Biswajeet Pradhan

Biswajeet Pradhan
University of Technology Sydney | UTS · Director, Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering & IT

BSc.(Hons)-Distinction; MSc.; MTech; PhD; habil.

About

993
Publications
628,879
Reads
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45,039
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Introduction
Distinguished Professor Biswajeet Pradhan is an internationally established scientist in the field of Geospatial Information Systems (GIS), remote sensing and image processing, complex modelling/geo-computing, machine learning and soft-computing applications, natural hazards and environmental modelling. He is the Director of the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS) at the Faculty of Engineering and IT. He is also the distinguished professor at the University of Technology, Sydney. He is listed as the World’s most Highly Cited researcher by Clarivate Analytics Report in 2018, 2017 and 2016 as one of the world’s most influential mind. In 2018, he has been awarded as World Class Professor by the Ministry of Research, Tech. and Higher Education, Indonesia.
Additional affiliations
August 2008 - December 2010
Technische Universität Dresden

Publications

Publications (993)
Article
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Internet of Behavior is the recent trend in Internet of Things (IoT) that analyzes the behaviour of an individual using huge amount of data collected from his/her activities. The behavioural data collection process from an individual to a data center in the network layer of IoT is addressed by RPL downward routing policy. A hybrid mode of operation...
Article
Droughts are the most spatially complex natural hazards that exert global impacts and are further aggravated by climate change. The investigation of drought events is challenging as it involves numerous factors ranging from detection and assessment to modelling, management and mitigation. The analysis of these factors and their quantitative assessm...
Article
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The demand for food delivery services (FDSs) during the COVID-19 crisis has been fuelled by consumers who prefer to order meals online and have it delivered to their door than to wait at a restaurant. Since many restaurants moved online and joined FDSs such as Uber Eats, Menulog, and Deliveroo, customer reviews on internet platforms have become a v...
Article
Performing the most up-to-date and accurate vulnerability assessment is key to an effective earthquake disaster management. In cities like Istanbul (Turkey) with a high rate of urban expansion, the safety of the residents must not be neglected. The challenges in such studies are related to the lack of a training dataset. Some areas are highly prone...
Article
Rainfall variation causes frequent unexpected disasters all over the world. Increasing rainfall intensity significantly escalates soil erosion and soil erosion related hazards. Forecasting accurate rainfall helps early detection of soil erosion vulnerability and can minimise the damages by taking appropriate measures caused by severe storms, drough...
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In the present work, a deep learning-based network called LeNet is applied for accurate grassland map production from Sentinel-2 data for the Greater Sydney region, Australia. First, we apply the technique to the base date Sentinel-2 data (non-seasonal) to make the vegetation maps. Then, we combine short time-series (seasonal) data and enhanced veg...
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Machine learning (ML) has emerged as a critical enabling tool in the sciences and industry in recent years. Today's machine learning algorithms can achieve outstanding performance on an expanding variety of complex tasks-thanks to advancements in technique, the availability of enormous databases, and improved computing power. Deep learning models a...
Article
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Since late 2019, the COVID-19 biological disaster has informed us once again that, essentially, learning from best practices from past experiences is envisaged as the top strategy to develop disaster management (DM) resilience. Particularly in Indonesia, however, DM activities are challenging, since we have not experienced such a disaster, implying...
Article
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During the COVID-19 crisis, customers’ preference in having food delivered to their doorstep instead of waiting in a restaurant has propelled the growth of food delivery services (FDSs). With all restaurants going online and bringing FDSs onboard, such as UberEATS, Menulog or Deliveroo, customer reviews on online platforms have become an important...
Article
Floods are regarded as one of the most devastating meteorological hazards globally, with severe socio-economic and environmental impacts. Over the years, various machine learning algorithms have been integrated with Geographic Information Systems (GIS) and Remote Sensing (RS) techniques to optimize the performance of flood susceptibility models. Ho...
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This study is focused on developing an approach for spatial mapping of groundwater by considering four types of factors (geological, topographical, hydrological, and climatic factors), and by using different bivariate statistical models, such as frequency ratio (FR) and Shannon’s entropy (SE). The developed approach was applied in a fractured aquif...
