamic general nesting spatial panel model (GNS) with common factors to deal with the dilemma, and examines the direct and spatial–temporal spillover effects of industrial structure, GDP per capita, investment in anti-pollution projects as percentage of GDP and energy price on carbon intensity in China over the period 2003–2017. Our analysis shows that: (1) China's carbon intensity showed the spatial agglomeration and temporal "inertia" from 2003 to 2017. (2) From the time dimension, the long-term effect of industrial structure first increased and then gradually decreased. (3) From the spatial dimension, industrial structure and investment in anti-pollution projects as percentage of GDP accounted for the main spatial heterogeneity. Furthermore, this paper attempts to provide policy implications to help reduce carbon intensity and achieve carbon neutrality in China. [ABSTRACT FROM AUTHOR]

Floods are one of the most frequent, dangerous natural disasters globally. During the period from 1990 to 2020, more than 50% of the world's recorded disasters were related to floods. This problem stems largely from the inadequate planning and economic circumstances of human settlements in flood-prone plains. Geospatial modelling can be a powerful tool for large-scale flood modelling. The hydrological and hydraulic models theory, GIS-based multi-criteria evaluation techniques, and machine learning algorithms for flood simulation are presented. The most used techniques and methodologies for the geospatial simulation of floods in the last decade are presented. This paper also shows the input data requirements and the algorithms used for each geospatial technique and a description of the tools and some relevant examples of geospatial flood studies are given. A comprehensive assessment of the characteristics of the flood models is presented based on its modelling approach, either for flood susceptibility, hazard, vulnerability, or risk. [ABSTRACT FROM AUTHOR]

This paper presents a Bayesian clustering approach that allows quantification of the effect of climate variability on seasonal precipitation data in Kebir Rhumel Basin (KRB). We applied this approach to simultaneously identify clusters of stations with similar characteristics and the climate variability associated with each cluster and for the individual stations within each cluster. Both full pooling Bayesian clustering (FPBC) and partial pooling Bayesian clustering (PPBC) models with nonstationary generalized extreme value (GEV) distribution are applied to each season. In these models, a climate index variable, namely the El Niño Southern Oscillation (ENSO), is included as a time-varying covariate with an appropriate basis function to potentially explain the temporal variation of one or more of the parameters of the distribution. Results reveal that the partial pooling Bayesian clustering model provided the best fit for the seasonal precipitation data. The significant effect of ENSO differs from one season to another. During spring and autumn, ENSO significantly affects precipitation across large parts of KRB. Furthermore, the southern part and northern part of KRB are positively and negatively influenced by ENSO during winter and summer, respectively. Moreover, almost all stations during spring and autumn are negatively and positively influenced by ENSO, respectively. Finally, we demonstrated that the proposed model helps to reduce the uncertainty in the parameter estimation and provides more robust results. [ABSTRACT FROM AUTHOR]

The paper presents the possibility of using data mining tools — artificial neural networks — in prediction of hydrometer reading after 24 h in order to limit the duration of the test to 4 h. The authors analysed a database of 693 granulometric composition analysis results of genetically different soils with the use of radial basis function network (RBF) and multilayer perceptron (MLP). The calculations performed showed that it is possible to use MLP to shorten the test time without affecting the quality of the results. The presented accuracy of the model, in the range of 0.55–0.72, allows one to determine the content of silt and clay fractions with an accuracy of 0.49% for equivalent diameter (dT) and 1.50% for percentage of all particles with a diameter smaller than dT (ZT). The results were better than that achieved using linear re-gression models with all predictors (REG), stepwise regression models (SREG), and classification and regression trees (CRT). Taking into account the uncertainty of hydrometric determinations, the obtained forecast values is lower than this uncertainty, therefore neural networks can be used to predict the results of this type of research. [ABSTRACT FROM AUTHOR]

This paper presents a long-term analysis of ambient PM10 concentrations measured at a background station in Spain, using backward trajectories generated with the HYSPLIT model, together with the identification of synoptic patterns and a fuzzy clustering analysis, to identify the sources present and evaluate their relationship with meteorological variables such as wind speed and direction, temperature, and atmospheric pressure. Ambient PM10 presented maximum values during the summer with mean concentrations of approximately 14 µg m−3 for August, and minimum values during the winter with concentrations of 6 µg m−3 for January. The daily cycle presented two peaks—one in the morning and another in the afternoon—with this variability being associated with transport emissions. African air masses reached the study site extending vertically at least up to 2500 m.a.g.l, with a frequency of 43.5%, and are associated to a mean ground PM10 concentration of 41.3 µg m−3. During the episodes of Saharan intrusions, high pressures with unclassified synoptic patterns (U) prevailed over the Iberian Peninsula. Local, European, maritime and intermittent contributions were the four main sources of pollutants identified through fuzzy clustering analysis. These intermittent contributions are associated with Saharan dust intrusions, with a long-term average PM10 concentration of 1.7 µg m−3, which represents an important contribution of 11.2%. These sources affect seasonal variations of PM10 background concentrations, and reach their maximum when the greatest contributions of desert dust occur—mainly during the spring–summer months. These results provide useful information for future comparisons and environmental monitoring of PM10 levels. [ABSTRACT FROM AUTHOR]

