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A comparative study of various combination strategies for landslide susceptibility mapping considering landslide types

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Geoscience Frontiers 2025年第2期16卷 243-261页

作者： Lanbing Yu Biswajeet Pradhan Yang Wang Faculty of Engineering China University of GeosciencesWuhan 430074China Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering and ITUniversity of Technology SydneyUltimoNSW 2007Australia Research Center of Geohazard Monitoring and Warning in the Three Gorges Reservoir Chongqing 404699China

Landslide susceptibility mapping (LSM) assists planners, local administrations, and decision-makers in preventing, mitigating and managing associated risks. This study proposes a novel DES-based framework that effectively captures the spatial developmental patterns of different landslide types, leading to higher precision LSM. The Wanzhou district (administrative division) of Chongqing Province, southwestern China, was selected as the test area, encompassing 881 landslides classified into rockfalls, reservoir-affected (RA) landslides, and non-reservoir-affected (NRA) landslides. Subsequently, three inventory maps and sixteen environment factors were used as inputs, with multicollinearity and importance analyses used to select the best factor combination for three types of landslides. Finally, the susceptibilities of rockfalls, RA and NRA landslides were combined by six combination strategies: Maximum, Mean, Probability, Voting, Stacking, and Dynamic Ensemble Selection (DES) models, and the optimal strategy was identified by area under the receiver operating characteristic curves (AUC), confusion matrix, and landslide distribution statistic. For LSM of individual landslide types, ResNet consistently outperformed traditional machine learning models, achieving testing AUC values of 0.8925, 0.9427, and 0.6754 for rockfalls, RA, and NRA landslides, respectively. The evaluation of the combination strategies revealed that the DES model achieved the highest testing AUC value of 0.8779, followed by Stacking (0.8728), Maximum (0.8704), Probability (0.8669), and Voting (0.8653), whereas the widely-used Mean method performed the worst (0.8503), even lower than the non-classified LSM (0.8587). The findings offer a robust approach for mitigating future landslide risks and minimizing their adverse impacts, providing valuable insights for geohazard management and decision-making.

关键词： Landslide susceptibility mapping Ensemble method Combination strategy Landslide types

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Earthquake-induced liquefaction hazard mapping at national-scale in Australia using deep learning techniques

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Geoscience Frontiers 2023年第1期14卷 118-132页

作者： Ratiranjan Jena Biswajeet Pradhan Mansour Almazroui Mazen Assiri Hyuck-Jin Park Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering and Information TechnologyUniversity of Technology SydneyAustralia Center of Excellence for Climate Change Research Department of MeteorologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Energy and Mineral Resources Engineering Sejong UniversityChoongmu-gwan209 Neungdong-roGwangjin-guSeoul 05006Korea Earth Observation Centre Institute of Climate ChangeUniversiti Kebangsaan Malaysia43600 UKMBangiSelangorMalaysia

Australia is a relatively stable continental region but not tectonically inert,having geological conditions that are susceptible to liquefaction when subjected to earthquake ground *** hazard assessment for Australia was conducted because no Australian liquefaction maps that are based on modern Al techniques are currently *** this study,several conditioning factors including Shear wave velocity(Vs30),clay content,soil water content,soil bulk density,soil thickness,soil pH,distance from river,slope and elevation were considered to estimate the liquefaction potential index(LPI).By considering the Probabilistic Seismic Hazard Assessment(PSHA)technique,peak ground acceleration(PGA)was derived for 50 yrs period(500 and 2500 yrs return period)in ***,liquefaction hazard index(LHI)(effects based on the size and depth of the liquefiable areas)was estimated by considering the LPI along with the 2%and 10%exceedance probability of earthquake ***,ground acceleration data from the Geoscience Australia projecting 2%and 10%exceedance rate of PGA for 50 yrs were used in this study to produce earthquake induced soil liquefaction hazard ***,deep neural net-works(DNNs)were also exerted to estimate liquefaction hazard that can be reported as liquefaction hazard base maps for Australia with an accuracy of 94%and 93%,*** per the results,very-high liquefaction hazard can be observed in Western and Southern Australia including some parts of *** research is the first ever country-scale study to be considered for soil liquefaction hazard in Australia using geospatial information in association with PSHA and deep learning *** study used an earthquake design magnitude threshold of Mw 6 using the source model *** resulting maps present the earthquake-triggered liquefaction hazard and are intending to establish a conceptual structure to guide more detailed investigations as may be requir

