Stochastic Environmental Research and Risk Assessment最新文献_第3页

A new single multiplicative neuron model artificial neural network based on black hole optimization algorithm: forecasting the amounts of clean water given to metropolis 基于黑洞优化算法的新型单乘法神经元模型人工神经网络：预测向大都市提供的清洁水量

IF 4.2 3区环境科学与生态学

Stochastic Environmental Research and Risk Assessment Pub Date : 2024-08-29 DOI: 10.1007/s00477-024-02802-3

Hakan Işık, Eren Bas, Erol Egrioglu, Tamer Akkan

{"title":"A new single multiplicative neuron model artificial neural network based on black hole optimization algorithm: forecasting the amounts of clean water given to metropolis","authors":"Hakan Işık, Eren Bas, Erol Egrioglu, Tamer Akkan","doi":"10.1007/s00477-024-02802-3","DOIUrl":"https://doi.org/10.1007/s00477-024-02802-3","url":null,"abstract":"An urban water demand with high accuracy and reliability plays fundamental role in creating a predictive water supply system. This is necessary to implement mechanisms and systems that can be used to estimate water demands. The paper aims to forecast the amounts of clean water given to metropolis with single multiplicative neuron model artificial neural network. For this purpose, a regular data set of Istanbul metropolis was selected as a model and utilised in the application process. Single multiplicative neuron model artificial neural network is a neural network that does not have the hidden layer unit number problem that many shallow and deep artificial neural networks have. In this study, the black hole optimization algorithm is used for the first time in the literature for the training of the single multiplicative neuron model artificial neural network. In line with the analysis results, it is concluded that the proposed new approach achieved better prediction results than the other compared methods for the time series of amounts of clean water given to metropolis.","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"21 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219805","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}

引用次数: 0

Assessing the impact of climate change and reservoir operation on the thermal and ice regime of mountain rivers using the XGBoost model and wavelet analysis 利用 XGBoost 模型和小波分析评估气候变化和水库运行对山区河流冰热机制的影响

IF 4.2 3区环境科学与生态学

Stochastic Environmental Research and Risk Assessment Pub Date : 2024-08-28 DOI: 10.1007/s00477-024-02803-2

Maksymilian Fukś, Mariola Kędra, Łukasz Wiejaczka

{"title":"Assessing the impact of climate change and reservoir operation on the thermal and ice regime of mountain rivers using the XGBoost model and wavelet analysis","authors":"Maksymilian Fukś, Mariola Kędra, Łukasz Wiejaczka","doi":"10.1007/s00477-024-02803-2","DOIUrl":"https://doi.org/10.1007/s00477-024-02803-2","url":null,"abstract":"This study presents an analysis of the influence of climatic conditions and the operation of a dam reservoir on the occurrence of ice cover and water temperature in two rivers (natural and transformed by reservoir operations) located in the Carpathian Mountains (central Europe). The analyses are based on data obtained from four hydrological and two climatological stations. The Extreme Gradient Boosting (XGBoost) machine learning model was used to quantitatively separate the effects of climate change from the effects arising from the operation of the dam reservoir. An analysis of the effects of reservoir operation on the phase synchronization between air and river water temperatures based on a continuous wavelet transform was also conducted. The analyses showed that there has been an increase in the average air temperature of the study area in November by 1.2 °C per decade (over the period 1984–2016), accompanied by an increase in winter water temperature of 0.3 °C per decade over the same period. As water and air temperatures associated with the river not influenced by the reservoir increased, there was a simultaneous reduction in the duration of ice cover, reaching nine days per decade. The river influenced by the dam reservoir showed a 1.05 °C increase in winter water temperature from the period 1994–2007 to the period 1981–1994, for which the operation of the reservoir was 65% responsible and climatic conditions were 35% responsible. As a result of the reservoir operation, the synchronization of air and water temperatures was disrupted. Increasing water temperatures resulted in a reduction in the average annual number of days with ice cover (by 27.3 days), for which the operation of the dam reservoir was 77.5% responsible, while climatic conditions were 22.5% responsible.","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"2 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219810","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}

引用次数: 0

Modeling non-stationarity in significant wave height over the Northern Indian Ocean 北印度洋显著波高的非稳态建模

