Stochastic environmental research and risk assessment : research journal最新文献

{"title":"Selection of the gridded temperature dataset for assessment of thermal bioclimatic environmental changes in Amu Darya River basin.","authors":"Obaidullah Salehie,&nbsp;Tarmizi Bin Ismail,&nbsp;Shamsuddin Shahid,&nbsp;Saad Sh Sammen,&nbsp;Anurag Malik,&nbsp;Xiaojun Wang","doi":"10.1007/s00477-022-02172-8","DOIUrl":"https://doi.org/10.1007/s00477-022-02172-8","url":null,"abstract":"<p><p>Assessment of the thermal bioclimatic environmental changes is important to understand ongoing climate change implications on agriculture, ecology, and human health. This is particularly important for the climatologically diverse transboundary Amy Darya River basin, a major source of water and livelihood for millions in Central Asia. However, the absence of longer period observed temperature data is a major obstacle for such analysis. This study employed a novel approach by integrating compromise programming and multicriteria group decision-making methods to evaluate the efficiency of four global gridded temperature datasets based on observation data at 44 stations. The performance of the proposed method was evaluated by comparing the results obtained using symmetrical uncertainty, a machine learning similarity assessment method. The most reliable gridded data was used to assess the spatial distribution of global warming-induced unidirectional trends in thermal bioclimatic indicators (TBI) using a modified Mann-Kendall test. Ranking of the products revealed Climate Prediction Center (CPC) temperature as most efficient in reconstruction observed temperature, followed by TerraClimate and Climate Research Unit. The ranking of the product was consistent with that obtained using SU. Assessment of TBI trends using CPC data revealed an increase in the T_min in the coldest month over the whole basin at a rate of 0.03-0.08 °C per decade, except in the east. Besides, an increase in diurnal temperature range and isothermally increased in the east up to 0.2 °C and 0.6% per decade, respectively. The results revealed negative implications of thermal bioclimatic change on water, ecology, and public health in the eastern mountainous region and positive impacts on vegetation in the west and northwest.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s00477-022-02172-8.</p>","PeriodicalId":520782,"journal":{"name":"Stochastic environmental research and risk assessment : research journal","volume":" ","pages":"2919-2939"},"PeriodicalIF":4.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8769093/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39718494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of supervised principal component analysis for global sensitivity analysis of models with correlated inputs.","authors":"Mohammad Ali Mohammad Jafar Sharbaf,&nbsp;Mohammad Javad Abedini","doi":"10.1007/s00477-021-02158-y","DOIUrl":"https://doi.org/10.1007/s00477-021-02158-y","url":null,"abstract":"<p><p>Global Sensitivity Analysis (GSA) plays a significant role in quantifying the tangible impact of model inputs on the uncertainty of response variable. As GSA results are strongly affected by correlated inputs, several studies have considered this issue, but most of them are computationally expensive, labor-intensive, and difficult to implement. Accordingly, this paper puts forward a novel regression-based strategy based on the Supervised Principal Component Analysis (SPCA), benefiting from the Reproducing Kernel Hilbert Space. Indeed, by conducting one kind of variance-based sensitivity analysis, a renowned method exclusively customized for models with orthogonal inputs, on SPCA regression, the impact of the correlation structure of input variables is considered. The ability of the suggested technique is evaluated with five test cases as well as three hydrologic and hydraulic models, and the results are compared with those obtained from the correlation ratio method; Taken as a benchmark solution, which is a robust but quite complicated approach in terms of programming. It is found that the proposed method satisfactorily identifies the sensitivity ordering of model inputs. Furthermore, it is proved in this study that the performance of the proposed approach is also supported by the total contribution index in the derived covariance decomposition equation. Moreover, the proposed method compared with the correlation ratio method, is found to be computationally efficient and easy to implement. Overall, the proposed scheme is appropriate for high dimensional, quite strong nonlinear or expensive models with correlated inputs, whose coefficient of determination between the original model and regression-based SPCA model is larger than 0.33.</p>","PeriodicalId":520782,"journal":{"name":"Stochastic environmental research and risk assessment : research journal","volume":" ","pages":"2789-2818"},"PeriodicalIF":4.