关键词： Earthquake induced liquefaction PSHA Deep learning GIS Australia

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Challenges and Limitations in Speech Recognition Technology:A Critical Review of Speech Signal Processing Algorithms,Tools and systems

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Computer modeling in Engineering & Sciences 2023年第5期135卷 1053-1089页

作者： Sneha Basak Himanshi Agrawal Shreya Jena Shilpa Gite Mrinal Bachute Biswajeet Pradhan Mazen Assiri Computer Science and Information Technology Department Symbiosis Institute of TechnologySymbiosis International(Deemed University)Pune412115India Symbiosis Centre for Applied Artificial Intelligence Symbiosis International(Deemed University)Pune412115India Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering and Information TechnologyUniversity of Technology SydneyNew South Wales2007Australia Center of Excellence for Climate Change Research King Abdulaziz UniversityJeddah21589Saudi Arabia Earth Observation Centre Institute of Climate ChangeUniversity Kebangsaan MalaysiaBangiSelangor43600Malaysia

Speech recognition systems have become a unique human-computer interaction(HCI)*** is one of the most naturally developed human abilities;speech signal processing opens up a transparent and hand-free computation *** paper aims to present a retrospective yet modern approach to the world of speech recognition *** development journey of ASR(Automatic Speech Recognition)has seen quite a few milestones and breakthrough technologies that have been highlighted in this paper.A step-by-step rundown of the fundamental stages in developing speech recognition systems has been presented,along with a brief discussion of various modern-day developments and applications in this *** review paper aims to summarize and provide a beginning point for those starting in the vast field of speech signal *** speech recognition has a vast potential in various industries like telecommunication,emotion recognition,healthcare,etc.,this review would be helpful to researchers who aim at exploring more applications that society can quickly adopt in future years of evolution.

关键词： Speech recognition automatic speech recognition(ASR) mel-frequency cepstral coefficients(MFCC) hidden Markov model(HMM) artificial neural network(ANN)

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AttentionPoolMobileNeXt: An automated construction damage detection model based on a new convolutional neural network and deep feature engineering models

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Multimedia Tools and Applications 2025年第4期84卷 1821-1843页

作者： Aydin, Mehmet Barua, Prabal Datta Chadalavada, Sreenivasulu Dogan, Sengul Tuncer, Turker Chakraborty, Subrata Acharya, Rajendra U. Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey University of Southern Queensland Toowoomba Australia School of Engineering University of Southern Queensland Springfield LakesQLD4300 Australia School of Science and Technology Faculty of Science Agriculture Business and Law University of New England ArmidaleNSW2351 Australia Center for Advanced Modelling and Geospatial Information Systems Faculty of Engineering and IT University of Technology Sydney SydneyNSW2007 Australia School of Mathematics Physics and Computing University of Southern Queensland Toowoomba Australia

In 2023, Turkiye faced a series of devastating earthquakes and these earthquakes affected millions of people due to damaged constructions. These earthquakes demonstrated the urgent need for advanced automated damage detection models to help people. This study introduces a novel solution to address this challenge through the AttentionPoolMobileNeXt model, derived from a modified MobileNetV2 architecture. To rigorously evaluate the effectiveness of the model, we meticulously curated a dataset comprising instances of construction damage classified into five distinct classes. Upon applying this dataset to the AttentionPoolMobileNeXt model, we obtained an accuracy of 97%. In this work, we have created a dataset consisting of five distinct damage classes, and achieved 97% test accuracy using our proposed AttentionPoolMobileNeXt model. Additionally, the study extends its impact by introducing the AttentionPoolMobileNeXt-based Deep Feature Engineering (DFE) model, further enhancing the classification performance and interpretability of the system. The presented DFE significantly increased the test classification accuracy from 90.17% to 97%, yielding improvement over the baseline model. AttentionPoolMobileNeXt and its DFE counterpart collectively contribute to advancing the state-of-the-art in automated damage detection, offering valuable insights for disaster response and recovery efforts. © The Author(s) 2024.

