Pub Date : 2022-07-03DOI: 10.1080/17499518.2022.2123175
Limin Zhang
The editors are pleased to present Best Paper Award 2021 for Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards to Michael P. Crisp, Mark B. Jaksa & Yien L. Kuo for their paper entitled “Characterising site investigation performance in multiple-layer soils and soil lenses” published in Georisk in 2021, Vol. 15, No. 3, pp. 196–208. This award was established in 2011 and is bestowed annually upon the author(s) of the best paper, on the basis of its technical merit, published in Georisk over the year. Nominations for this award are solicited from the editorial board members, the managing editors, and the advisory board members of Georisk. The decision is made by the managing editors. The editors are also pleased to present Georisk Most Cited Paper Award 2021 to Wengang Zhang, Chongzhi Wu, Yongqin Li, Lin Wang & P. Samui for their paper “Assessment of pile drivability using random forest regression and multivariate adaptive regression splines” published in Georisk in 2021, Vol. 15, No. 1, pp. 27–40. This paper is most cited among the papers published in Georisk during 2018–2022, reflecting the intense attention readers pay to data-driven methods. The papers published in Georisk have made a profound impact on the assessment and management of risks for engineered systems and geohazards. We launched “Georisk Most Cited Paper Award” in 2017 to recognise the contributions of the authors whose papers were highly cited. This award is given annually, selected based on rolling 5-year Scopus citations, with judgement from the managing editors. Georisk received nearly 200 manuscripts in 2021. The responsibility of timely reviewing these submissions falls on the shoulder of all the editors and editorial board members (EBMs). To recognise the contributions of our EBMs, a new award “Georisk Best EBM Award” was launched in 2021. The editors are pleased to present “Georisk Best EBM Award 2021” to Prof Jia-Jyun Dong of National Central University, who handled 5 manuscripts with decisions in the past year. We would like to congratulate the recipients of the Georisk Best Paper Award, Most Cited Paper Award, and Best EBM Award.
{"title":"2021 Georisk best paper award, most cited paper award and best EBM award","authors":"Limin Zhang","doi":"10.1080/17499518.2022.2123175","DOIUrl":"https://doi.org/10.1080/17499518.2022.2123175","url":null,"abstract":"The editors are pleased to present Best Paper Award 2021 for Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards to Michael P. Crisp, Mark B. Jaksa & Yien L. Kuo for their paper entitled “Characterising site investigation performance in multiple-layer soils and soil lenses” published in Georisk in 2021, Vol. 15, No. 3, pp. 196–208. This award was established in 2011 and is bestowed annually upon the author(s) of the best paper, on the basis of its technical merit, published in Georisk over the year. Nominations for this award are solicited from the editorial board members, the managing editors, and the advisory board members of Georisk. The decision is made by the managing editors. The editors are also pleased to present Georisk Most Cited Paper Award 2021 to Wengang Zhang, Chongzhi Wu, Yongqin Li, Lin Wang & P. Samui for their paper “Assessment of pile drivability using random forest regression and multivariate adaptive regression splines” published in Georisk in 2021, Vol. 15, No. 1, pp. 27–40. This paper is most cited among the papers published in Georisk during 2018–2022, reflecting the intense attention readers pay to data-driven methods. The papers published in Georisk have made a profound impact on the assessment and management of risks for engineered systems and geohazards. We launched “Georisk Most Cited Paper Award” in 2017 to recognise the contributions of the authors whose papers were highly cited. This award is given annually, selected based on rolling 5-year Scopus citations, with judgement from the managing editors. Georisk received nearly 200 manuscripts in 2021. The responsibility of timely reviewing these submissions falls on the shoulder of all the editors and editorial board members (EBMs). To recognise the contributions of our EBMs, a new award “Georisk Best EBM Award” was launched in 2021. The editors are pleased to present “Georisk Best EBM Award 2021” to Prof Jia-Jyun Dong of National Central University, who handled 5 manuscripts with decisions in the past year. We would like to congratulate the recipients of the Georisk Best Paper Award, Most Cited Paper Award, and Best EBM Award.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49448806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-03DOI: 10.1080/17499518.2022.2101182
Shirin C. Samiljan, D. Christian, G. Baecher
{"title":"John T. Christian (1936–2022)","authors":"Shirin C. Samiljan, D. Christian, G. Baecher","doi":"10.1080/17499518.2022.2101182","DOIUrl":"https://doi.org/10.1080/17499518.2022.2101182","url":null,"abstract":"","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43588489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.3390/geohazards3030019
B. Gaite, J. Macías, J. Cantavella, C. Sánchez-Linares, Carlos González, Luis Carlos Puertas