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8787458/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39748881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Autoregressive count data modeling on mobility patterns to predict cases of COVID-19 infection.","authors":"Jing Zhao,&nbsp;Mengjie Han,&nbsp;Zhenwu Wang,&nbsp;Benting Wan","doi":"10.1007/s00477-022-02255-6","DOIUrl":"https://doi.org/10.1007/s00477-022-02255-6","url":null,"abstract":"<p><p>At the beginning of 2022 the global daily count of new cases of COVID-19 exceeded 3.2 million, a tripling of the historical peak value reported between the initial outbreak of the pandemic and the end of 2021. Aerosol transmission through interpersonal contact is the main cause of the disease's spread, although control measures have been put in place to reduce contact opportunities. Mobility pattern is a basic mechanism for understanding how people gather at a location and how long they stay there. Due to the inherent dependencies in disease transmission, models for associating mobility data with confirmed cases need to be individually designed for different regions and time periods. In this paper, we propose an autoregressive count data model under the framework of a generalized linear model to illustrate a process of model specification and selection. By evaluating a 14-day-ahead prediction from Sweden, the results showed that for a dense population region, using mobility data with a lag of 8 days is the most reliable way of predicting the number of confirmed cases in relative numbers at a high coverage rate. It is sufficient for both of the autoregressive terms, studied variable and conditional expectation, to take one day back. For sparsely populated regions, a lag of 10 days produced the lowest error in absolute value for the predictions, where weekly periodicity on the studied variable is recommended for use. Interventions were further included to identify the most relevant mobility categories. Statistical features were also presented to verify the model assumptions.</p>","PeriodicalId":520782,"journal":{"name":"Stochastic environmental research and risk assessment : research journal","volume":" ","pages":"4185-4200"},"PeriodicalIF":4.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9223272/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40407523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the association between COVID-19 vaccination levels and incidence and lethality rates at a regional scale in Spain.","authors":"Álvaro Briz-Redón,&nbsp;Ángel Serrano-Aroca","doi":"10.1007/s00477-021-02166-y","DOIUrl":"https://doi.org/10.1007/s00477-021-02166-y","url":null,"abstract":"<p><p>The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the coronavirus disease 2019 (COVID-19), has led to the deepest global health and economic crisis of the current century. This dramatic situation has forced the public health authorities and pharmaceutical companies to develop anti-COVID-19 vaccines in record time. Currently, almost 80% of the population are vaccinated with the required number of doses in Spain. Thus, in this paper, COVID-19 incidence and lethality rates are analyzed through a segmented spatio-temporal regression model that allows studying if there is an association between a certain vaccination level and a change (in mean) in either the incidence or the lethality rates. Spatial dependency is included by considering the Besag-York-Mollié model, whereas natural cubic splines are used for capturing the temporal structure of the data. Lagged effects between the exposure and the outcome are also taken into account. The results suggest that COVID-19 vaccination has not allowed yet (as of September 2021) to observe a consistent reduction in incidence levels at a regional scale in Spain. In contrast, the lethality rates have displayed a declining tendency which has associated with vaccination levels above 50%.</p>","PeriodicalId":520782,"journal":{"name":"Stochastic environmental research and risk assessment : research journal","volume":" ","pages":"2941-2948"},"PeriodicalIF":4.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8727484/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39888950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling air quality level with a flexible categorical autoregression.","authors":"Mengya Liu,&nbsp;Qi Li,&nbsp;Fukang Zhu","doi":"10.1007/s00477-021-02164-0","DOIUrl":"https://doi.org/10.1007/s00477-021-02164-0","url":null,"abstract":"<p><p>To study urban air quality, this paper proposes a novel categorical time series model, which is based on a linear combination of bounded Poisson distribution and discrete distribution to describe the dynamic and systemic features of air quality, respectively. Daily air quality level data of three major cities in China, including Beijing, Shanghai and Guangzhou, are analyzed. It is concluded that the air quality in Beijing is the worst among the three cities but is gradually improving, and its dynamics is also the most pronounced. Theoretically, the design of our model increases the flexibility of the probabilistic structure while ensuring a dynamic feedback mechanism without high computational stress. We estimate the parameters through an adaptive Bayesian Markov chain Monte Carlo sampling scheme and show the satisfactory finite sample performance of the model through simulation studies.