IF 4.2 3区环境科学与生态学

Stochastic Environmental Research and Risk Assessment Pub Date : 2024-08-27 DOI: 10.1007/s00477-024-02775-3

P. Dhanyamol, V. Agilan, Anand KV

{"title":"Modeling non-stationarity in significant wave height over the Northern Indian Ocean","authors":"P. Dhanyamol, V. Agilan, Anand KV","doi":"10.1007/s00477-024-02775-3","DOIUrl":"https://doi.org/10.1007/s00477-024-02775-3","url":null,"abstract":"Statistical descriptions of extreme met-ocean conditions are essential for the safe and reliable design and operation of structures in marine environments. The significant wave height (({H}_{S})) is one of the most essential wave parameters for coastal and offshore structural design. Recent studies have reported that a time-varying component exists globally in the ({H}_{S}). Therefore, the non-stationary behavior of an annual maximum series of ({H}_{S}) is important for various ocean engineering applications. This study aims to analyze the frequency of ({H}_{S}) over the northern Indian Ocean by modeling the non-stationarity in the ({H}_{S}) series using a non-stationary Generalized Extreme Value (GEV) distribution. The hourly maximum ({H}_{S}) data (with a spatial resolution of 0.5° longitude × 0.5° latitude) collected from the global atmospheric reanalysis dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF) is used for the study. To model the annual maximum series of ({H}_{S}) using a non-stationary GEV distribution, two physical covariates (El-Ni (widetilde{n}) o Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)) and time covariates are introduced into the location and scale parameters of the GEV distribution. The return levels of various frequencies of ({H}_{S}) are estimated under non-stationary conditions. From the results, average increases of 13.46%, 13.66%, 13.85%, and 14.02% are observed over the study area for the 25-year, 50-year, 100-year, and 200-year return periods, respectively. A maximum percentage decrease of 33.3% and a percentage increase of 167% are observed in the return levels of various return periods. The changes in the non-stationary return levels over time highlight the importance of modeling the non-stationarity in ({H}_{S}).","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"27 4 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219809","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}

引用次数: 0

Simulation of spatial flooding disaster on urban roads and analysis of influencing factors: taking main city of Hangzhou as an example 城市道路空间洪涝灾害模拟及影响因素分析--以杭州主城区为例

IF 4.2 3区环境科学与生态学

Stochastic Environmental Research and Risk Assessment Pub Date : 2024-08-27 DOI: 10.1007/s00477-024-02796-y

Rikun Wen, Jinjing Sun, Chunling Tao, Hao Tao, Chingaipe N’tani, Liu Yang

{"title":"Simulation of spatial flooding disaster on urban roads and analysis of influencing factors: taking main city of Hangzhou as an example","authors":"Rikun Wen, Jinjing Sun, Chunling Tao, Hao Tao, Chingaipe N’tani, Liu Yang","doi":"10.1007/s00477-024-02796-y","DOIUrl":"https://doi.org/10.1007/s00477-024-02796-y","url":null,"abstract":"This study assessed the risk of urban road waterlogging and the threshold of the influencing factors using software simulation and data analysis. This study selected the road space in the main urban area of Hangzhou City from 2019 to 2021 as the research object. ArcGIS software was used to study the spatial distribution of road waterlogging points. Kernel density analysis and the Geographic Detector (GD) method were used to determine the dominant factors affecting road waterlogging. This study reveals the central clustering distribution characteristics of road waterlogging and the five-level risk zoning of disasters. The simulation results show that the highest-risk areas for road waterlogging in the main urban area of Hangzhou are distributed in Chao Wang Road, Jianguo Middle Road, Jianguo South Road, Hupao Road, Lingyin Road, Fuchunjiang Road, Moganshan Road Sect. 4, and Tianmu Mountain Road Sect. 3. The ranking of the impact factors for road waterlogging was as follows: elevation > vegetation coverage > slope > impervious surface abundance > distance from rivers. Factor threshold for worst flooding is that the elevation of < 15–20 m, a slope of < 8–10°, vegetation coverage of < 10%, and an abundance of impermeable surfaces > 60–70%. Elevation and vegetation coverage were the significant factors with the greatest impact on road space waterlogging. The combination of elevation and vegetation coverage, elevation and slope, and elevation and impervious surface abundance had a greater impact on road waterlogging than the other three combinations. All the interactions of the influencing factors had a nonlinear enhancing effect on urban road waterlogging disasters.","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"4 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219807","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}

