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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]

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]

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]

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]

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]

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]

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 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]

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]

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]

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]

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]

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]

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]

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 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]

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]

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]

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]

Precise and efficient landslide displacement prediction is crucial for improving the effectiveness of landslide warning systems. Numerous time series decomposition and machine learning (ML) methods have been proposed and applied in landslide displacement prediction. Nevertheless, most ML methods display individual biases when applied to landslide displacement datasets, and the effect of different methods for time series decomposition on prediction results has not been systematically studied. Therefore, this paper adopts four methods commonly used for time series decomposition to decompose the accumulated displacement into a trend term and a periodic term. The double exponential smoothing is utilized to predict the trend displacement. After the grey relation analysis between the periodic displacement and the external cyclical influencing factors, the ensemble algorithm is used to integrate six commonly used ML algorithms for the prediction of periodic displacement, so as to eliminate the bias of individual artificial intelligence method and enhance the accuracy and stability of prediction results. Furthermore, Bayesian optimization is employed to optimize the base-learners, ensuring the integration fairness. The typical step-like landslides (i.e., Bazimen landslide, Caojiatuo landslide) in the Three Gorges area are selected to compare the performance of different methods for time series decomposition and illustrate the effectiveness of the framework of the ensemble algorithm with the evaluation indices of mean absolute error, mean absolute percentage error and root mean square error. The prediction results indicate that the ICEEMDAN method has the best performance in displacement decomposition. In addition, the prediction results of Bayesian optimized ensemble method are more robust than those of individual ML method, facilitating more accurate and stable landslide displacement prediction and more effective reference for landslide early warning. [ABSTRACT FROM AUTHOR]

The intensities and frequencies of extreme rainstorms are increasing, which may result in severe inundation of urban metro systems. Although there is some risk assessment research on regional metro systems based on spatiotemporal data, the characteristics of specific metro stations and shortcomings in the emergency response process need more consideration. In this paper, a risk analysis model for rainstorm inundation in metro systems based on a Bayesian network and a practical case study are proposed. First, the risk factors are obtained by integrating general mechanism analysis and the case study. Second, an event evolution diagram is established to represent the comprehensive evolution process of a potential event. Third, the risk analysis model is established using a Bayesian network model considering the quantitative causal relationships between risk factors. This model is used to analyze the risk of supporting emergency management, including emergency preparation based on critical risk factor sensitivity identification, prewarning response strategy development based on risk analysis as rainstorms occur, and rescue strategy development based on risk analysis as rainstorm water flows into metro tunnels. Furthermore, this model can be flexibly improved as natural hazards and metro systems change and as new problems are exposed in practical cases. [ABSTRACT FROM AUTHOR]

Accurately and reliably predicting droughts under multiple models of Global Climate Models (GCMs) is a challenging task. To address this challenge, the Multimodel Ensemble (MME) method has become a valuable tool for merging multiple models and producing more accurate forecasts. This paper aims to enhance drought monitoring modules for the twenty-first century using multiple GCMs. To achieve this goal, the research introduces a new weighing paradigm called the Multimodel Homo-min Pertinence-max Hybrid Weighted Average (MHmPmHWAR) for the accurate aggregation of multiple GCMs. Secondly, the research proposes a new drought index called the Condensed Multimodal Multi-Scalar Standardized Drought Index (CMMSDI). To assess the effectiveness of MHmPmHWAR, the research compared its findings with the Simple Model Average (SMA). In the application, eighteen different GCM models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) were considered at thirty-two grid points of the Tibet Plateau region. Mann–Kendall (MK) test statistics and Steady States Probabilities (SSPs) of Markov chain were used to assess the long-term trend in drought and its classes. The analysis of trends indicated that the number of grid points demonstrating an upward trend was significantly greater than those displaying a downward trend in terms of spatial coverage, at a significance level of 0.05. When examining scenario SSP1-2.6, the probability of moderate wet and normal drought was greater in nearly all temporal scales than other categories. The outcomes of SSP2-4.5 demonstrated that the likelihoods of moderate drought and normal drought were higher than other classifications. Additionally, the results of SSP5-8.5 were comparable to those of SSP2-4.5, underscoring the importance of taking effective actions to alleviate drought impacts in the future. The results demonstrate the effectiveness of the MHmPmHWAR and CMMSDI approaches in predicting droughts under multiple GCMs, which can contribute to effective drought monitoring and management. [ABSTRACT FROM AUTHOR]

