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Geoscience Frontiers 2021年第6期12卷 354-355页

作者： Biswajeet Pradhan Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) Faculty of Engineering and Information TechnologyUniversity of Technology SydneyNSW 2007Australia Department of Energy and Mineral Resources Engineering Sejong UniversityChoongmu-gwan209 Neungdong-roGwangjin-guSeoul 05006Republic of Korea Center of Excellence for Climate Change Research King Abdulaziz UniversityP.O.Box 80234Jeddah 21589Saudi Arabia

Energy is a vital commodity that sustains human lives,as well as economic *** challenges towards energy generation,demand and supply are plenty owing to the use of fossil fuels leading to climate change and environmental problems like water and air *** the increasing awareness over climate change,post Paris Agreement,the role of energy plays a key role towards achieving the proposed *** contributions in this Special Issue of Geoscience Frontiers on Energy includes 8 papers from esteemed research groups worldwide which explores,highlights and provide new insights towards the various aspects of energy.

关键词： commodity plenty Preface

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Estimating earthquake peak ground acceleration and intensity using short-time Fourier and wavelet transform techniques: a case study at Odisha, India

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Arabian Journal of Geosciences 2022年第11期15卷 1-16页

作者： Jena, Ratiranjan Al-Amri, Abdullah Malulud, Khairul Nizam Abdul Shanableh, Abdallah Al-Ruzouq, Rami Center for Advanced Modeling and Geospatial Information Systems School of Civil and Environmental Engieering Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia GIS & Remote Sensing Center Research Institute of Sciences and Engineering University of Sharjah Sharjah United Arab Emirates Civil and Environmental Engineering Department University of Sharjah Sharjah United Arab Emirates Department of Geology and Geophysics College of Science King Saud University Riyadh Saudi Arabia Earth Observation Center Institute of Climate Change Universiti Kebangsaan Malaysia Bangi Malaysia

Seismic hazard assessment is indispensable to improve and update the earthquake peak ground acceleration (PGA) and intensity map. Although a lot of work has been done on earthquake hazard assessment using various mathematical functions and spatial technologies, however, limited studies could be seen on estimating PGA and intensity variation using the powerful wavelets tool. This paper analyzed the location-frequency characteristics of PGA, intensity, and their magnitude values based on short-time Fourier transform (STFT) and continuous wavelet transform (CWT) techniques. The models were implemented in the Indian state of Odisha. Earthquake ground motion is a random process that depends on lithology, amplification factors, structural features, and complicated tectonics of a given location. Four STFTs, namely Rectangle, Welch, Hanning, and Hamming and four CWT techniques such as Morlet, Paul (k=2), derivative of Gaussian DOG (k=2), and DOG (k=6) were implemented to study earthquake PGA in horizontal directions and their motion intensities. This study would help to locate the long-period ground motion in all the event locations. This research could help in identifying the areas, where the power of long-period ground motions could trigger structural damage. This study concludes that Hanning and Hamming’s windows are superior to others in STFT, and DOG (k=6) is better than others in CWT analysis. Detailed seismic wave analysis and long-period ground motions study are required to minimize the structural damage during destructive earthquakes. Hence, wavelet techniques could be useful to specify the seismically active locations and local structural features.

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APG: A novel python-based ArcGIS toolbox to generate absence-datasets for geospatial studies

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Geoscience Frontiers 2021年第6期12卷 90-104页

作者： Seyed Amir Naghibi Hossein Hashemi Biswajeet Pradhan Department of Water Resources Engineering&Center for Advanced Middle Eastern Studies Lund UniversityLundSweden Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) Faculty of Engineering and Information TechnologyUniversity of Technology SydneyNew South WalesAustralia Department of Energy and Mineral Resources Engineering Sejong UniversityChoongmu-gwan209 Neungdong-ro Gwangjin-guSeoul 05006Republic of Korea Department of Meteorology King Abdulaziz UniversityP.O.Box 80234Jeddah 21589Saudi Arabia Earth Observation Centre Institute of Climate ChangeUniversiti Kebangsaan MalaysiaBangiSelangorMalaysia

