Stochastic Environmental Research and Risk Assessment最新文献

{"title":"Failure analysis in smart grid solar integration using an extended decision-making-based FMEA model under uncertain environment","authors":"Mohammad Reza Maghami, Sahand Vahabzadeh, Arthur Guseni Oliver Mutambara, Saeid Jafarzadeh Ghoushchi, Chandima Gomes","doi":"10.1007/s00477-024-02764-6","DOIUrl":"https://doi.org/10.1007/s00477-024-02764-6","url":null,"abstract":"<p>Failures in the integration of solar energy into smart grids can have significant implications for energy reliability and environmental sustainability, resulting in a greater dependence on conventional energy sources and increased carbon emissions. These failures can impact system functionality, efficiency, and long-term cost savings. Therefore, failure analysis plays a crucial role in identifying the underlying causes, devising appropriate solutions, and enhancing the performance of solar integration within smart grid systems. The conventional method of failure mode and effects analysis (FMEA) is widely utilized to identify failure modes in various processes. However, the Risk Priority Number (RPN) scoring system employed in FMEA has faced criticism due to its limitations. To overcome this challenge, our hybrid FMEA approach integrates cost and time considerations into the RPN calculation, thereby enhancing the assessment of failure factors. In the second step of our methodology, we utilize the Spherical Fuzzy Step-Wise Weight Assessment Ratio Analysis (SF-SWARA) technique and expert insights to determine the weightage of the five underlying factors. Lastly, in the third phase, we propose the Spherical Fuzzy Weighted Aggregated Sum Product Assessment (SF-WASPAS) method to prioritize risks based on the outcomes of the previous phases, while taking into account the uncertainty in the determinants and assigning varying weights to them. According to SF-WASPAS, the highest-rated failure is connectivity and cybersecurity, underscoring the critical importance of ensuring secure and reliable connections in solar systems. Additionally, the FMEA results indicate that overheating or fire ranks as the most significant risk, emphasizing the need for effective fire prevention and mitigation strategies.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"11 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141614965","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":"Forecasting short- and medium-term streamflow using stacked ensemble models and different meta-learners","authors":"Francesco Granata, Fabio Di Nunno","doi":"10.1007/s00477-024-02760-w","DOIUrl":"https://doi.org/10.1007/s00477-024-02760-w","url":null,"abstract":"<p>Streamflow forecasting holds a pivotal role in the effective management of water resources, flood control, hydropower generation, agricultural planning, and environmental conservation.</p><p>This study assessed the effectiveness of a stacked Multilayer Perceptron-Random Forest (MLP-RF) ensemble model for short- to medium-term (7 to 15 days ahead) daily streamflow forecasts in the UK. The stacked model combines MLP and RF, enhancing generalization by capturing complex nonlinear relationships and robustness to noisy data. Stacking reduces bias and variance by aggregating predictions and addressing differing sources of bias and variance in MLP and RF. Furthermore, this ensemble model is computationally inexpensive. The study also examined the impact of different meta-learner algorithms, Elastic Net (EN), Isotonic Regression (IR), Pace Regression (PR), and Radial Basis Function (RBF) Neural Networks, on model performance.</p><p>For 1-day ahead forecasts, all models performed well (Kling Gupta efficiency, KGE, from 0.921 to 0.985, mean absolute percentage error, MAPE, from 3.59 to 13.02%), with minimal impact from the choice of meta-learner. At 7-day ahead forecasts, satisfactory results were obtained (KGE from 0.876 to 0.963, MAPE from 11.53 to 24.55%), while at the 15-day horizon, accuracy remained reasonable (KGE from 0.82 to 0.961, MAPE from 18.31 to 34.38%). The RBF meta-learner generally led to more accurate predictions, particularly affecting low and peak flow rates. RBF consistently outperformed in predicting low flow rates, while EN excelled in predicting flood flow rates in many cases. For estimating total discharged water volume, all models exhibited low relative error (&lt; 0.08).</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"15 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141614963","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":"Quantifying the stochastic trends of climate extremes over Yemen: a comprehensive assessment using ERA5 data","authors":"Ali Salem Al-Sakkaf, Jiahua Zhang, Fengmei Yao, Mohammed Magdy Hamed, Ali R. Al-Aizari, Abdulkarem Qasem Dammag, Yousef A. Al-Masnay, Fursan Thabit, Shamsuddin Shahid","doi":"10.1007/s00477-024-02772-6","DOIUrl":"https://doi.org/10.1007/s00477-024-02772-6","url":null,"abstract":"<p>Climate change is worsening existing vulnerabilities in developing countries such as Yemen. This study examined the spatial distribution trends of extreme climate indices defined by ETCCDI (Expert Team on Climate Change Detection and Indices), for precipitation and temperature, from 1988 to 2021. It employed both the classical Mann–Kendall (MK) test as well as its modified (MMK) version that accounts for long-term persistence in hydroclimatic time series, that could otherwise impact the significance of the identified trends. It represents the first country-level investigation of climate extremes in Yemen using ERA5 reanalysis data to overcome the limitations of station data. Results found widespread increases in temperature indices, indicating significant warming nationwide. Minimum temperatures amplified more than maximums, particularly TNn (the minimum of the minimum temperature), with an increasing trend of more than 0.7℃ per decade. Inland cities exhibited more substantial warming than coastal cities. Precipitation trends displayed higher spatial variability, with intensity indices declining across most areas, raising drought concerns. However, Socotra Island presents an exception, with increased precipitation intensity and heightened flood risks. Furthermore, spatial heterogeneity in precipitation indices underscored Yemen’s complex terrain. Fewer trends were significant when applying the MMK test versus MK, confirming the impact of climate variability over the region. This research identifies the most climate-vulnerable regions to prioritise focused adaptation actions. Adaptation strategies are urgently needed, including efficient irrigation, flood assessments for Socotra Island, and investigation of projected climate changes and their implications under diverse topographic and climatic influences.