Regional debris flow susceptibility assessment is an effective method to prevent debris flow hazards, and deep learning is emerging as a novel approach in this discipline with the development of computers. However, when debris flow samples are insufficient, there will be problems like overfitting or misclassification. To overcome these problems, this paper proposes a semi-supervised deep neural network model (LPA-DNN) combined with label propagation algorithm (LPA), which utilizes high confidence unlabeled samples as pseudo-samples reasonably in few-label scenarios. Xinzhou, Shanxi Province, was selected as the study area, and a dataset containing 292 debris flow samples and 10 types of impact factors was compiled based on watershed units. Using the dataset and pseudo-samples, the LPA-DNN model was built to get debris flow susceptibility map. Meanwhile, DNN and SVM were set up for comparison to demonstrate that the proposed LPA-DNN model has excellent performance and higher accuracy. LPA-DNN alleviates the problem of low accuracy that caused by samples lacking in deep learning to a certain extent, and obtains great classification results, which proves that it is quite potential in regional debris flow susceptibility assessment. [ABSTRACT FROM AUTHOR]

The inhomogeneous Log-Gaussian Cox Process (LGCP) defines a flexible point process model for the analysis of spatial point patterns featuring inhomogeneity/spatial trend and aggregation patterns. To fit an LGCP model to spatial point pattern data and study the spatial trend, one could link the intensity function with continuous spatial covariates. Although non-continuous covariates are becoming more common in practice, the existing estimation methods so far only cover covariates in continuous form. As a consequence, to implement such methods, the non-continuous covariates are replaced by the continuous ones by applying some transformation techniques, which are many times problematic. In this paper, we develop a technique for inhomogeneous LGCP involving non-continuous covariates, termed piecewise constant covariates. The method does not require covariates transformation and likelihood approximation, resulting in an estimation technique equivalent to the one for generalized linear models. We apply our method for modeling COVID-19 transmission risk in East Java, Indonesia, which involves five piecewise constant covariates representing population density and sources of crowd. We outline that population density and industry density are significant covariates affecting the COVID-19 transmission risk in East Java. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new extension of the Vasicek model to the multivariate case and analyze its characteristics, including its probability density function, marginal trends, and correlation functions. We also conduct statistical estimation on the model, including likelihood parameter estimation and estimation of the marginal trend and correlation functions. To validate the effectiveness of our proposed model, we use simulation studies and examine the goodness of fit. Additionally, we apply our model to a bivariate case involving the analysis of CO 2 and N 2 O concentrations. We describe the data, fit the bivariate Vasicek stochastic diffusion model, and compare it with the univariate case. Finally, we summarize our findings and discuss the potential applications of our proposed model. [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 utilization of wind energy requires a careful study of its characteristics. Statistical distributions are used to evaluate the wind characteristics, such as speed, and also wind power potential. In this paper, in addition to commonly used parametric probability distributions (Generalized Rayleigh (GR), Gamma (G), Gumbel (Gu), Exponentiated Weibull (EW)), we propose and evaluate more recent distributions (Exponentiated Half-Logistic (EHL), Exponentiated Half-Normal (EHN), Skew Logistic (SKL), Generalized Extreme Value (GEV)) in order to increase the accuracy of wind speed calculation in future studies. To study the wind condition, mean daily wind speed and direction data (For 7 meteorological stations in northern Iran from 2007 to 2017) were received and analyzed. Then, the wind energy potential was calculated using the results of accurate models. Based on the results, the distributions of (G, EHL, EHN, EW, and GR) were fitted to the data. The performance of models varied from station to station. However, the EHN distribution gave the smallest value of the Kolmogorov–Smirnov statistic in most situations, indicating a good fit of this distribution to the total data. Accordingly, this distribution is suitable for fitting wind speed data, because the values of the measured wind speed are in compliance with the model-based estimates. Therefore, we recommend this distribution as a suitable model for simulation, calculation, and other criteria for modeling wind condition in similar areas. Meanwhile, the highest wind power density for wind energy production is observed in Ardabil, Gorgan, and Zanjan stations. [ABSTRACT FROM AUTHOR]