Real-time local tsunami warnings embody uncertainty from unknowns in the source definition within the first minutes after the tsunami generates. In general, Tsunami Warning Systems (TWS) provide a quick estimate for tsunami action from deterministic simulations of a single event. In this study, variability in tsunami source parameters has been included by running 135 tsunami simulations; besides this, four different computational domains in the northeastern Atlantic ocean have been considered, resulting in 540 simulations associated with a single event. This was done for tsunamis generated by earthquakes in the Gulf of Cadiz with impact in the western Iberian peninsula and the Canary Islands. A first answer is provided after one minute, and 7 min are required to perform all the simulations in the four computational domains. The fast computation allows alert levels all along the coast to be incorporated into the Spanish National Tsunami Early Warning System. The main findings are that the use of a set of scenarios that account for the uncertainty in source parameters can produce higher tsunami warnings in certain coastal areas than those obtained from a single deterministic reference scenario. Therefore, this work shows that considering uncertainties in tsunami source parameters helps to avoid possible tsunami warning level underestimations. Furthermore, this study demonstrates that this is possible to do in real time in an actual TWS with the use of high-performance computing resources.
{"title":"Analysis of Faster-Than-Real-Time (FTRT) Tsunami Simulations for the Spanish Tsunami Warning System for the Atlantic","authors":"B. Gaite, J. Macías, J. Cantavella, C. Sánchez-Linares, Carlos González, Luis Carlos Puertas","doi":"10.3390/geohazards3030019","DOIUrl":"https://doi.org/10.3390/geohazards3030019","url":null,"abstract":"Real-time local tsunami warnings embody uncertainty from unknowns in the source definition within the first minutes after the tsunami generates. In general, Tsunami Warning Systems (TWS) provide a quick estimate for tsunami action from deterministic simulations of a single event. In this study, variability in tsunami source parameters has been included by running 135 tsunami simulations; besides this, four different computational domains in the northeastern Atlantic ocean have been considered, resulting in 540 simulations associated with a single event. This was done for tsunamis generated by earthquakes in the Gulf of Cadiz with impact in the western Iberian peninsula and the Canary Islands. A first answer is provided after one minute, and 7 min are required to perform all the simulations in the four computational domains. The fast computation allows alert levels all along the coast to be incorporated into the Spanish National Tsunami Early Warning System. The main findings are that the use of a set of scenarios that account for the uncertainty in source parameters can produce higher tsunami warnings in certain coastal areas than those obtained from a single deterministic reference scenario. Therefore, this work shows that considering uncertainties in tsunami source parameters helps to avoid possible tsunami warning level underestimations. Furthermore, this study demonstrates that this is possible to do in real time in an actual TWS with the use of high-performance computing resources.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91076183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-22DOI: 10.1080/17499518.2022.2088803
Jianfeng Wu, Yanrong Li, Shuai Zhang, Joachim Chris Junior Oualembo Mountou
{"title":"Early identification of potential loess landslide using convolutional neural networks with skip connection: a case study in northwest Lvliang City, Shanxi Province, China","authors":"Jianfeng Wu, Yanrong Li, Shuai Zhang, Joachim Chris Junior Oualembo Mountou","doi":"10.1080/17499518.2022.2088803","DOIUrl":"https://doi.org/10.1080/17499518.2022.2088803","url":null,"abstract":"","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47149839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-20DOI: 10.3390/geohazards3020018
S. Harig, N. Zamora, A. Gubler, N. Rakowsky
Tsunami inundation estimates are of crucial importance to hazard and risk assessments. In the context of tsunami forecast, numerical simulations are becoming more feasible with the growth of computational power. Uncertainties regarding source determination within the first minutes after a tsunami generation might be a major concern in the issuing of an appropriate warning on the coast. However, it is also crucial to investigate differences emerging from the chosen algorithms for the tsunami simulations due to a dependency of the outcomes on the suitable model settings. In this study, we compare the tsunami inundation in three cities in central Chile (Coquimbo, Viña del Mar, and Valparaíso) using three different models (TsunAWI, Tsunami-HySEA, COMCOT) while varying the parameters such as bottom friction. TsunAWI operates on triangular meshes with variable resolution, whereas the other two codes use nested grids for the coastal area. As initial conditions of the experiments, three seismic sources (2010 Mw 8.8 Maule, 2015 Mw 8.3 Coquimbo, and 1730 Mw 9.1 Valparaíso) are considered for the experiments. Inundation areas are determined with high-resolution topo-bathymetric datasets based on specific wetting and drying implementations of the numerical models. We compare each model’s results and sensitivities with respect to parameters such as bottom friction and bathymetry representation in the varying mesh geometries. The outcomes show consistent estimates for the nearshore wave amplitude of the leading wave crest based on identical seismic source models within the codes. However, with respect to inundation, we show high sensitivity to Manning values where a non-linear behaviour is difficult to predict. Differences between the relative decrease in inundation areas and the Manning n-range (0.015–0.060) are high (11–65%), with a strong dependency on the characterization of the local topo-bathymery in the Coquimbo and Valparaíso areas. Since simulations carried out with such models are used to generate hazard estimates and warning products in an early tsunami warning context, it is crucial to investigate differences that emerge from the chosen algorithms for the tsunami simulations.