关键词： Image classification

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Debris flow simulation 2D(DFS 2D):Numerical modelling of debris flows and calibration of friction parameters

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第6期14卷 1747-1760页

作者： Minu Treesa Abraham Neelima Satyam Biswajeet Pradhan Hongling Tian Department of Civil Engineering Indian Institute of Technology IndoreIndoreIndia Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering and Information TechnologyUniversity of Technology SydneySydneyAustralia Center of Excellence for Climate Change Research King Abdulaziz UniversityJeddahSaudi Arabia Earth Observation Centre Institute of Climate ChangeUniversity Kebangsaan MalaysiaBangiMalaysia Key Laboratory of Mountain Hazards and Surface Process Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengduChina

Debris flows are rapid mass movements with a mixture of rock,soil and ***-intensity rainfall events have triggered multiple debris flows around the globe,making it an important concern from the disaster management *** study presents a numerical model called debris flow simulation 2D(DFS 2D)and applicability of the proposed model is investigated through the values of the model parameters used for the reproduction of an occurred debris flow at Yindongzi gully in China on 13 August *** model can be used to simulate debris flows using three different rheologies and has a userfriendly interface for providing the *** DFS 2D,flow parameters can be estimated with respect to space and *** values of the flow resistance parameters of model,dry-Coulomb and turbulent friction,were calibrated through the back analysis and the values obtained are 0.1 and 1000 m/s^(2),*** new methods of calibration are proposed in this study,considering the crosssectional area of flow and topographical changes induced by the debris *** proposed methods of calibration provide an effective solution to the cumulative errors induced by coarse-resolution digital elevation models(DEMs)in numerical modelling of debris *** statistical indices such as Willmott's index of agreement,mean-absolute-error,and normalized-root-mean-square-error of the calibrated model are 0.5,1.02 and 1.44,*** comparison between simulated and observed values of topographic changes indicates that DFS 2D provides satisfactory results and can be used for dynamic modelling of debris flows.

关键词： Debris flows Numerical model Rheology Debris flow simulation 2D(DFS 2D)

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Iris Liveness Detection Using Fragmental Energy of Haar Transformed Iris Images Using Ensemble of Machine Learning Classifiers

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Computer modeling in Engineering & Sciences 2023年第7期136卷 323-345页

作者： Smita Khade Shilpa Gite Sudeep D.Thepade Biswajeet Pradhan Abdullah Alamri Symbiosis International(Deemed University) Symbiosis Institute of TechnologyPune412115India Symbiosis Centre for Applied Artificial Intelligence Symbiosis International(Deemed University)Pune412115India Computer Engineering Pimpri Chinchwad College of EngineeringPune411044India Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School for Civil and Environmental EngineeringFaculty of Engineering and Information TechnologyUniversity of Technology SydneySydneyNew SouthWales2007Australia Earth Observation Center Institute of Climate ChangeUniversity Kebangsaan MalaysiaUKMBangiSelangor43600Malaysia Department of Geology&Geophysics College of ScienceKing Saud UniversityP.O.Box 2455Riyadh11451Saudi Arabia

Contactless verification is possible with iris biometric identification,which helps prevent infections like COVID-19 from *** systems have grown unsteady and dangerous as a result of spoofing assaults employing contact lenses,replayed the video,and print *** work demonstrates an iris liveness detection approach by utilizing fragmental coefficients of Haar transformed Iris images as signatures to prevent spoofing attacks for the very first time in the identification of iris *** assorted feature creation ways are studied in the presented solutions,and these created features are explored for the training of eight distinct machine learning classifiers and *** predicted iris liveness identification variants are evaluated using recall,F-measure,precision,accuracy,APCER,BPCER,and *** standard datasets were used in the *** main contribution of our study is achieving a good accuracy of 99.18%with a smaller feature *** fragmental coefficients of Haar transformed iris image of size 8∗8 utilizing random forest algorithm showed superior iris liveness detection with reduced featured vector size(64 features).Random forest gave 99.18%***,conduct an extensive experiment on cross datasets for detailed *** results of our experiments showthat the iris biometric template is decreased in size tomake the proposed framework suitable for algorithmic verification in real-time environments and settings.