Ensuring the cooperative security of water, energy, and food (WEF) nexus system is one of the keys to achieve regional high-quality development. In the present study, the pressure-state-response (PSR) model was introduced to construct the cooperative security evaluation index system (CSEIS) of the WEF nexus system from the perspectives of stability of resources supply and demand (ST), coordination of resources utilization(CO), and sustainability of cooperative security of the WEF nexus system (SU). Then, the dynamic evolution trend and key obstacle factors of cooperative security of the WEF nexus system were revealed and analyzed respectively on the basis of comprehensive evaluation in Southwest China. In order to further improve the cooperative security of the WEF nexus system, this paper proposed a series of countermeasures from the perspectives of resources stability, coordination and sustainability, etc. The results showed that: The cooperative security of the WEF nexus system in four regions in Southwest China were maintained at their own levels with a relatively high probability. Specifically, the cooperative security of the WEF nexus system in Chongqing and Sichuan was mostly at Level III (moderate security), while that of Guizhou and Yunnan was mostly at Level IV (lower security). As for the transfer risk, the cooperative security levels of Chongqing and Sichuan were more inclined to transfer downward with limited potential and speed, while the cooperative security levels of Guizhou and Yunnan provinces were more inclined to transfer upward. More than half of the key obstacle factors came from the response system (e.g. D20, D21, D25, D27, and D28), indicating that the response subsystem had the greatest impact on the cooperative security of the WEF nexus system. [ABSTRACT FROM AUTHOR]

Motivated by the realistic demand of reducing the flood risk of urban metro stations, this paper proposes an innovative research framework for flood risk assessment and classification. In this framework, a specified criterion system is established from the perspective of hazard, vulnerability, exposure and rescue capacity. Furthermore, we introduce an application of the SMAA-2-FFS-H approach to verify the practicality and efficiency of the proposed framework. As an extension of SMAA-FFS-H proposed by predecessors, the SMAA-2-FFS-H method comprises stochastic multi-criteria acceptability analysis-2 (SMAA-2), fuzzy flowsort (FFS) and the multi-criteria hierarchy process. Its stochastic nature allows for imprecisions and uncertainty that naturally exist in the particular physical environment of metro stations to be embedded into the evaluation process. The flood risk of 32 stations in Zhengzhou Metro Line 5 during the "7 · 20" rainstorm event is evaluated as a case study. The results accurately indicate that only Shakoulu Station is at high risk, which corresponds to events observed during the rainstorm. Moreover, the risk class characteristics of the remaining stations correspond to historical flooding events. Among them, 10 metro stations are classified as medium risk, 21 are classified as low risk and none are categorized as being at very low risk. Therefore, all metro stations are at risk of flooding although the degree of risk differs between some metro stations. In addition, the validity and robustness of the introduced method are demonstrated by performing comparative analysis and sensitivity analysis. Finally, some managerial suggestions are provided for strengthening the flood control capabilities of metro stations. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamics of the time-series of water temperature of the Skokomish River (2019–2020) at hourly time scale by employing well-known nonlinear methods of chaotic data analysis including average mutual information, false nearest neighbors, correlation exponent, and local divergence rates. The delay time and the embedding dimension were calculated as 1400 and 9, respectively. The results indicated that the thermal regime in this river is chaotic due to the correlation dimension (1.38) and the positive largest Lyapunov exponent (0.045). Furthermore, complex networks have been applied to study the periodicity of thermal time-series throughout a year. A special algorithm is then used to find the so-called communities of the nodes. The algorithm found three communities which have been called Cold, Intermediate, and Warm. The temperatures in these three communities are, respectively, in the intervals (0.8, 5.8), (5.8, 11.63), and (11.63, 15.8). This analysis indicates that highest variations in water temperature occur between warm and cold seasons, and complex networks are highly capable to analyze hydrothermal fluctuations and classify their time-series. [ABSTRACT FROM AUTHOR]