Fatal Shock, competing risks and stress models are utilized in reliability and survival analysis to characterize various application types. In this context, this paper aimed to introduce a new class of bivariate distribution based on the Marshall–Olkin type. The proposed distribution termed as the Bivariate Extended Chen (BEC) distribution so that the marginals have Extended Chen distribution. Some properties of this distribution are derived and explained. The BEC distribution parameters are estimated using the maximum likelihood method. Finally, two applications to real life applications highlight the significance and adaptability of the Bivariate Extended Chen (BEC) distribution. The BEC distribution offers a superior fit than different competing bivariate distributions, illustrating its flexibility and applicability in modelling. [ABSTRACT FROM AUTHOR]

It is now almost three years that COVID-19 has been the cause of misery for millions of people around the world. Many countries are in process of vaccination. Due to the social complexity of the problem, the future of decisions is not clear. As such, there is a need for the mathematical modeling to predict the long-run behavior of the COVID-19 dynamic for the decision-making with regard to the result of the pandemic on the economy, health, and others. In this paper, we have studied the short and long-run behavior of COVID-19. In a novel way, random evolution (Trichotomous and Dichotomous Markov Noise) is used to model and analyze the long-run behavior of the pandemic in different phases of the pandemic in different countries. On the given conditions, the random evolution model can help us establish the long-run asymptotic behaviour of the pandemic. This allows us to consider different phases of the pandemic as well as the effect of vaccination and other measures taken. The simplicity of the model makes it a practical tool for decision-making based on the long-run behavior of the pandemic. As such, we have established a criterion for the comparison of different regions and countries in different phases. In this regard, we have used real pandemic data from different countries to validate our results. [ABSTRACT FROM AUTHOR]

This paper addresses how residents can safely access shelters from their buildings. Firstly, we investigated Xiaying Village, a typical community with severe buildings damage at the epicentre of the Ms5.0 magnitude earthquake in Tonghai that occurred on August 13, 2018, and found that the injured residents when escaping resulted from the damage to masonry buildings. Secondly, a probabilistic approach combining response spectrum and allowable maximum acceleration is proposed to estimate the damage risk of masonry buildings with inadequate seismic performance in old communities. Thirdly, three models are proposed to assess the risk of the escape path, which include all scenarios of the escape of residents to shelters. Finally, an accessibility analysis of the paths that residents may choose to escape to the shelter is presented. The results of the community escape case study in the Tonghai earthquake show that the probabilistic approach to estimating the escape risk is feasible, and the number of damaged buildings in the escape path dictates the accessibility of residents to the shelter. The proposed method is important for guiding old community residents to escape when a strong earthquake strikes, and it is an effective way to improve community disaster resilience. [ABSTRACT FROM AUTHOR]

The study on the evolution of water use structure is very important for water resources management. In this paper, the evolution of water use structure is defined by the matrix-based Renyi's alpha order entropy functional, and a method of detection of change-point is developed based on the Bernaola-Galvan algorithm (BG algorithm) and moving window technique. Furthermore, key driving factors are identified for different kinds of water use through the normalized information flow, which is used to explain the sudden change of water use structure. The new method is applied to analysis on abrupt change of water use structure in Qingyang, China. It is found that the structure of water use is basically stable before 2010 and becomes extremely unstable during the period of 2014–2018. The irrigation water consumption changed abruptly in 2005 and 2014. The industrial water consumption changed abruptly in 2005 and 2017, while the domestic water consumption changed abruptly in 2009 and 2012. The urban public water consumption varied abruptly in 2005 and 2011. The water consumption for forestry, animal husbandry and fishery changed abruptly in 2009 and 2013, while the eco-environmental water consumption changed abruptly in 2009 and 2017. Moreover, the reasons of abrupt change for different kinds of water consumption are analyzed and discussed, this will be useful for future water resources planning and management. [ABSTRACT FROM AUTHOR]

Accurate and effective PM2.5 concentration prediction has important implications for public health and the ecological environment. To provide more accurate early warnings for haze prevention, urban planning, and people's travel planning, this paper proposes a new combined PM2.5 concentration prediction model. Firstly, the original sequence is decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), followed by decomposition and reconstruction of the data by adaptive variational mode decomposition (AVMD) and sample entropy (SE), and then the reconstructed subseries are predicted by long and short-term memory networks (LSTM). The empirical analysis was carried out with three datasets from Beijing, Tianjin, and Baoding, and the following conclusions can be drawn: (1) The validity and robustness of the proposed model were verified, with R2 (0.982), RMSE (2.792), and MAPE (9.088%) being optimal in all comparison experiments. (2) The incorporation of secondary decomposition and pattern reorganization algorithms can effectively handle data with high volatility and non-linearity. (3) Compared with traditional machine learning models, the long and short-term memory network is more suitable for time series prediction. The model provides a novel and effective PM2.5 concentration prediction tool for the government and the public. [ABSTRACT FROM AUTHOR]

Representativeness and quality of collected meteorological data impact accuracy and precision of climate, hydrological, and biogeochemical analyses and predictions. We developed a comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework, consisting of three major phases: Phase I—Preliminary data exploration, i.e., processing of raw datasets, with the challenging problems of time formatting and combining datasets of different lengths and different time intervals; Phase II—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme data; and Phase III—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The paper includes two use cases based on the time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado), and the Barro Colorado Island (BCI, Panama) meteorological station. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets. [ABSTRACT FROM AUTHOR]