Article
Droughts are one of the most devastating and recurring natural disaster due to a multitude of reasons. Among the different drought studies, drought forecasting is one of the key aspects of effective drought management. The occurrence of droughts is related to a multitude of factors which is a combination of hydro-meteorological and climatic factors...
Article
Dengue Fever (DF) is a common vector-borne disease with catastrophic health implications. DF prediction modelling is a challenging task, although technologies such as Geographical Information Systems (GIS) and spatial statistics have improved our understanding of dengue dynamics. In this paper, we create a robust data analysis model to (i) provide...
Book
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Floods and flash floods with hydrometeorological are the most devastating natural disasters causing massive damages to natural and man-made features. Flood hazards are a major threat to human life (injure or death of man and animal life), properties (agricultural area, yield production, building, and homes), and infrastructures (bridges, roads, rai...
Article
This study proposed a robust artificial intelligence (AI) model based on the social behaviour of the imperialist competitive algorithm (ICA) and artificial neural network (ANN) for modelling the deflection of reinforced concrete beams, abbreviated as ICA-ANN model. Accordingly, the ICA was used to adjust and optimize the parameters of an ANN model...
Article
Soil erosion hazard is one of the prominent climate hazards that negatively impact countries’ economies and livelihood. According to the global climate index, Sri Lanka is ranked among the first ten countries most threatened by climate change during the last three years (2018–2020). However, limited studies were conducted to simulate the impact of...
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Background Artificial intelligence technologies in classification/detection of COVID-19 positive cases suffer from generalizability. Moreover, accessing and preparing another large dataset is not always feasible and time-consuming. Several studies have combined smaller COVID-19 CT datasets into “supersets” to maximize the number of training samples...
Chapter
Spatial Modeling of Flood Risk and Hazard: Societal Implication offers a flexible medium for the study of the prediction and assessment of flood risk, human interference, infrastructure loss, damages of properties, and management strategies. The main aim of this book is to highlight the applications of remote sensing (RS), GIS modeling,GIS modellin...
Article
The stringent COVID-19 lockdown measures in 2020 significantly impacted people's mobility and air quality worldwide. This study presents an assessment of the impacts of the lockdown and the subsequent reopening on air quality and people's mobility in the United Arab Emirates (UAE). Google's community mobility reports and UAE's government lockdown m...
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To be proactive in mountain hazard mitigation, landslide disaster assessments are becoming increasingly urgent. In this study, three modeling techniques, namely, support vector machine (SVM), convolutional neural network (CNN-1D), and (CNN-2D), were applied and their outcomes were compared for landslide susceptibility mapping at Asir Region, Saudi...
Article
Rockfalls and unstable slopes pose a serious threat to people and property along roads/highways in the southwestern mountainous regions of Saudi Arabia. In this study, the application of terrestrial light detection and ranging (LiDAR) technology was applied aiming to propose a strategy to analyze and accurately depict the detection of rockfall chan...
Preprint
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The ever-growing volume of satellite imagery data presents a challenge for industry and governments making data-driven decisions based on the timely analysis of very large data sets. Commonly used deep learning algorithms for automatic classification of satellite images are time and resource-intensive to train. The cost of retraining in the context...
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Geohazard risk is high in Arab countries due to ineffective disaster preparedness measures, mismanagement, lack of public awareness, inadequate funding and lack of stakeholder support. One such country is Egypt, which is hit by floods every year that cost lives and bring the economy to a standstill. Moreover, not much has been done to map flood-pro...
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Central Zagros region in Iran is a major hotspot of carbon storage and sequestration which has experienced severe land cover change in recent decades that has led to carbon emission. In this research, using temporal Landsat images, land cover maps were produced and used in Land Change Modeler to predict land cover changes in 2020, 2030, 2040 and 20...
Article
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The aquaculture expansion has made significant contributions to global food security, socio-economic development and, if implemented sustainably, can help preserve stable coastal environments. This study explicitly details the rapid expansion of large-scale aquaculture growth across the Kolleru and Upputeru regions of South India. We developed a no...
Article
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Lithological mapping is a critical aspect of geological mapping that can be useful in studying the mineralization potential of a region and has implications for mineral prospectivity mapping. This is a challenging task if performed manually, particularly in highly remote areas that require a large number of participants and resources. The combinati...
