Stochastic Environmental Research and Risk Assessment最新文献

{"title":"Clustering of temporal profiles in US climate change data using logistic mixture of spatial multivariate linear models","authors":"Seonwoo Lee, Keunbaik Lee, Ju-Hyun Park, Minjung Kyung, Seong-Taek Yun, Jieun Lee, Yongsung Joo","doi":"10.1007/s00477-024-02779-z","DOIUrl":"https://doi.org/10.1007/s00477-024-02779-z","url":null,"abstract":"<p>In recent decades, the annual mean temperature has increased, with unusual alternations of hot and cold years. In addition, the changes in temporal precipitation patterns are caused by complex interactions between temperature change, the global water cycle, and other components of the Earth’s systems. To construct a statistical model of these temporal patterns in terms of temperature and precipitation, we propose a logistic mixture of spatial multivariate penalized regression splines for temporal profiles and apply this model to the contiguous United States climate data over 123 years (1900 to 2022) at 252 weather stations. The results reveal that the proposed model identifies climatologically meaningful clusters of weather stations in the contiguous United States with two important meteorological variables, temperature and precipitation, identifying the climate change patterns of each climate zone. The surface air temperature increased in the Northeast and West (Mountain and Pacific) regions, where the climate is affected by the continental Arctic air. A notable increment of precipitation also occurred in the Northeast. In contrast, the South region, where the climate is affected by the tropical Atlantic Ocean, is more stable than other regions in terms of year-to-year variations in temperature and precipitation.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"2 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning models for multi-step prediction of water levels incorporating meteorological variables and historical data","authors":"Lingxuan Chen, Zhaocai Wang, Ziang Jiang, Xiaolong Lin","doi":"10.1007/s00477-024-02766-4","DOIUrl":"https://doi.org/10.1007/s00477-024-02766-4","url":null,"abstract":"<p>Precise multi-step water level predictions are crucial for managing water resources and mitigating the effects of extreme weather. This study introduces a novel approach by integrating Variational Mode Decomposition (VMD), Whale Optimization Algorithm (WOA), and Long Short-Term Memory (LSTM) to forecast variations in water levels, employing both endogenous and exogenous environmental variables. Furthermore, this research proposes two additional fusion algorithms, each possessing unique potential for enhancement: Multivariate Long Short-Term Memory (MLSTM) and an advancement in the Residual Sequence (RESID). The predictive accuracy of these diverse algorithms is assessed using data from the water levels in Jinan Baotu Spring, China. The findings indicate that the VMD-WOA-LSTM model presents the most robust results for both long-term and short-term predictions. For multi-step, ultra-short-term forecasts, VMD-WOA-MLSTM proves to be a pragmatic algorithm. However, the refined algorithm that incorporates RESID does not significantly improve and, indeed, may diminish prediction accuracy. Conclusively, the VMD-WOA-LSTM, exemplifying a data-driven predictive algorithm, boasts high accuracy and demonstrates versatility in water level forecasting across various scenarios.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"49 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid EMD and MODWT models for monthly precipitation forecasting using an innovative error decomposition method","authors":"Laleh Parviz, Mansour Ghorbanpour","doi":"10.1007/s00477-024-02797-x","DOIUrl":"https://doi.org/10.1007/s00477-024-02797-x","url":null,"abstract":"<p>The accurate prediction of precipitation is crucial for agricultural management, water resources planning, and drought monitoring. One effective approach involves using a combination of linear and nonlinear models in a hybrid system. This study focuses on enhancing the hybrid model by employing the signal decomposition method, particularly for the complex nonlinear component. The research evaluated the effectiveness of incorporating seasonal autoregressive integrated moving average (SARIMA) with empirical mode decomposition (EMD) and maximal overlap discrete wavelet transform (MODWT) methods in the hybrid model structure using monthly precipitation data from stations in Iran. The procedure involved obtaining error series from the SARIMA model, decomposing the error series into intrinsic mode functions (IMFs) using EMD, and then applying support vector regression to forecast them. The evaluation criteria showed that using EMD in the hybrid model structure enhanced its efficiency by reducing significant error criteria and increasing residual predictive deviation. The proposed model also preserved precipitation forecasts in terms of time, with overestimated forecasts exhibiting high efficiency (RPD values &gt; 2.5). Additionally, incorporating MODWT as a secondary decomposition in the final step of the proposed model further improved precipitation forecasting accuracy compared to the hybrid model solely incorporating EMD. The assimilation of signal decomposition methods in a hybrid model can enhance the accuracy and reliability of precipitation forecasts by revealing important error patterns.