One important step in binary modeling of environmental problems is the generation of absence-datasets that are traditionally generated by random sampling and can undermine the quality of *** solve this problem,this study develops the Absence Point Generation(APG)toolbox which is a Python-based ArcGIS toolbox for automated construction of absence-datasets for geospatial *** APG employs a frequency ratio analysis of four commonly used and important driving factors such as altitude,slope degree,topographic wetness index,and distance from rivers,and considers the presence locations buffer and density layers to define the low potential or susceptibility zones where absence-datasets are *** test the APG toolbox,we applied two benchmark algorithms of random forest(RF)and boosted regression trees(BRT)in a case study to investigate groundwater potential using three absence datasets i.e.,the APG,random,and selection of absence samples(SAS)*** BRT-APG and RF-APG had the area under receiver operating curve(AUC)values of 0.947 and 0.942,while BRT and RF had weaker performances with the SAS and Random *** effect resulted in AUC improvements for BRT and RF by 7.2,and 9.7%from the Random dataset,and AUC improvements for BRT and RF by 6.1,and 5.4%from the SAS dataset,*** APG also impacted the importance of the input factors and the pattern of the groundwater potential maps,which proves the importance of absence points in environmental binary *** proposed APG toolbox could be easily applied in other environmental hazards such as landslides,floods,and gully erosion,and land subsidence.

关键词： Absence-dataset Classification Python Machine learning algorithms GIS Groundwater Hydrogeology

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Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks

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Geoscience Frontiers 2021年第2期12卷 625-637页

作者： Husam A.H.Al-Najjar Biswajeet Pradhan Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) Faculty of Engineering and ITUniversity of Technology Sydney2007NSWAustralia Department of Energy and Mineral Resources Engineering Sejong UniversityChoongmu-gwan209Neungdong-roGwangin-guSeoul05006Republic of Korea Center of Excellence for Climate Change Research King Abdulaziz UniversityP.O.Box 80234Jeddah21589Saudi Arabia Earth Observation Center Institute of Climate ChangeUniversiti Kebangsaan Malaysia43600UKMBangiSelangorMalaysia

In recent years,landslide susceptibility mapping has substantially improved with advances in machine ***,there are still challenges remain in landslide mapping due to the availability of limited inventory *** this paper,a novel method that improves the performance of machine learning techniques is *** proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of *** this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked ***,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron *** show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM *** models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,*** using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,*** the additional samples improved the test accuracy of all the models except *** a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is

关键词： Landslide susceptibility Inventory Machine learning Generative adversarial network Convolutional neural network Geographic information system

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Artificial Intelligence:A new era for spatial modelling and interpreting climate-induced hazard assessment

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Geoscience Frontiers 2024年第4期15卷 1-13页

作者： Abhirup Dikshit Biswajeet Pradhan Sahar S.Matin Ghassan Beydoun M.Santosh Hyuck-Jin Park Khairul Nizam Abdul Maulud Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School of Civil&Environmental EngineeringFaculty of Engineering and ITUniversity of Technology SydneyNSW 2007Australia Earth Observation Center Institute of Climate ChangeUniversiti Kebangsaan Malaysia43600 UKM BangiSelangorMalaysia Department of Energy and Mineral Resources Engineering Sejong UniversityChoongmu-gwan209 Neungdong-roGwangjin-guSeoul 05006Republic of Korea School of Earth Sciences and Resources China University of Geosciences BeijingXueyuan RoadBeijing 100083China Department of Earth Sciences University of AdelaideAdelaideSouth AustraliaAustralia Faculty of Science Kochi UniversityKochi 780-8520Japan

The application of Artificial Intelligence in various fields has witnessed tremendous progress in the recent *** field of geosciences and natural hazard modelling has also benefitted immensely from the introduction of novel algorithms,the availability of large quantities of data,and the increase in computational *** enhancement in algorithms can be largely attributed to the elevated complexity of the network architecture and the heightened level of abstraction found in the network's later *** a result,AI models lack transparency and accountability,often being dubbed as"black box"*** AI(XAI)is emerging as a solution to make AI models more transparent,especially in domains where transparency is *** discussion surrounds the use of XAI for diverse purposes,as researchers explore its applications across various *** the growing body of research papers on XAI case studies,it has become increasingly important to address existing gaps in the *** current literature lacks a comprehensive understanding of the capabilities,limitations,and practical implications of *** study provides a comprehensive overview of what constitutes XAI,how it is being used and potential applications in hydrometeorological natural *** aims to serve as a useful reference for researchers,practitioners,and stakeholders who are currently using or intending to adopt XAI,thereby contributing to the advancements for wider acceptance of XAI in the future.