Article
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The main objective of this study is to integrate adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and artificial neural network (ANN) to design an integrated supervised committee machine artificial intelligence (SCMAI) model to spatially predict the groundwater vulnerability to seawater intrusion in Gharesoo-Gorgan Rood c...
Article
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This study evaluates state-of-the-art machine learning models in predicting the most sustainable arsenic mitigation preference. A Gaussian distribution-based Naïve Bayes (NB) classifier scored the highest Area Under the Curve (AUC) of the Receiver Operating Characteristic curve (0.82), followed by Nu Support Vector Classification (0.80), and K-Neig...
Article
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The present work aims to identify the geochemical signatures of copper deposits in the Igherm area (Anti-Atlas, Morocco) using stream sediment data. We applied factor analysis on the data normalized by log transformation. This approach identified three geochemical data associations (F1, F2, and F3) which were then used to calculate the values of th...
Article
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Debris flows are rapid mass movements with a mixture of rock, soil and water. High-intensity rainfall events have triggered multiple debris flows around the globe, making it an important concern from the disaster management perspective. This study presents a numerical model called debris flow simulation 2D (DFS 2D) and applicability of the proposed...
Article
Buildings are among the most important elements in the urban structure that can affect urban planning. Therefore, it is important to create the footprint of the buildings, especially in developing cities, which is highly time-consuming and costly. Although LiDAR technology has already been used for this purpose, the need to process voluminous amoun...
Article
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Wildfires drive deforestation that causes various losses. Although many studies have used spatial approaches, a multi-dimensional analysis is required to determine priority areas for mitigation. This study identified priority areas for wildfire mitigation in Indonesia using a multi-dimensional approach including disaster, environmental, historical,...
Preprint
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Lung cancer is the leading cause of cancer death worldwide and a good prognosis depends on early diagnosis. Unfortunately, screening programs for the early diagnosis of lung cancer are uncommon. This is in-part due to the at-risk groups being located in rural areas far from medical facilities. Reaching these populations would require a scaled appro...
Article
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In Disaster Management (DM), reusing knowledge of best practices from past experiences is envisaged as the best approach for dealing with future disasters. But analysing and modelling processes involved in those experiences is a well-known challenge. But the efficient storage of those processes to allow reuse by others in future DM endeavours is ev...
Article
Detection of hydrothermal alteration zones (HAZs) associated with porphyry copper systems using remote sensing imagery is a crucial stage for discovering high potential zone of ore mineralization. Statistical model-based clustering methods have great potential for automatic and accurate detection of hydrothermal alteration minerals using hyperspect...
Article
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The presence of a well-trained, mobile CNN model with a high accuracy rate is imperative to build a mobile-based early breast cancer detector. In this study, we propose a mobile neural network model breast cancer mobile network (BreaCNet) and its implementation framework. BreaCNet consists of an effective segmentation algorithm for breast thermogra...
Article
Existing automated road extraction approaches concentrate on regional accuracy rather than road shape and connectivity quality. Most of these techniques produce discontinuous outputs caused by obstacles, such as shadows, buildings, and vehicles. This study proposes a shape and connectivity-preserving road identification deep learning-based architec...
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This study proposes a new model for land suitability for educational facilities based on spatial product development to determine the optimal locations for achieving education targets in West Java, Indonesia. Single-aspect approaches, such as accessibility and spatial hazard analyses, have not been widely applied in suitability assessments on the l...
Article
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Iris biometric identification allows for contactless authentication, which helps to avoid the transmission of diseases like COVID-19. Biometric systems become unstable and hazardous due to spoofing attacks involving contact lenses, replayed video, cadaver iris, synthetic Iris, and printed iris. This work demonstrates the iris presentation attacks d...
Article
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With the increasing computational facilities and data availability, machine learning (ML) models are gaining wide attention in landslide modeling. This study evaluates the effect of spatial resolution and data splitting, using five different ML algorithms (naïve bayes (NB), K nearest neighbors (KNN), logistic regression (LR), random forest (RF) and...
Preprint
Erosion and flood events can damage soils, water, quality, and sediment transportation, causing many cumulative hazards. In developing countries, such as Iran, the empirical models, which are low-cost procedures to mitigate the environmental hazards, are necessary to plan the watersheds. Hence, the main aim of this study is to evaluate erosion and...