{"title":"Systematic Comparison of Tsunami Simulations on the Chilean Coast Based on Different Numerical Approaches","authors":"S. Harig, N. Zamora, A. Gubler, N. Rakowsky","doi":"10.3390/geohazards3020018","DOIUrl":"https://doi.org/10.3390/geohazards3020018","url":null,"abstract":"Tsunami inundation estimates are of crucial importance to hazard and risk assessments. In the context of tsunami forecast, numerical simulations are becoming more feasible with the growth of computational power. Uncertainties regarding source determination within the first minutes after a tsunami generation might be a major concern in the issuing of an appropriate warning on the coast. However, it is also crucial to investigate differences emerging from the chosen algorithms for the tsunami simulations due to a dependency of the outcomes on the suitable model settings. In this study, we compare the tsunami inundation in three cities in central Chile (Coquimbo, Viña del Mar, and Valparaíso) using three different models (TsunAWI, Tsunami-HySEA, COMCOT) while varying the parameters such as bottom friction. TsunAWI operates on triangular meshes with variable resolution, whereas the other two codes use nested grids for the coastal area. As initial conditions of the experiments, three seismic sources (2010 Mw 8.8 Maule, 2015 Mw 8.3 Coquimbo, and 1730 Mw 9.1 Valparaíso) are considered for the experiments. Inundation areas are determined with high-resolution topo-bathymetric datasets based on specific wetting and drying implementations of the numerical models. We compare each model’s results and sensitivities with respect to parameters such as bottom friction and bathymetry representation in the varying mesh geometries. The outcomes show consistent estimates for the nearshore wave amplitude of the leading wave crest based on identical seismic source models within the codes. However, with respect to inundation, we show high sensitivity to Manning values where a non-linear behaviour is difficult to predict. Differences between the relative decrease in inundation areas and the Manning n-range (0.015–0.060) are high (11–65%), with a strong dependency on the characterization of the local topo-bathymery in the Coquimbo and Valparaíso areas. Since simulations carried out with such models are used to generate hazard estimates and warning products in an early tsunami warning context, it is crucial to investigate differences that emerge from the chosen algorithms for the tsunami simulations.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74030035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-19DOI: 10.1080/17499518.2022.2088802
Chenxu Su, Bijiao Wang, Yunhong Lv, Mingpeng Zhang, Da-lei Peng, B. Bate, Shuai Zhang
ABSTRACT Datasets containing recorded landslide and non-landslide samples can greatly influence the performance of machine learning (ML) models, which are commonly used in landslide susceptibility mapping (LSM). However, the non-landslide samples cannot be directly obtained. In this study, a pattern-based approach is proposed to improve the LSM process, constructing unsupervised machine learning (UML) – supervised machine learning (SML) collaborative models in which the non-landslide samples can be reasonably selected. Two UML models, the Gaussian mixture model (GMM) and K-means, are introduced to sample the non-landslide datasets with four sampling selections (abbreviated as A, B, C and D, respectively). Then non-landslide patterns recognised by the UML models are learned by the random forest (RF). A new sensitivity index, accuracy improvement ratio (AIR), is defined to evaluate the superiority of these sampling selections. Compared with the GMM-RF model, the K-means-RF model is more capable of recognising non-landslide patterns and providing sufficient and reliable non-landslide samples. The sampling selection A of the K-means-RF with an AIR value of 2.3 is regarded as the best selection. The results indicate that the UML-SML model based on the pattern-based approach offers an effective strategy to find the non-landslide samples and has a better solution to the LSM.