</p>","PeriodicalId":520782,"journal":{"name":"Stochastic environmental research and risk assessment : research journal","volume":" ","pages":"2835-2845"},"PeriodicalIF":4.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8730310/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39898269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of robustness of hybrid artificial neural network with artificial bee colony and firefly algorithm in predicting COVID-19 new cases: case study of Iran.","authors":"Mohammad Javad Shaibani,&nbsp;Sara Emamgholipour,&nbsp;Samira Sadate Moazeni","doi":"10.1007/s00477-021-02098-7","DOIUrl":"https://doi.org/10.1007/s00477-021-02098-7","url":null,"abstract":"<p><p>As an ongoing public health menace, the novel coronavirus pandemic has challenged the world. With several mutations and a high transmission rate, the virus is able to infect individuals in an exponential manner. At the same time, Iran is confronted with multiple wave peaks and the health care system is facing a major challenge. In consequence, developing a robust forecasting methodology can assist health authorities for effective planning. In that regard, with the help of Artificial Neural Network-Artificial Bee Colony (ANN-ABC) and Artificial Neural Network- Firefly Algorithm (ANN-FA) as two robust hybrid artificial intelligence-based models, the current study intends to select the optimal model with the maximum accuracy rate. To do so, first a sample of COVID-19 confirmed cases in Iran ranging from 19 February 2020 to 25 July 2021 is compiled. 75% (25%) of total observation is randomly allocated as training (testing) data. Afterwards, an ANN model is trained with Levenberg-Marquardt algorithm. Accordingly, based on R-squared and root-mean-square error criteria, the optimal number of hidden neurons is computed as 17. The proposed ANN model is employed to develop ANN-ABC and ANN-FA models for achieving the maximum accuracy rate. According to ANN-ABC, the R- squared values of the optimal model are 0.9884 and 0.9885 at train and test stages. In respect to ANN-FA, the R-squared ranged from 0.9954 to 0.9940 at the train and test phases, which indicates the outperformance of ANN-FA for predicting COVID-19 new cases in Iran. Finally, the proposed ANN-ABC and ANN-FA are applied for simulating the COVID-19 new cases data in different countries. The results revealed that both models can be used as a robust predictor of COVID-19 data and in a majority of cases ANN-FA outperforms the ANN-ABC.</p>","PeriodicalId":520782,"journal":{"name":"Stochastic environmental research and risk assessment : research journal","volume":" ","pages":"2461-2476"},"PeriodicalIF":4.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8481113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39487457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of short-lived climate forced sulfur dioxide in Tehran, Iran, using machine learning analysis.","authors":"Faezeh Borhani,&nbsp;Majid Shafiepour Motlagh,&nbsp;Yousef Rashidi,&nbsp;Amir Houshang Ehsani","doi":"10.1007/s00477-021-02167-x","DOIUrl":"https://doi.org/10.1007/s00477-021-02167-x","url":null,"abstract":"<p><p>This paper presents a time-series analysis of SO₂ air concentration and the effects of particulates (either PM_2.5 and PM₁₀) concentrations and meteorological conditions (relative humidity and wind speed) on SO₂ trend in Tehran for the period from 2011 to 2020. The source data were obtained from 21 monitoring stations of Air Quality Control Company and meteorological stations in Tehran. To predict the status of future concentration of SO₂, PM_2.5 and PM₁₀, a Box-Jenkins ARIMA approach was used to model the monthly time series. Considering the whole period of ten years, a somewhat downward trend was noted for SO₂ air concentration, even though a slight rising trend was observed in 2020 year. Monthly sulfur dioxide concentrations showed the lowest value in June and the highest value in January. Seasonal concentrations were lowest in spring and highest in winter. Then, in the ArcGIS software, the IDW method was used to obtain air pollution zoning maps. As a result, the highest average concentration of SO₂ occurred in the north and southwest of Tehran. In the last step, Relations between the SO₂ concentration and particulate matters and relative humidity and wind speed were calculated statistically using the daily average data. We finally concluded that the combined effect of particulate matters and relative humidity with the increasing role of Sulfur dioxide overcomes the decreasing role of wind speed. This study can contribute to a better understanding of the SO₂ air pollution in Tehran affected by meteorological conditions and the rapid urbanization and industrialization, followed by the possible combustion of fuel oil in power plants and health problems.