引用次数: 0

Coupled hydrogeophysical inversion through ensemble smoother with multiple data assimilation and convolutional neural network for contaminant plume reconstruction 通过集合平滑器与多重数据同化和卷积神经网络进行耦合水文地质物理反演，以重建污染物羽流

IF 4.2 3区环境科学与生态学

Stochastic Environmental Research and Risk Assessment Pub Date : 2024-08-25 DOI: 10.1007/s00477-024-02800-5

Camilla Fagandini, Valeria Todaro, Cláudia Escada, Leonardo Azevedo, J. Jaime Gómez-Hernández, Andrea Zanini

{"title":"Coupled hydrogeophysical inversion through ensemble smoother with multiple data assimilation and convolutional neural network for contaminant plume reconstruction","authors":"Camilla Fagandini, Valeria Todaro, Cláudia Escada, Leonardo Azevedo, J. Jaime Gómez-Hernández, Andrea Zanini","doi":"10.1007/s00477-024-02800-5","DOIUrl":"https://doi.org/10.1007/s00477-024-02800-5","url":null,"abstract":"In the field of groundwater, accurate delineation of contaminant plumes is critical for designing effective remediation strategies. Typically, this identification poses a challenge as it involves solving an inverse problem with limited concentration data available. To improve the understanding of contaminant behavior within aquifers, hydrogeophysics emerges as a powerful tool by enabling the combination of non-invasive geophysical techniques (i.e., electrical resistivity tomography—ERT) and hydrological variables. This paper investigates the potential of the Ensemble Smoother with Multiple Data Assimilation method to address the inverse problem at hand by simultaneously assimilating observed ERT data and scattered concentration values from monitoring wells. A novelty aspect is the integration of a Convolutional Neural Network (CNN) to replace and expedite the expensive geophysical forward model. The proposed approach is applied to a synthetic case study, simulating a tracer test in an unconfined aquifer. Five scenarios are compared, allowing to explore the effects of combining multiple data sources and their abundance. The outcomes highlight the efficacy of the proposed approach in estimating the spatial distribution of a concentration plume. Notably, the scenario integrating apparent resistivity with concentration values emerges as the most promising, as long as there are enough concentration data. This underlines the importance of adopting a comprehensive approach to tracer plume mapping by leveraging different types of information. Additionally, a comparison was conducted between the inverse procedure solved using the full geophysical forward model and the CNN model, showcasing comparable performance in terms of results, but with a significant acceleration in computational time.","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"38 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219816","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}

引用次数: 0

Risk assessment model for dust explosion in dust removal pipelines using an attention mechanism-based convolutional neural network 利用基于注意机制的卷积神经网络建立除尘管道粉尘爆炸风险评估模型

IF 4.2 3区环境科学与生态学

Stochastic Environmental Research and Risk Assessment Pub Date : 2024-08-22 DOI: 10.1007/s00477-024-02781-5

Yang Li, Gaozhi Cui, Qinglin Han, Simeng Chen, Shuaishuai Lu

{"title":"Risk assessment model for dust explosion in dust removal pipelines using an attention mechanism-based convolutional neural network","authors":"Yang Li, Gaozhi Cui, Qinglin Han, Simeng Chen, Shuaishuai Lu","doi":"10.1007/s00477-024-02781-5","DOIUrl":"https://doi.org/10.1007/s00477-024-02781-5","url":null,"abstract":"Dust explosions occur frequently during production, transportation, and storage processes involving combustible dusts, with dust explosions caused by de-dusting systems being the most common. To prevent such accidents, we need to perform timely and accurate risk assessment. Therefore, we have developed a risk assessment model for dust explosion of dust duct deposition based on convolutional neural network with an attention mechanism (ConvNeXt-Tsc). By enhancing the ConvNeXt block and introducing an attention mechanism, we can more accurately extract the critical features related to the thickness of deposited dust in images of the ducts, achieving a model recognition accuracy of 95.15%. We have verified that the model has a high assessment accuracy in practical applications, which helps to detect potential hazards in dust ducts in time and avoid explosion accidents. The results show that the model has a wide range of application prospects in sedimentary dust explosion risk assessment, with high reliability, practicality, and scientific rigor.","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"1 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219811","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}