{"title":"Improved landslide susceptibility mapping using unsupervised and supervised collaborative machine learning models","authors":"Chenxu Su, Bijiao Wang, Yunhong Lv, Mingpeng Zhang, Da-lei Peng, B. Bate, Shuai Zhang","doi":"10.1080/17499518.2022.2088802","DOIUrl":"https://doi.org/10.1080/17499518.2022.2088802","url":null,"abstract":"ABSTRACT Datasets containing recorded landslide and non-landslide samples can greatly influence the performance of machine learning (ML) models, which are commonly used in landslide susceptibility mapping (LSM). However, the non-landslide samples cannot be directly obtained. In this study, a pattern-based approach is proposed to improve the LSM process, constructing unsupervised machine learning (UML) – supervised machine learning (SML) collaborative models in which the non-landslide samples can be reasonably selected. Two UML models, the Gaussian mixture model (GMM) and K-means, are introduced to sample the non-landslide datasets with four sampling selections (abbreviated as A, B, C and D, respectively). Then non-landslide patterns recognised by the UML models are learned by the random forest (RF). A new sensitivity index, accuracy improvement ratio (AIR), is defined to evaluate the superiority of these sampling selections. Compared with the GMM-RF model, the K-means-RF model is more capable of recognising non-landslide patterns and providing sufficient and reliable non-landslide samples. The sampling selection A of the K-means-RF with an AIR value of 2.3 is regarded as the best selection. The results indicate that the UML-SML model based on the pattern-based approach offers an effective strategy to find the non-landslide samples and has a better solution to the LSM.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2022-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42726043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-16DOI: 10.1080/17499518.2022.2087884
K. Phoon, Wenpeng Zhang
ABSTRACT Machine learning (ML) is widely used in many industries, resulting in recent interests to explore ML in geotechnical engineering. Past review papers focus mainly on ML algorithms while this paper advocates an agenda to put data at the core, to develop novel algorithms that are effective for geotechnical data (existing and new), to address the needs of current practice, to exploit new opportunities from emerging technologies or to meet new needs from digital transformation, and to take advantage of current knowledge and accumulated experience. This agenda is called data-centric geotechnics and it contains three core elements: data centricity, fit for (and transform) practice, and geotechnical context. The future of machine learning in geotechnics should be envisioned with this “data first practice central” agenda in mind. Data-driven site characterization (DDSC) is an active research topic in this agenda because an understanding of the ground is crucial in all projects. Examples of DDSC challenges are ugly data and explainable site recognition. Additional challenges include making ML indispensable (ML supremacy), learning how to learn (meta-learning), and becoming smart (digital twin).
{"title":"Future of machine learning in geotechnics","authors":"K. Phoon, Wenpeng Zhang","doi":"10.1080/17499518.2022.2087884","DOIUrl":"https://doi.org/10.1080/17499518.2022.2087884","url":null,"abstract":"ABSTRACT Machine learning (ML) is widely used in many industries, resulting in recent interests to explore ML in geotechnical engineering. Past review papers focus mainly on ML algorithms while this paper advocates an agenda to put data at the core, to develop novel algorithms that are effective for geotechnical data (existing and new), to address the needs of current practice, to exploit new opportunities from emerging technologies or to meet new needs from digital transformation, and to take advantage of current knowledge and accumulated experience. This agenda is called data-centric geotechnics and it contains three core elements: data centricity, fit for (and transform) practice, and geotechnical context. The future of machine learning in geotechnics should be envisioned with this “data first practice central” agenda in mind. Data-driven site characterization (DDSC) is an active research topic in this agenda because an understanding of the ground is crucial in all projects. Examples of DDSC challenges are ugly data and explainable site recognition. Additional challenges include making ML indispensable (ML supremacy), learning how to learn (meta-learning), and becoming smart (digital twin).","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45972797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-13DOI: 10.1080/17499518.2022.2088801
V. Mahmoudabadi, N. Ravichandran
ABSTRACT This paper presents a quantitative framework to optimise embedded footing performance subjected to extreme historical climate events with respect to the uncertainties associated with site-specific soil and climatic parameters. The proposed framework is developed based on partially saturated soil mechanics principles in conjunction with a multi-objective optimisation algorithm called Non-dominated Sorting Genetic Algorithm (NSGA-II) to develop a robust optimised design procedure. The proposed method was applied to two semi-arid climate sites, Riverside and Victorville, both situated in California, United States. The results show that the proposed method generally improves the embedded footing design compared to conventional methods in terms of cost and performance. Based on the findings, under the extreme climate conditions, the proposed method estimates the average soil degree of saturation within the footing influence zone between 52% and 95%, with a mean value of 63.1% for the Victorville site, and 57% and 90% with a mean value of 81.6% for the site in Riverside. It is also found that the optimal design from the proposed method shows a lower total construction cost, 44% and 19%, for the Victorville and Riverside sites, respectively, compared to the ones designed by the conventional methods.