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"4 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141614966","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":"Impact of climate and weather extremes on soybean and wheat yield using machine learning approach","authors":"Mamta Kumari, Abhishek Chakraborty, Vishnubhotla Chakravarathi, Varun Pandey, Parth Sarathi Roy","doi":"10.1007/s00477-024-02759-3","DOIUrl":"https://doi.org/10.1007/s00477-024-02759-3","url":null,"abstract":"<p>The escalating climate instability and extreme weather events significantly jeopardize food security. The study assessed the impact of long-term climatic variables and extreme weather events on soybean and wheat yields in rainfed central India. To address inherent spatial variability, the study area was divided into homogeneous zones based on rainfall and soil parameters. Crop yields were correlated with a comprehensive set of driving variables at seasonal and monthly scales within each zone. Machine learning algorithms, including Random Forest Regression (RFR) and Neural Networks (NN), were employed to analyze crop yield anomalies caused by climate and weather extremes. The Sobol’ index was utilized for global sensitivity analysis to identify key parameters. Results showed significant negative correlations between thermo-meteorological parameters and yields of both monsoon soybean and winter wheat across multiple districts. Soybean yield exhibited a notable positive correlation with hydro-meteorological parameters, while wheat yield displayed a significant positive correlation with cold temperature extremes. RFR and NN demonstrated similar performance, with Root Mean Square Error (RMSE) values ranging from 0.27 to 0.39 t/ha for soybean and 0.4 to 0.6 t/ha for wheat. The Sobol’ index highlighted the high sensitivity of soybean yield to rainfall and rainy days during July and August, corresponding to the crop development and flowering stages. In contrast, wheat yield was primarily influenced by temperature extremes, particularly cold nights and hot days during the reproductive-maturity stage. These crop- and growth-stage-specific analyses of meteorological parameters are essential for devising effective strategies to adapt and mitigate climate emergencies.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"2011 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141610900","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":"Assessment of loss of life caused by dam failure based on fuzzy theory and hybrid random forest model","authors":"Qiaogang Yin, Yanlong Li, Ye Zhang, Lifeng Wen, Lei She, Xinjian Sun","doi":"10.1007/s00477-024-02771-7","DOIUrl":"https://doi.org/10.1007/s00477-024-02771-7","url":null,"abstract":"<p>Dam failure may lead to significant casualties among downstream residents. Therefore, it is crucial to study a reliable method to quantitatively assess the loss of life (<i>LOL</i>) caused by dam failure for emergency response to dam failure incidents. Based on a statistical analysis of typical dam failure accidents in China and the research on the formation mechanism of <i>LOL</i>, the study quantified the factors influencing <i>LOL</i> using fuzzy theory and constructed a quantitative database for the <i>LOL</i>. Then, it proposed an innovative algorithm integrating the grey wolf optimization (GWO) algorithm and the random forest (RF) model. Finally, a data-driven assessment model for the <i>LOL</i> caused by dam failure was developed by combining the gray correlation analysis of the factors. The performance of the GWO-RF model was validated using a dataset of the <i>LOL</i> caused. The proposed model was used to assess the <i>LOL</i> in typical dam failure events. The results indicate that the model has higher accuracy, with an average absolute error of approximately 945 persons, significantly lower than 2529 persons in the Graham method. Thus, it can effectively estimate the <i>LOL</i> caused by dam failure. This study developed a novel method for quantitatively assessing the <i>LOL</i> caused by dam failure, which could also serve as a reference for modeling disaster consequences in other fields.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"21 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141587936","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":"Joint identification of contaminant source and dispersion coefficients based on multi-observed reconstruction and ensemble Kalman filtering","authors":"Li Jing, Jun Kong, Mingjie Pan, Tong Zhou, Teng Xu","doi":"10.1007/s00477-024-02767-3","DOIUrl":"https://doi.org/10.1007/s00477-024-02767-3","url":null,"abstract":"<p>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.