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"8 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trend analysis of greening and browning in Hyrcanian forests and their responses to climate change","authors":"Ahmad Abbasnezhad Alchin, Ali Asghar Darvishsefat, Vahid Nasiri, Jarosław Socha","doi":"10.1007/s00477-024-02794-0","DOIUrl":"https://doi.org/10.1007/s00477-024-02794-0","url":null,"abstract":"<p>Recognizing the impact of climate change on the temporal and spatial variations in forests is crucial for sustaining them in the face of climate change. Here, we aimed to: (1) analyses the greening and browning trends in HFs based on time-series VIs, focusing on foliage trends observable through remote sensing; (2) explore the temporal and spatial trends of climatic factors; and (3) identify the relationship between the greening and browning of the forests and climate change. In this regard, we generated an 18-year (2003–2020) time series with an 8-day temporal resolution, encompassing MODIS vegetation indices (EVI and NDVI) and four climatic and hydrological factors: day and night temperature (LSTd, LSTn), precipitation (PRE), and actual evapotranspiration (ET). Subsequently, we used spatial statistical methods for analysis. EVI and NDVI trend analyses over the study period revealed greening in 77.02% and 92.32% of the study area, respectively. The statistical test confirmed significance (<i>p</i> &lt; 0.05) for this greening in around 41.59% (EVI trend) and 75.11% (NDVI trend). Regarding the climatic and hydrological factors, PRE exhibited a declining trend, whereas LSTd, LSTn, and ET showed an increasing trend. Conclusively, the results reveal a positive correlation, ranging between 0.7 and 0.9, between temperature (LSTd and LSTn) and vegetation indices, indicating a close association between the greening process in HFs and rising temperatures (LSTd and LSTn). These results contribute to the understanding of the ecological resilience of HFs, aiding in the development of strategies to enhance ecosystems’ resilience in the face of climate change.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"60 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulating future hydrological droughts and sediment yield by integrating different climate scenarios for a semiarid basin in Brazil","authors":"Tatiane Gomes Frade, Celso Augusto Guimarães Santos, Richarde Marques da Silva","doi":"10.1007/s00477-024-02777-1","DOIUrl":"https://doi.org/10.1007/s00477-024-02777-1","url":null,"abstract":"<p>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, <i>NSE</i> = 0.82 for calibration and R² = 0.89, <i>NSE</i> = 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.</p><h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"20 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141969766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in weather forecasting for precision agriculture: From statistical modeling to transformer-based architectures","authors":"Chouaib El Hachimi, Salwa Belaqziz, Saïd Khabba, Bouchra Ait Hssaine, Mohamed Hakim Kharrou, Abdelghani Chehbouni","doi":"10.1007/s00477-024-02778-0","DOIUrl":"https://doi.org/10.1007/s00477-024-02778-0","url":null,"abstract":"<p>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).</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"59 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic modeling of extreme events involving decaying variables with an application in seismology","authors":"Muhammad Mohsin, Salman Abbas, Muhammad Mubeen Khan","doi":"10.1007/s00477-024-02787-z","DOIUrl":"https://doi.org/10.1007/s00477-024-02787-z","url":null,"abstract":"<p>Natural hazards are the extreme events that significantly distress life on Earth. To mitigate the detrimental impacts of these extreme events, it is essential to examine and model them using a probabilistic approach. Probability distributions are competent enough to analyze the exponential behavior and estimate the pattern of randomness in these real-life phenomena. We use the generalized Pareto-exponential distribution (GPED) and find it to be an appropriate model for extreme events that involve exponentially decaying variables. Interestingly, the GPED also comprises the features of both the well-known exponential and Pareto distributions and approaches several other well-known distributions after certain transformations. We derive its various probabilistic characteristics and provide an empirical study for different parametric values to observe their behavior. We follow the maximum likelihood method to estimate the unknown model parameters and conduct a simulation study for different sample sizes and different combinations of the model parameters to examine their stability. We also demonstrate the applicability of our model by using a data set from the field of seismology and establish its better performance by comparing it with some extant distributions.