This paper deals with the study of stationarity and mean reversion in the temperature anomalies series in the southwestern American cone. In particular, monthly temperatures in 12 Chilean meteorological stations were studied (from the 1960's to nowadays), examining if temperature shocks are expected to remain in the long term or if they are reversible. The results clearly show a significant relationship between the latitude, climate, and the order of integration of the temperatures. The orders of integration tend to be smaller in colder southern parts, therefore impacts of climate change are expected to be more reversible. However, in northern desert areas the orders of integration are larger than 0.5, thus impacts are expected to be maintained for a longer time. [ABSTRACT FROM AUTHOR]

The ecological health of water quality is an essential factor in the survival and development of human beings. By setting up multiple sensors in the water body, people observe the factual state of water quality, and collect a large number of sequence data containing time information. In order to effectively utilize water quality time series data and predict future water quality changes, this paper proposes an improved deep learning method based on spatiotemporal feature correlated, called the convolution recurrent basis expansion analysis architecture. The model improves the original model by using the ability of convolutional neural network structure to extract spatial features and the continuous memory ability of recurrent neural network structure. Then, the physical prior knowledge is integrated into the proposed network to limit the unreasonable results predicted in the feasible solution space to further improve the learning efficiency of the model. For specific research objects, the model can mine multi-dimensional features in water quality sequences at different depths, and learn the temporal features of the sequences hierarchically. In order to verify the performance of the proposed model, we apply the proposed model to the water quality dataset collected in Tanghe Reservoir for simulation. The results of the study demonstrate that our model is suitable for water quality prediction and has distinct advantages over traditional neural networks. Our model will make accurate predictions of future changes in water quality and provide technical support in the refined water quality automatic monitoring system. [ABSTRACT FROM AUTHOR]

The weekend effect, i.e., the concentration of air pollutants is different from weekends to weekdays, has been explored since the 1970 s. In most studies, the weekend effect is referred to the change of O 3 , i.e., lower emission of NO x on weekends leads to a higher concentration of O 3 . Determining whether it is true can provide insight into the strategy of air pollution controlling. In this study, we explore the weekly cycle of cities of China based on the weekly cycle anomaly (WCA), which is proposed in this paper. The advantage of using WCA is that we can avoid the influence of other change components such as the daily cycle and the seasonal cycle. The p values of the significant tests from all cities are analyzed for obtaining a whole picture of the weekly cycle of air pollution. The result shows that the concept of the weekend effect is not proper for cities in China, as many cities reach the valley phase of the emission on weekdays but not weekends. Thus, researches should not pre-assume that the weekend is the low emission scenario. We focus on the anomaly of O 3 on the peak and the valley of the emission scenario estimated by the NO 2 concentration. Through the analysis of the distribution of the p values of all cities, we show that almost all cities in China have weekly cycle of O 3 corresponding to the weekly cycle emission of NO x , i.e., O 3 of the peak time of NO 2 is less than valley time. The cities of the strong weekly cycle are located in four regions, i.e., the Beijing-Tianjing-Hebei region, the Shandong Peninsula Delta, the Yangtze River Delta, and the Pearl River Delta, and these regions are also the regions with relatively severe pollution levels. [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]

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]

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]