Article
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Several drought indices have been developed based on various processes (e.g., precipitation, soil moisture, vegetation health) that respond differently to modes of climate variability, shadowing their relatability to teleconnections, which in turn, limits drought forecasting. In this study, we advanced the multivariate analysis of droughts by using...
Article
Displacement of rock mass in tunnels and underground mines is considered one of the most hazardous phenomena that can cause the collapse of the structures. In this study, the rock properties, such as the depth of the tunnels (H), the angle of rock layers (α), anti-bending moment (Wc), the width of the tunnels (b), the tensile strength of rock layer...
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The robustness of landslide prediction models has become a major focus of researchers worldwide. We developed two novel hybrid predictive models that combine the self-organizing, deep-learning group method of data handling (GMDH) with two swarm intelligence optimization algorithms, i.e., cuckoo search algorithm (CSA) and whale optimization algorith...
Article
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Knowledge of the groundwater potential, especially in an arid region, can play a major role in planning the sustainable management of groundwater resources. In this study, nine machine learning (ML) algorithms-namely, Artificial Neural Network (ANN), Decision Jungle (DJ), Averaged Perceptron (AP), Bayes Point Machine (BPM), Decision Forest (DF), Lo...
Article
Droughts are the most spatially complex geohazard, which often lasts for years, thereby severely impacting socioeconomic sectors. One of the critical aspects of drought studies is developing a reliable and robust forecasting model, which could immensely help drought management planners in adopting adequate measures. Further, the prediction of droug...
Article
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Tabriz city in NW Iran is a seismic-prone province with recurring devastating earthquakes that have resulted in heavy casualties and damages. This research developed a new computational framework to investigate four main dimensions of vulnerability (environmental, social, economic and physical). An Artificial Neural Network (ANN) Model and a SWOT-Q...
Article
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Optimisation plays a key role in the application of machine learning in the spatial prediction of landslides. The common practice in optimising landslide prediction models is to search for optimal/suboptimal hyperparameter values in a number of predetermined hyperparameter configurations based on an objective function, i.e., k-fold cross-validation...
Article
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Studies relating to trends of vegetation, snowfall and temperature in the north-western Himalayan region of India are generally focused on specific areas. Therefore, a proper understanding of regional changes in climate parameters over large time periods is generally absent, which increases the complexity of making appropriate conclusions related t...
Article
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Iris biometric detection provides contactless authentication, preventing the spread of COVID-19-like contagious diseases. However, these systems are prone to spoofing attacks attempted with the help of contact lenses, replayed video, and print attacks, making them vulnerable and unsafe. This paper proposes the iris liveness detection (ILD) method t...
Article
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The application of machine learning (ML) algorithms for processing remote sensing data is momentous, particularly for mapping hydrothermal alteration zones associated with porphyry copper deposits. The unsupervised Dirichlet Process (DP) and the supervised Support Vector Machine (SVM) techniques can be executed for mapping hydrothermal alteration z...
Article
Globally, the frequency, intensity, and damage rate of floods have increased extensively in the last few decades. The present study proposes a spatial-based multi-criteria approach integrating mitigation capacity to map the degree of flood risk. Flood risk is calculated considering 14 relevant factors under three risk components. Thematic layers we...
Article
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In arid and semi-arid regions, groundwater is considered being the most available natural resources for different water use. However, it is being limited in quantity. As such, its sustainable development and managementent depends on based on various criteria (e.g. climatic conditions, scale, aquifer properties, etc.). This study presents three mult...
Article
Navigation, also known as discovering one’s direction, is a complex human activity. To produce effective routes, it relies on knowledge of the surroundings’ precise geometry and semantic information. Complex geometrical data can be precisely delineated with the improvement of 3D geometric models. A precise 3D geometric model containing a specifical...
Article
Rainfall thresholds are commonly utilized to forecast landslides using the historical relationship between occurrence of slope failures and rainfall in an area. SIGMA (Sistema Integrato Gestione Monitoraggion Allerta) is a rainfall threshold model, which uses the statistical distribution of rainfall for forecasting the occurrence of landslides. The...
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The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focused on the diagnosis of COVID-19 from medical images. However, these models have found limited, if any, clinical application due in part to unproven generalization to data sets beyond their source training corpus. This study investi...