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"64 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inclusion of fractal dimension in machine learning models improves the prediction accuracy of hydraulic conductivity","authors":"Abhradip Sarkar, Pragati Pramanik Maity, Mrinmoy Ray, Aditi Kundu","doi":"10.1007/s00477-024-02793-1","DOIUrl":"https://doi.org/10.1007/s00477-024-02793-1","url":null,"abstract":"<p>Measurement of hydraulic conductivity (HC) in the field and laboratory is time-consuming, laborious, and expensive, pedo-transfer functions can be used to predict the soil HC using easy-to-measure soil properties like bulk density (BD), soil texture, fractal dimension (D), organic carbon (OC) and glomalin content. In this study, 121 soil samples were used to predict HC using Multi Linear Regression, and four machine learning-based models i.e., Artificial Neural Network (ANN), Support Vector Machine (SVM), Classification and Regression Trees (CART) and Random Forest (RF). Two sets of input data were used i.e., dataset 1: texture data, BD, OC, and glomalin content and dataset 2: D, BD, OC, and glomalin content (Dataset 2). The models were evaluated based on Mean Absolute Error, Mean Absolute Percentage Error, Nash–Sutcliffe model efficiency, Root Mean Square Error (RMSE), and correlation coefficient. ANN with three hidden layers performed significantly for both input sets. The RMSE value was decreased by 17% in the training dataset and by 5.55% in the testing dataset when D was added to the input set for ANN. For both datasets, RF performed better and outperformed CART in predicting HC. According to the results, SVM with dataset 2 outperformed all other models which showed the inclusion of D in the dataset could predict HC more efficiently. However, further study is required for different combinations of datasets for evaluating the prediction efficiency of machine learning models for various regions.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"12 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliability analysis of cutting slopes under rainfall conditions considering copula dependence between shear strengths","authors":"Lei-Lei Liu, Yue-Bing Xu, Wen-Qing Zhu, Khan Zallah, Lei Huang, Can Wang","doi":"10.1007/s00477-024-02789-x","DOIUrl":"https://doi.org/10.1007/s00477-024-02789-x","url":null,"abstract":"<p>Slope reliability is of great importance in geotechnical engineering, and it is susceptible to various factors, such as slope cutting and rainfall. Currently, how the copula dependence structure affects the reliability of cutting slopes under rainfall conditions is still an open question. This study investigates the influence of copula dependence structure on the reliability analysis of a real slope, considering the slope cutting and rainfall characteristics (i.e., rainfall intensity, duration, and pattern). The Gaussian, Plackett, Frank, and No.16 copulas are first employed to model the joint probability distribution of the measured soil strength parameters. The optimal copula is subsequently identified using Akaike information criterion and Bayesian information criterion. The probability of failure (<i>P</i>_<i>f</i>) and the distribution of critical slip surface (CSS) for different slope cutting and rainfall conditions are then obtained within the framework of Monte Carlo simulation. The results show that the copula dependence between shear strengths has significant influence on the <i>P</i>_<i>f</i> for the cutting slope under rainfall conditions. The commonly used Gaussian copula may underestimate the <i>P</i>_<i>f</i>, while the No.16 copula would overestimate the <i>P</i>_<i>f</i> for different slope cutting angles and rainfall intensities, durations and patterns. The differences in <i>P</i>_<i>f</i> obtained by different copula functions decrease with the increase of cutting angle, cutting distance and rainfall intensity. Furthermore, the differences in <i>P</i>_<i>f</i> obtained by different copula functions show little variations with changes in rainfall duration and pattern. Although the copula function has a significant influence on the <i>P</i>_<i>f</i>, it has negligible influence on CSS. This study provides a practical tool for the selection of copula function and valuable insights for slope design and management under slope cutting and rainfall conditions.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"1 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: Risk assessment of river water quality using long-memory processes subject to divergence or Wasserstein uncertainty","authors":"Hidekazu Yoshioka, Yumi Yoshioka","doi":"10.1007/s00477-024-02762-8","DOIUrl":"https://doi.org/10.1007/s00477-024-02762-8","url":null,"abstract":"","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"36 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141969785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}