Occupational risk assessment (ORA) is a process that consists of evaluating, ranking, and classifying the hazards and associated risks arising in any workplace from the viewpoint of occupational health and safety. Many ORA methods have been proposed in the literature, from a single independent expert to participatory methodologies made by group decision and simple to complex ones. In this paper, a holistic ORA is presented, which uses two important multi-attribute decision methods named Bayesian Best-Worst Method (Bayesian BWM) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). Bayesian BWM is used to determine the importance weights of six different assessment criteria, which are the probability of hazardous event (P), frequency (F), severity (S), detectability (D), cost (C) and sensitivity not to use personal protective equipment (SNP). Since the classical BWM finds solution to the weights of a number of criteria from only one expert's judgment, Bayesian BWM is preferred in this paper (1) to enable participation of a group of experts, (2) to aggregate the preferences of these multiple experts into consensus without loss of information and (3) to follow a probabilistic way for solving the ORA problem. The hazards are then ranked by VIKOR. The approach is implemented in the ORA process of a textile production plant. Results of risk analysis showed that electricity hazard and associated risks constitute the highest risk ratings. These hazards arise from the product, process, human and working environment. The associated risks are evaluated, prioritized, and detailed control measures are proposed. This study made comparisons with the classical BWM-VIKOR approach to demonstrate the proposed approach's difference and practicality. Results can also help practitioners and risk analysts in formulating the improvement measures to increase the overall safety of the working environment further. [ABSTRACT FROM AUTHOR]

Flash floods cause great harm to people's lives and property safety. Rainfall is one of the main causes of flash floods in small watersheds. The uncertainty of rainfall events results in inconsistency between the traditional single rainfall pattern and the actual rainfall process, which poses a great challenge for the early warning and forecasting of flash floods. To carry out the effective flash flood early warning and forecasting, this paper proposes a novel rainfall pattern by coupling total rainfall and peak rainfall intensity based on copula functions, i.e., the rainfall pattern of risk probability combination (RPRPC). On this basis, the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) hydrological model is used to simulate the rainfall-runoff process, the trial algorithm is used to calculate the critical rainfall (CR), and the optimistic-general-pessimistic (O–G-P) early warning mode considering the decision maker's risk preference is proposed. The small watershed of Xinxian in Henan province, China, is taken as a case study for calculation. The results show that the RPRPC is feasible and closer to the actual rainfall process than the traditional rainfall pattern, Frank copula function is the best for determining the joint distribution function of total rainfall and peak rainfall intensity, and the HEC-HMS model can be applied to small watersheds in hilly areas. Additionally, both RPRPC and antecedent soil moisture condition (ASMC) have influence on CR, and the variation of RPRPC will change the influence of ASMC on CR. Finally, the effectiveness of O–G-P early warning mode is verified. [ABSTRACT FROM AUTHOR]

Inherent biases in numerical simulation models of global climate models (GCMs) reduce the scope for accurately assessing future droughts under multimodel ensemble. The aim of this research is to increase the efficiency of multimodel ensembles. Consequently, this paper introduces a two-way hybrid weighting scheme for ensembling multiple GCMs. The proposed weighting scheme enhances the coherence of the multimodel ensemble of climate model simulations with real observed data and minimizes the impact of non-identical seasonal behavior in climate simulations within the multimodel ensemble. In the application, we consider precipitation data from 18 GCMs of CMIP6 from the Tibet Plateau region. To evaluate the effectiveness of the proposed scheme, we compared the proposed ensemble data with simple model averaged (SMA) ensemble using the Pearson product-moment correlation coefficient (r), root mean squared error (RMSE), and mean absolute error (MAE). Furthermore, we assessed the future characteristics of various drought categories using the steady-state probabilities of a Markov Chain. Small values of errors metircs and high correlation indicate that the proposed hybrid weighting scheme exhibits better performance than the SMA. Under the proposed index, the long term probabilities of various drought classe show variations across different time scales of drought. These results provide perspectives on how the the long term probabilities of various drought categories evolve over time. [ABSTRACT FROM AUTHOR]