引用次数: 0

Urban flooding risk assessment based on the impact of land cover spatiotemporal characteristics with hydrodynamic simulation 基于水动力模拟的土地覆被时空特征影响的城市洪水风险评估

IF 4.2 3区环境科学与生态学

Stochastic Environmental Research and Risk Assessment Pub Date : 2024-08-19 DOI: 10.1007/s00477-024-02798-w

Yanfen Geng, Xiao Huang, Xinyu Hu, Yingmeng Zhong, Peng Liu

{"title":"Urban flooding risk assessment based on the impact of land cover spatiotemporal characteristics with hydrodynamic simulation","authors":"Yanfen Geng, Xiao Huang, Xinyu Hu, Yingmeng Zhong, Peng Liu","doi":"10.1007/s00477-024-02798-w","DOIUrl":"https://doi.org/10.1007/s00477-024-02798-w","url":null,"abstract":"In recent years, urban flooding has emerged as a major challenge, with land cover change identified as a key contributing factor. This study investigates the sensitivity of urban flooding risk to land cover changes. Seven urban land cover maps from different years and five different rainfall events, were selected as the examples. Based on hydrodynamic model simulations, this study analyzed the relationship between the total area of urban flooding and the proportion of ponding depths across various depth intervals and the land cover change. The study region was divided into 41 sub-areas based on road classifications and building clusters. The urban flood risk considering the aggregation of urban flooding, maximum ponding depth, the extent of the ponded area, and the average ponding depth was quantified within these sub-regions. Additionally, ten characteristic points were extracted from two sub-area with significant risk changes to analyze the logic of urban flooding risk evolution under land use change. The results indicate that: (1) There is a positive correlation between the total area of urban flooding and the proportion of high ponding depths and increasing impervious surfaces. (2) Urbanization significantly increases urban flooding risk, with 28 out of 41 areas experiencing heightened risk, including 6 sub-areas with risk increases exceeding 100%. (3) When the rainfall event changes from a 20-year to a 100-year return period, the maximum ponding depth in cropland stabilizes compared to impervious surfaces. Conversion of cropland to impervious surfaces accelerates increases in ponding depth and can lead to higher maximum ponding depths.","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"29 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219815","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}

引用次数: 0

Modelling of slope reliability analysis methods based on random field and asymmetric CNNs 基于随机场和非对称 CNN 的斜坡可靠性分析方法建模

IF 4.2 3区环境科学与生态学

Stochastic Environmental Research and Risk Assessment Pub Date : 2024-08-17 DOI: 10.1007/s00477-024-02774-4

He Jia, Sherong Zhang, Chao Wang, Xiaohua Wang

{"title":"Modelling of slope reliability analysis methods based on random field and asymmetric CNNs","authors":"He Jia, Sherong Zhang, Chao Wang, Xiaohua Wang","doi":"10.1007/s00477-024-02774-4","DOIUrl":"https://doi.org/10.1007/s00477-024-02774-4","url":null,"abstract":"To improve slope reliability calculations and address high-nonlinearity in random fields, an AI algorithm, namely Convolutional Neural Network (CNN) with asymmetric convolution is introduced. The method accounts for the interdependence and auto-correlation of soil material and uses Python-based secondary development in ABAQUS Version 6.14 to improve computational efficiency and user-friendliness in finite element simulations. A Cholesky decomposition-based centroid point method is used for random fields to simplify computation. Additionally, an asymmetric convolution-based CNN surrogate model replaces finite element simulations to address challenges such as parameter correlations and random field discretization for improved analysis efficiency. The methodology uses random field samples and safety factors as inputs and outputs for training, which improves predictability and addressing high-dimensional issues. Its effectiveness is demonstrated through case studies involving single-layer undrained saturated clay slopes and double-layer cohesive soil slopes. The results demonstrate the effectiveness of the CNN approach that utilizes asymmetric convolution, with outcomes closely resembling those obtained through finite element simulation. This method demonstrates a 95.8% improvement in time efficiency compared to software-based calculations and a 93.5% enhancement over batch calculations using ABAQUS. These results confirm the effectiveness of the introduced reliability analysis method and the ability to provide accurate results while significantly boosting computational efficiency.","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"27 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219814","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}