关键词： Artificial Intelligence Explainable AI(XAI) Climate change Spatial modelling Natural hazards

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Assessment of geochemical and sedimentological characteristics of atmospheric dust in Shiraz,southwest Iran

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Geoscience Frontiers 2020年第3期11卷 783-792页

作者： Amir Karimian Torghabeh Biswajeet Pradhan Ashkan Jahandari Department of Earth Sciences Faculty of SciencesShiraz UniversityShirazIran The Centre for Advanced Modeling and Geospatial Information Systems(CAMGIS) Faculty of Engineering and ITUniversity of Technology SydneySydneyNSW2007Australia Department of Energy and Mineral Resources Engineering Sejong UniversityChoongmu-gwan209 Neungdong ro Gwangjin-guSeoul05006South Korea Department of Geology Shahid Bahonar University of KermanKermanIran

Geogenic dust is commonly believed to be one of the most important environmental problems in the Middle *** present study investigated the geochemical characteristics of atmospheric dust particles in Shiraz City(south of Iran).Atmospheric dust samples were collected through a dry collector method by using glass trays at 10 location sites in May *** composition was analysed through inductively coupled plasma optical emission *** data showed that the dustiest days were usually in spring and summer,particularly in April.X-ray diffraction analysis of atmospheric dust samples indicated that the mineralogical composition of atmospheric dust was calcite+dolomite(24%)>palygorskite(18%)>quartz(14%)>muscovite(13%)>albite(11%)>kaolinite(7%)>gypsum(7%)>zircon=anatase(3%).The high occurrence of palygorskite(16%-23%) could serve as a tracer of the source areas of dust storms from the desert of Iraq and Saudi Arabia to the South of *** electron microscopy indicated that the sizes of the collected dust varied from 50 μm to0.8 μm,but 10 μm was the predominant *** atmospheric dust collected had prismatic trigonal-rhombohedral crystals and semi-rounded irregular ***,diatoms were detected in several samples,suggesting that emissions from dry-bed lakes,such as Hoor Al-Azim Wetland(located in the southwest of Iran),also contributed to the dust *** trajectory simulations were performed at the date of sampling by using the NOAA HYSPLIT *** showed that the sources of atmospheric dust in the study area were the eastern area of Iraq,eastern desert of Saudi Arabia,Kuwait and Khuzestan *** Ca/Al ratio of the collected samples(1.14) was different from the upper continental crust(UCC) value(UCC=0.37),whereas Mg/A1(0.29),K/Al(0.22) and Ti/Al(0.07) ratios were close to the UCC value(0.04).This condition favours desert calcisols as the main mineral dust *** of the crustal enrichment factor(EF

关键词： Dust storm Atmospheric dust Heavy metals Diatom Palygorskite Provenance

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Systematic investigation into generalization of COVID-19 CT deep learning models with Gabor ensemble for lung involvement scoring

arXiv

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arXiv 2021年

作者： Horry, Michael James Chakraborty, Subrata Pradhan, Biswajeet Fallahpoor, Maryam Hossein, Chegeni Paul, Manoranjan Center for Advanced Modelling and Geospatial Information Systems School of Information Systems & Modelling Faculty of Engineering and IT University of Technology Sydney SydneyNSW2007 Australia Imaging Center Mehr General Hospital Tehran Iran School of Computing and Mathematics Charles Sturt University BathurstNSW2795 Australia

The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focusing on diagnosis and stratification of COVID-19 from medical images. Despite this large-scale research effort, these models have found limited practical application due in part to unproven generalization of these models beyond their source study. This study investigates the generalizability of key published models using the publicly available COVID-19 Computed Tomography data through cross dataset validation. We then assess the predictive ability of these models for COVID-19 severity using an independent new dataset that is stratified for COVID-19 lung involvement. Each inter-dataset study is performed using histogram equalization, and contrast limited adaptive histogram equalization with and without a learning Gabor filter. The study shows high variability in the generalization of models trained on these datasets due to varied sample image provenances and acquisition processes amongst other factors. We show that under certain conditions, an internally consistent dataset can generalize well to an external dataset despite structural differences between these datasets with f1 scores up to 86%. Our best performing model shows high predictive accuracy for lung involvement score for an independent dataset for which expertly labelled lung involvement stratification is available. Creating an ensemble of our best model for disease positive prediction with our best model for disease negative prediction using a min-max function resulted in a superior model for lung involvement prediction with average predictive accuracy of 75% for zero lung involvement and 96% for 75-100% lung involvement with almost linear relationship between these stratifications. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