关键词： Iris images liveness identification Haar transform machine learning biometric feature formation ensemble model

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Spatial flood susceptibility mapping using an explainable artificial intelligence(XAI)model

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Geoscience Frontiers 2023年第6期14卷 8-19页

作者： Biswajeet Pradhan Saro Lee Abhirup Dikshit Hyesu Kim Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering&ITUniversity of Technology SydneySydneyNSW 2007Australia Earth Observation Center Institute of Climate ChangeUniversiti Kebangsaan Malaysia43600 UKMBangiSelangorMalaysia Geoscience Data Center Korea Institute of Geoscience and Mineral Resources(KIGAM)124 Gwahang-noYuseong-guDaejeon 34132South Korea Department of Resources Engineering Korea University of Science and Technology217 Gajeong-roYuseong-guDaejeon 34113South Korea Department of Astronomy Space Science and GeologyChungnam National University99 Daehak-roYuseong-guDaejeon 34134South Korea

Floods are natural hazards that lead to devastating financial losses and large displacements of *** susceptibility maps can improve mitigation measures according to the specific conditions of a study *** design of flood susceptibility maps has been enhanced through use of hybrid machine learning and deep learning *** these models have achieved better accuracy than traditional models,they are not widely used by stakeholders due to their black-box *** this study,we propose the application of an explainable artificial intelligence(XAI)model that incorporates the Shapley additive explanation(SHAP)model to interpret the outcomes of convolutional neural network(CNN)deep learning models,and analyze the impact of variables on flood susceptibility *** study was conducted in Jinju Province,South Korea,which has a long history of flood *** performance was evaluated using the area under the receiver operating characteristic curve(AUROC),which showed a prediction accuracy of 88.4%.SHAP plots showed that land use and various soil attributes significantly affected flood susceptibility in the study *** light of these findings,we recommend the use of XAIbased models in future flood susceptibility mapping studies to improve interpretations of model outcomes,and build trust among stakeholders during the flood-related decision-making process.

关键词： Flood susceptibility Explainable AI Deep learning South Korea

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Earthquake risk assessment in NE India using deep learning and geospatial analysis

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Geoscience Frontiers 2021年第3期12卷 541-556页

作者： Ratiranjan Jena Biswajeet Pradhan Sambit Prasanajit Naik Abdullah M.Alamri The Center for Advanced Modeling and Geospatial Information Systems(CAMGIS) Faculty of Engineering and Information TechnologyUniversity of Technology SydneyNSW 2007Australia Department of Energy and Mineral Resources Engineering Sejong UniversityChoongmu-gwan209Neungdong-roGwangin-guSeoul 05006Republic of Korea Earth Observation Center Institute of Climate ChangeUniversiti Kebangsaan Malaysia43600 UKMBangiSelangorMalaysia Active Fault and Earthquake Hazard Mitigation Research Institute Pukyong National UniversityBusan 48513South Korea Department of Geology&Geophysics College of ScienceKing Saud UniversityRiyadh 11451Saudi Arabia

Earthquake prediction is currently the most crucial task required for the probability,hazard,risk mapping,and mitigation *** prediction attracts the researchers'attention from both academia and ***,the risk assessment approaches have used various traditional and machine learning ***,deep learning techniques have been rarely tested for earthquake probability ***,this study develops a convolutional neural network(CNN)model for earthquake probability assessment in NE *** conducts vulnerability using analytical hierarchy process(AHP),Venn's intersection theory for hazard,and integrated model for risk mapping.A prediction of classification task was performed in which the model predicts magnitudes more than 4 Mw that considers nine *** classification results and intensity variation were then used for probability and hazard mapping,***,earthquake risk map was produced by multiplying hazard,vulnerability,and coping *** vulnerability was prepared by using six vulnerable factors,and the coping capacity was estimated by using the number of hospitals and associated variables,including budget available for disaster *** CNN model for a probability distribution is a robust technique that provides good *** show that CNN is superior to the other algorithms,which completed the classification prediction task with an accuracy of 0.94,precision of 0.98,recall of 0.85,and F1 score of *** indicators were used for probability mapping,and the total area of hazard(21,412.94 km^(2)),vulnerability(480.98 km^(2)),and risk(34,586.10 km^(2))was estimated.