{"title":"Reliability-based optimization in climate-adaptive design of embedded footing","authors":"V. Mahmoudabadi, N. Ravichandran","doi":"10.1080/17499518.2022.2088801","DOIUrl":"https://doi.org/10.1080/17499518.2022.2088801","url":null,"abstract":"ABSTRACT This paper presents a quantitative framework to optimise embedded footing performance subjected to extreme historical climate events with respect to the uncertainties associated with site-specific soil and climatic parameters. The proposed framework is developed based on partially saturated soil mechanics principles in conjunction with a multi-objective optimisation algorithm called Non-dominated Sorting Genetic Algorithm (NSGA-II) to develop a robust optimised design procedure. The proposed method was applied to two semi-arid climate sites, Riverside and Victorville, both situated in California, United States. The results show that the proposed method generally improves the embedded footing design compared to conventional methods in terms of cost and performance. Based on the findings, under the extreme climate conditions, the proposed method estimates the average soil degree of saturation within the footing influence zone between 52% and 95%, with a mean value of 63.1% for the Victorville site, and 57% and 90% with a mean value of 81.6% for the site in Riverside. It is also found that the optimal design from the proposed method shows a lower total construction cost, 44% and 19%, for the Victorville and Riverside sites, respectively, compared to the ones designed by the conventional methods.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47606028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-13DOI: 10.3390/geohazards3020017
Juan F. Rodríguez, J. Macías, M. Castro, Marc de la Asunción, C. Sánchez-Linares
Operational TEWS play a key role in reducing tsunami impact on populated coastal areas around the world in the event of an earthquake-generated tsunami. Traditionally, these systems in the NEAM region have relied on the implementation of decision matrices. The very short arrival times of the tsunami waves from generation to impact in this region have made it not possible to use real-time on-the-fly simulations to produce more accurate alert levels. In these cases, when time restriction is so demanding, an alternative to the use of decision matrices is the use of datasets of precomputed tsunami scenarios. In this paper we propose the use of neural networks to predict the tsunami maximum height and arrival time in the context of TEWS. Different neural networks were trained to solve these problems. Additionally, ensemble techniques were used to obtain better results.
{"title":"Use of Neural Networks for Tsunami Maximum Height and Arrival Time Predictions","authors":"Juan F. Rodríguez, J. Macías, M. Castro, Marc de la Asunción, C. Sánchez-Linares","doi":"10.3390/geohazards3020017","DOIUrl":"https://doi.org/10.3390/geohazards3020017","url":null,"abstract":"Operational TEWS play a key role in reducing tsunami impact on populated coastal areas around the world in the event of an earthquake-generated tsunami. Traditionally, these systems in the NEAM region have relied on the implementation of decision matrices. The very short arrival times of the tsunami waves from generation to impact in this region have made it not possible to use real-time on-the-fly simulations to produce more accurate alert levels. In these cases, when time restriction is so demanding, an alternative to the use of decision matrices is the use of datasets of precomputed tsunami scenarios. In this paper we propose the use of neural networks to predict the tsunami maximum height and arrival time in the context of TEWS. Different neural networks were trained to solve these problems. Additionally, ensemble techniques were used to obtain better results.","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87649390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-06DOI: 10.1080/17499518.2022.2084555
Himanshu Rana, G. S. Sivakumar Babu
{"title":"Probabilistic back analysis for rainfall-induced slope failure using MLS-SVR and Bayesian analysis","authors":"Himanshu Rana, G. S. Sivakumar Babu","doi":"10.1080/17499518.2022.2084555","DOIUrl":"https://doi.org/10.1080/17499518.2022.2084555","url":null,"abstract":"","PeriodicalId":48524,"journal":{"name":"Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44827785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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