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Prediction of spatial soil loss impacted by long-term land-use/land-cover change in a tropical watershed

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Geoscience Frontiers 2019年第2期10卷 389-403页

作者： J.H.Abdulkareem B.Pradhan W.N.A.Sulaiman N.R.Jamil Department of Environmental Science Faculty of Environmental StudiesUniversiti Putra Malaysia (UPM) Department of Soil Science Institute for Agricultural Research/Faculty of AgricultureAhmadu Bello University Department of Civil Engineering Faculty of EngineeringGeospatial Information Science Research Center (GISRC)Universiti Putra Malaysia (UPM) Centre for Advanced Modeling and Geospatial Information Systems (CAMGIS) Faculty of Engineering and Information TechnologyUniversity of Technology Sydney Department of Energy and Mineral Resources Engineering Choongmu-gwanSejong University

The devastating effect of soil erosion is one of the major sources of land degradation that affects human lives in many ways which occur mainly due to deforestation, poor agricultural practices, overgrazing,wildfire and urbanization. Soil erosion often leads to soil truncation, loss of fertility, slope instability, *** causes irreversible effects on the poorly renewable soil resource. In view of this, a study was conducted in Kelantan River basin to predict soil loss as influenced by long-term land use/land-cover(LULC) changes in the area. The study was conducted with the aim of predicting and assessing soil erosion as it is influenced by long-term LULC changes. The 13,100 km^2 watershed was delineated into four sub-catchments Galas, Pergau, Lebir and Nenggiri for precise result estimation and ease of execution. GIS-based Universal Soil Loss Equation(USLE) model was used to predict soil loss in this study. The model inputs used for the temporal and spatial calculation of soil erosion include rainfall erosivity factor,topographic factor, land cover and management factor as well as erodibility factor. The results showed that 67.54% of soil loss is located under low erosion potential(reversible soil loss) or 0-1 t ha^(-1) yr^(-1) soil loss in Galas, 59.17% in Pergau, 53.32% in Lebir and 56.76% in Nenggiri all under the 2013 LULC *** from the correlation of soil erosion rates with LULC changes indicated that cleared land in all the four catchments and under all LULC conditions(1984-2013) appears to be the dominant with the highest erosion losses. Similarly, grassland and forest were also observed to regulate erosion rates in the area. This is because the vegetation cover provided by these LULC types protects the soil from direct impact of rain drops which invariably reduce soil loss to the barest minimum. Overall, it was concluded that the results have shown the significance of LULC in the control of erosion. Maps generated from the study may be usef

关键词： Soil erosion USLE Landuse/cover changes GIS Malaysia

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Using Susceptible-Exposed-Infectious-Recovered Model to Forecast Coronavirus Outbreak

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Computers, Materials & Continua 2021年第5期67卷 1595-1612页

作者： Debabrata Dansana Raghvendra Kumar Arupa Parida Rohit Sharma Janmejoy Das Adhikari Hiep Van Le Binh Thai Pham Krishna Kant Singh Biswajeet Pradhan Department of Computer Science and Engineering GIET UniversityGunupurOdishaIndia Department of Electronics&Communication Engineering Faculty of Engineering and TechnologySRM Institute of Science and TechnologyNCR CampusDelhi-NCR CampusModinagarGhaziabadIndia Institute of Research and Development Duy Tan UniversityDa Nang550000Viet Nam University of Transport Technology Hanoi100000Viet Nam Department of ECE KIET Group of InstitutionsGhaziabadIndia Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) University of Technology SydneySydneyNSW2007Australia Department of Energy and Mineral Resources Engineering Sejong UniversitySeoul05006Korea Center of Excellence for Climate Change Research King Abdulaziz UniversityJeddah21589Saudi Arabia Earth Observation Center Institute of Climate ChangeUniversiti Kebangsaan Malaysia43600SelangorMalaysia