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"6 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141568575","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":"Image classification for sub-surface crack identification in concrete dam based on borehole CCTV images using deep dense hybrid model","authors":"Qianwei Dai, Muhammad Ishfaque, Saif Ur Rehman Khan, Yu-Long Luo, Yi Lei, Bin Zhang, Wei Zhou","doi":"10.1007/s00477-024-02743-x","DOIUrl":"https://doi.org/10.1007/s00477-024-02743-x","url":null,"abstract":"<p>The research investigates the significance of identifying structure discontinuities, such as cracks, in concrete dams to ensure dam safety and stability. A novel automatic image classification method is developed, employing Deep Dense Transfer Learning (DDTL) with pre-trained models, including EfficientNetB1, ResNet50, and a hybrid model to identify the detection of cracks in sub-surfaces at pillow dams in Sichuan province, China. The developed model was trained, validated, and tested, with the Hybrid model demonstrating superior performance. The results showed that the DDTL models had high classification accuracies, surpassing Convolutional identification techniques for sub-surface cracks. Consequently, this study suggests that automatic image classification techniques can effectively identify and localize structural defects in concrete dams. This is an innovative approach to predicting normal borehole images and crack recognition using CCTV borehole images.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"23 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141568576","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":"An analysis framework for stationary and nonstationary sediment load frequency in a changing climate","authors":"Xi Yang, Min Qin, Zhihe Chen","doi":"10.1007/s00477-024-02763-7","DOIUrl":"https://doi.org/10.1007/s00477-024-02763-7","url":null,"abstract":"<p>Non-stationary sediment load analysis is critical for river engineering design and water resource management. Traditional sediment load frequency analysis methods usually assume stationarity, which can lead to inconsistent results in a changing environment because they cannot account for factors such as time variations. Here, we use generalized additive models for location, scale and shape (GAMLSS) to establish non-stationary models with time, precipitation and streamflow as covariates (named Model 1 and Model 2, respectively), and compare their fitting effects with stationary models (parameters unchanged: Model 0). In this study, the sediment load of the Jinsha River Basin in southwest China was analyzed. Outcomes indicate that: (1) the research area's sediment load decreased significantly, with a significant change point in 2002 (<i>p</i> &lt; 0.1); (2) the goodness of fit indices (global fitting deviation: GD, AIC criterion and SBC criterion) based on Model 2 are smaller than the values of the other two models. The other two models' sediment load quantile design values are within Model 2's range. (3) Compared with Model1, precipitation and streamflow as covariates in Model 2 are more able to capture the non-stationary features of sediment load frequency. Furthermore, Model 2 can more accurately forecast future changes in sediment load when external physical factors are considered. The findings of this research can serve as a scientific foundation for decision makers to carry out water conservancy planning and design and river management and development.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"14 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505111","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":"Utilizing artificial intelligence techniques for soil depth prediction and its influences in landslide hazard modeling","authors":"Ananta Man Singh Pradhan, Suchita Shrestha, Jung-Hyun Lee, In-Tak Hwang, Hyuck-Jin Park","doi":"10.1007/s00477-024-02765-5","DOIUrl":"https://doi.org/10.1007/s00477-024-02765-5","url":null,"abstract":"<p>Soil depth plays a pivotal role in determining hillslope stability, understanding hydrogeology, promoting optimal vegetation growth, and comprehensively elucidating soil erosion dynamics. In this study, two robust artificial intelligence methodologies, quantile regression forest (QRF) and deep neural network (DNN), were employed to predict spatial variations in soil depth across a digital terrain. Particularly during periods of intense rainfall, shallow landslides pose recurrent threats to human safety and property integrity. Thus, the identification of potential landslide-prone regions becomes imperative for mitigating associated risks. During slope stability analyses, soil depth assumes significance; nonetheless, data regarding soil depth from areas prone to landslides are rarely obtained. The main objective of this study is to explore the impact of incorporating soil depth spatial distributions on the predictive capabilities of shallow landslide model within a given terrain. By leveraging two distinct spatial soil depth distributions, a comprehensive analysis of slope stability analysis was conducted. The significance of soil depth spatial distribution, particularly when employing DNN-generated data, is underscored in refining predictions and preventing overestimations of landslide-prone or stable regions. Notably, integration of DNN-derived soil depth data into the infinite slope model yielded a marked enhancement in the accuracy of factor of safety (FS) distributions, achieving an impressive 86.9% accuracy rate while QRF-derived FS has shown 74.7% accuracy. This analytical approach, while straightforward, offers a powerful tool for evaluating slope instability and forecasting shallow landslides, thereby facilitating proactive mitigation measures.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"30 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141529211","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":"Vector-valued Gaussian processes on non-Euclidean product spaces: constructive methods and fast simulations based on partial spectral inversion","authors":"Xavier Emery, Nadia Mery, Emilio Porcu","doi":"10.1007/s00477-024-02755-7","DOIUrl":"https://doi.org/10.1007/s00477-024-02755-7","url":null,"abstract":"<p>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.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"80 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505139","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}