引用次数: 0

An improved nonlinear dynamical model for monthly runoff prediction for data scarce basins 用于数据稀缺流域月径流预测的改进型非线性动力学模型

IF 4.2 3区环境科学与生态学

Stochastic Environmental Research and Risk Assessment Pub Date : 2024-08-17 DOI: 10.1007/s00477-024-02773-5

Longxia Qian, Nanjun Liu, Mei Hong, Suzhen Dang

{"title":"An improved nonlinear dynamical model for monthly runoff prediction for data scarce basins","authors":"Longxia Qian, Nanjun Liu, Mei Hong, Suzhen Dang","doi":"10.1007/s00477-024-02773-5","DOIUrl":"https://doi.org/10.1007/s00477-024-02773-5","url":null,"abstract":"Making accurate and reliable predictions for monthly runoff in data scarce basins is still a major challenge. In this study, a new model, the CL-NDM, is developed by combining Convolutional Neural Network-Long Short-term Memory (CNN-LSTM) and a nonlinear dynamic model. The CL-NDM can overcome the deficiency of observed data by fusing spatial and temporal dependencies in runoff sequences at different stations. First, phase space reconstruction is used to enlarge the dimensions of the runoff sequences and reconstruct the attractors of the runoff sequences. Then, the CNN-LSTM is employed to construct the mapping between non-delay and delay attractors. Finally, the prediction set of the target variable is obtained by embedding multiple times. The CL-NDM is performed for monthly runoff prediction at eleven hydrological stations in the Weihe River, China. Compared with the CNN, LSTM and CNN-LSTM models, which require a large amount of training samples, the CL-NDM behaves much better, especially in situations with small training sample sizes. The maximum increase in R is 74%, and the maximum NSE is as large as 0.8. The maximum improvement in RMSE and MAPE is 53% and 88%, respectively. The CL-NDM has stronger ability to capture peak value while LSTM, CNN-LSTM and CNN models show obvious time lag in the prediction of peak point. The improved nonlinear dynamical model may provide a valuable method for runoff prediction in data-scarce regions.","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"18 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219813","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}

引用次数: 0

Stability prediction of multi-material complex slopes based on self-attention convolutional neural networks 基于自注意卷积神经网络的多材料复杂斜坡稳定性预测

IF 4.2 3区环境科学与生态学

Stochastic Environmental Research and Risk Assessment Pub Date : 2024-08-14 DOI: 10.1007/s00477-024-02792-2

Mansheng Lin, Xuedi Chen, Gongfa Chen, Zhiwei Zhao, David Bassir

{"title":"Stability prediction of multi-material complex slopes based on self-attention convolutional neural networks","authors":"Mansheng Lin, Xuedi Chen, Gongfa Chen, Zhiwei Zhao, David Bassir","doi":"10.1007/s00477-024-02792-2","DOIUrl":"https://doi.org/10.1007/s00477-024-02792-2","url":null,"abstract":"This study proposes an integrated slope stability prediction model for various complex slope scenarios, including soil, rock, and rock-soil mixed situations. First, a small number of numerical slopes are constructed using the digital twin (DT) technique, and then these slope parameters are sorted and fine-tuned to build a database containing 19,666 soil, single/multiple sets of inclined joints, and rock-soil mixed slope scenarios. Second, the self-attention (SA) mechanism that can analyze the correlation of data features is connected to a classical convolutional neural network (CNN), forming a trained CNN-based SA model (CNN-SA) with 80% of the samples from the built database. The remaining 20% of the database and the stability of six actual slopes are then used for prediction. The performance of the CNN-SA is compared and evaluated. The results indicate that the DT technique is a reliable tool for providing the data to train the AI models, especially when the sample data is limited. As the complexity of the slopes increases, the prediction error of the models increases, and the CNN-based SA mechanism can effectively reduce these prediction errors compared to a classical CNN and other attention mechanisms.","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"6 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219817","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}

引用次数: 0