The Coronavirus disease 2019(COVID-19)outbreak was rst discovered in Wuhan,China,and it has since spread to more than 200 *** World Health Organization proclaimed COVID-19 a public health emergency of international concern on January 30,***,a quickly spreading infection that could jeopardize the well-being of countless individuals requires prompt action to forestall the malady in a timely ***19 is a major threat worldwide due to its ability to rapidly *** vaccines are yet available for *** objective of this paper is to examine the worldwide COVID-19 pandemic,specically studying Hubei Province,China;Taiwan;South Korea;Japan;and Italy,in terms of exposed,infected,recovered/deceased,original conrmed cases,and predict conrmed cases in specic countries by using the susceptible-exposed-infectious-recovered model to predict the future outbreak of *** applied four differential equations to calculate the number of conrmed cases in each country,plotted them on a graph,and then applied polynomial regression with the logic of multiple linear regression to predict the further spread of the *** also compared the calculated and predicted cases of conrmed population and plotted them in the graph,where we could see that the lines of calculated and predicted cases do intersect with each other to give the perfect true results for the future spread of the *** study considered the cases from 22 January 2020 to 25 April 2020.

关键词： COVID-19 SEIR forecasting global pandemic predict conrmed case

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Multi-pressure based environmental vulnerability assessment in a coastal area of Bangladesh: A case study on Cox's Bazar

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Geomatica 2024年第2期76卷

作者： Roy, Sanjoy Saha, Monojit Hasan, Md Mehedi Razzaque, Afifa Sumaiya, Nusrat Hoque, Muhammad Al-Amin Misiūnė, Ieva Ambrosino, Maurizio Depellegrin, Daniel Bengal Institute - Architecture Landscapes and Settlements Dhaka Bangladesh Landscape Analysis and Management Laboratory Department of Geography University of Girona Girona Spain Geographical Sciences University of Maryland College Park MD United States Cryospheric Sciences Laboratory NASA Goddard Space Flight Center GreenbeltMD United States Department of Geography and Environment University of Dhaka Dhaka Bangladesh Department of Geography and Environment Jagannath University Dhaka Bangladesh Centre for Advanced Modelling and Geospatial Information Systems Faculty of Engineering and IT The University of Technology Sydney NSW Australia Institute of Geosciences Vilnius University Vilnius Lithuania Department of Science and Technology University of Sannio Benevento Italy IHCantabria - Instituto de Hidráulica Ambiental de la Universidad de Cantabria Santander Spain

Bangladesh ranks among the top 10 countries globally in terms of climate change impacts and faces numerous anthropogenic and natural pressures. Cox's Bazar, its primary tourist district, is experiencing severe degradation of its physical and ecological environments due to anthropogenic disturbances and climate change. To improve its environmental quality and preserve its ecological resources effectively, it is essential to develop a spatial decision support instrument addressing multi-pressures and cumulative environmental vulnerability (EV). This study presents an expert opinion-independent, scalable, and customizable spatial methodological framework, integrating multi-sourced geospatial data with GIS-based Fuzzy Logic to assess spatial distributions of five pressure groups and their resulting EV in Cox's Bazar. 18 criteria were chosen based on a structured literature review to evaluate the five pressure groups. Results revealed that 17 % to 27 % of the study area is exposed to high to very high hydro-meteorological, topographic, land resource, anthropogenic, and natural hazard pressures. The EV results indicated that one-third of the study area, majorly covering Kutubdia, Pekua, Cox's Bazar Sadar, Teknaf, and Ukhia upazilas, is highly environmentally vulnerable. For enhanced environmental protection, this study improved the existing method of environmental protection zoning by introducing a novel zoning approach that integrates in-situ biodiversity data with EV data. This new zoning method delineated 24 % (555 sq. km.) as strict, 45 % (1047 sq. km.) as medium, and 31 % (725 sq. km.) as soft protection zones in the study area. The sensitivity analysis identified land resource pressure as the most influential component of EV. With a correlation coefficient of 0.91, the accuracy assessment confirms a high level of reliability in the EV results. This study provides valuable insights into environmental pressures and vulnerability in Cox's Bazar, which are crucial for i

关键词： Anthropogenic

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