</p>","PeriodicalId":520782,"journal":{"name":"Stochastic environmental research and risk assessment : research journal","volume":" ","pages":"2847-2860"},"PeriodicalIF":4.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8741550/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39701918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using data-driven algorithms for semi-automated geomorphological mapping.","authors":"Elisa Giaccone,&nbsp;Fabio Oriani,&nbsp;Marj Tonini,&nbsp;Christophe Lambiel,&nbsp;Grégoire Mariéthoz","doi":"10.1007/s00477-021-02062-5","DOIUrl":"https://doi.org/10.1007/s00477-021-02062-5","url":null,"abstract":"<p><p>In this paper, we compare the performance of two data-driven algorithms to deal with an automatic classification problem in geomorphology: Direct Sampling (DS) and Random Forest (RF). The main goal is to provide a semi-automated procedure for the geomorphological mapping of alpine environments, using a manually mapped zone as training dataset and predictor variables to infer the classification of a target zone. The applicability of DS to geomorphological classification was never investigated before. Instead, RF based classification has already been applied in few studies, but only with a limited number of geomorphological classes. The outcomes of both approaches are validated by comparing the eight detected classes with a geomorphological map elaborated on the field and considered as ground truth. Both DS and RF give satisfactory results and provide similar performances in term of accuracy and Cohen's Kappa values. The map obtained with RF presents a noisier spatial distribution of classes than when using DS, because DS takes into account the spatial dependence of the different classes. Results suggest that DS and RF are both suitable techniques for the semi-automated geomorphological mapping in alpine environments at regional scale, opening the way for further improvements.</p>","PeriodicalId":520782,"journal":{"name":"Stochastic environmental research and risk assessment : research journal","volume":" ","pages":"2115-2131"},"PeriodicalIF":4.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9463315/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40356019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatio-temporal estimation of wind speed and wind power using extreme learning machines: predictions, uncertainty and technical potential.","authors":"Federico Amato,&nbsp;Fabian Guignard,&nbsp;Alina Walch,&nbsp;Nahid Mohajeri,&nbsp;Jean-Louis Scartezzini,&nbsp;Mikhail Kanevski","doi":"10.1007/s00477-022-02219-w","DOIUrl":"https://doi.org/10.1007/s00477-022-02219-w","url":null,"abstract":"<p><p>With wind power providing an increasing amount of electricity worldwide, the quantification of its spatio-temporal variations and the related uncertainty is crucial for energy planners and policy-makers. Here, we propose a methodological framework which (1) uses machine learning to reconstruct a spatio-temporal field of wind speed on a regular grid from spatially irregularly distributed measurements and (2) transforms the wind speed to wind power estimates. Estimates of both model and prediction uncertainties, and of their propagation after transforming wind speed to power, are provided without any assumptions on data distributions. The methodology is applied to study hourly wind power potential on a grid of <math><mrow><mn>250</mn> <mo>×</mo> <mn>250</mn></mrow> </math>  m <math><msup><mrow></mrow> <mn>2</mn></msup> </math> for turbines of 100 m hub height in Switzerland, generating the first dataset of its type for the country. We show that the average annual power generation per turbine is 4.4 GWh. Results suggest that around 12,000 wind turbines could be installed on all 19,617 km <math><msup><mrow></mrow> <mn>2</mn></msup> </math> of available area in Switzerland resulting in a maximum technical wind potential of 53 TWh. To achieve the Swiss expansion goals of wind power for 2050, around 1000 turbines would be sufficient, corresponding to only 8% of the maximum estimated potential.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s00477-022-02219-w.</p>","PeriodicalId":520782,"journal":{"name":"Stochastic environmental research and risk assessment : research journal","volume":" ","pages":"2049-2069"},"PeriodicalIF":4.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9463360/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40355576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}