关键词： Earthquake Convolutional neural network geospatial information systems Hazard Vulnerability Risk North-East India

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Enhancing landslide susceptibility mapping incorporating landslide typology via stacking ensemble machine learning in Three Gorges Reservoir,China

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Geoscience Frontiers 2024年第4期15卷 78-96页

作者： Lanbing Yu Yang Wang Biswajeet Pradhan Faculty of Engineering China University of GeosciencesWuhan 430074China Research Center of Geohazard Monitoring and Warning in the Three Gorges Reservoir Chongqing 404199China Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering and ITUniversity of Technology SydneyUltimoNSW 2007Australia Earth Observation Centre Institute of Climate ChangeUniversiti Kebangsaan Malaysia43600 UKMBangiSelangorMalaysia

Different types of landslides exhibit distinct relationships with environmental conditioning ***,in regions where multiple types of landslides coexist,it is required to separate landslide types for landslide susceptibility mapping(LSM).In this paper,a landslideprone area located in Chongqing Province within the middle and upper reaches of the Three Gorges Reservoir area(TGRA),China,was selected as the study area.733 landslides were classified into three types:reservoir-affected landslides,non-reservoir-affected landslides,and *** landslide inventory datasets and 15 landslide conditional factors were trained by three Machine Learning models(logistic regression,random forest,support vector machine),and a Deep Learning(DL)*** comparing the models using receiver operating characteristics(ROC),the landslide susceptibility indexes of three types landslides were acquired by the best performing *** indexes were then used as input to generate the final map based on the Stacking *** results revealed that DL model showed the best performance in LSM without considering landslide types,achieving an area under the curve(AUC)of 0.854 for testing and 0.922 for ***,when we separated the landslide types for LSM,the AUC improved by 0.026 for testing and 0.044 for ***,this paper demonstrates that considering different landslide types in LSM can significantly improve the quality of landslide susceptibility *** maps in turn,can be valuable tools for evaluating and mitigating landslide hazards.

关键词： Landslide susceptibility mapping Deep learning model Landslide types Stacking method

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Machine Learning Driven Mental Stress Detection on Reddit Posts Using Natural Language Processing

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Human-Centric Intelligent systems 2023年第2期3卷 80-91页

作者： Inamdar, Shaunak Chapekar, Rishikesh Gite, Shilpa Pradhan, Biswajeet AIML Department Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Symbiosis Center for Applied AI (SCAAI) Symbiosis International (Deemed University) Pune India Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS) School of Civil and Environmental Engineering University of Technology Sydney Sydney Australia

People’s mental conditions are often reflected in their social media activity due to the internet's anonymity. Psychiatric issues are often detected through such activities and can be addressed in their early stages, potentially preventing the consequences of unattended mental disorders like depression and anxiety. In this paper, the authors have implemented machine learning models and used various embedding techniques to classify posts from the famous social media blog site Reddit as stressful and non-stressful. The dataset used contains user posts that can be analyzed to detect patterns in the social media activity of those diagnosed with mental disorders. This paper uses different NLP (Natural Language Processing) tools such as ELMo (Embeddings from Language Models) word embeddings, BERT (Bidirectional Encoder Representations from Transformers) tokenizers, and BoW (Bag of Words) approach to create word/sentence data that can be fed to machine learning models. The results of each method have been discussed. The results achieved a top F1 score of 0.76, a Precision score of 0.71, and a Recall of 0.74 using only the preprocessed texts and machine learning algorithms to classify the posts. The results achieved by this paper are significant and have the potential to be applied in real-world scenarios to analyze mental stress among social media users. Although this paper focuses on data from Reddit, the techniques used can be transferred to similar social media platforms and could help solve the growing mental health crisis.

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