Digital Commons Network^™

Leveraging Artificial Intelligence And Machine Learning To Accelerate Discovery Of Disease-Modifying Therapies In Type 1 Diabetes., Melanie R. Shapiro, Erin M. Tallon, Matthew E. Brown, Amanda L. Posgai, Mark A. Clements, Todd M. Brusko

Manuscripts, Articles, Book Chapters and Other Papers

Progress in developing therapies for the maintenance of endogenous insulin secretion in, or the prevention of, type 1 diabetes has been hindered by limited animal models, the length and cost of clinical trials, difficulties in identifying individuals who will progress faster to a clinical diagnosis of type 1 diabetes, and heterogeneous clinical responses in intervention trials. Classic placebo-controlled intervention trials often include monotherapies, broad participant populations and extended follow-up periods focused on clinical endpoints. While this approach remains the 'gold standard' of clinical research, efforts are underway to implement new approaches harnessing the power of artificial intelligence and machine learning …

A Survey On Energy Efficient Medium Access Control For Acoustic Wireless Communication Networks In Underwater Environments, Walid K. Hasan, Iftekhar Ahmad, Daryoush Habibi, Quoc Viet Phung, Mohammad Al-Fawa'reh, Kazi Yasin Islam, Ruba Zaheer, Haitham Khaled

Research outputs 2022 to 2026

Underwater communication plays a crucial role in monitoring the aquatic environment on Earth. Due to their unique characteristics, underwater acoustic channels present unique challenges including lengthy signal transmission delays, limited data transfer bandwidth, variable signal quality, and fluctuating channel conditions. Furthermore, the reliance on battery power for most Underwater Wireless Acoustic Networks (UWAN) devices, coupled with the challenges associated with battery replacement or recharging, intensifies the challenges. Underwater acoustic communications are heavily constrained by available resources (e.g., very limited bandwidth, and limited energy storage). Consequently, the role of medium access control (MAC) protocol which distributes available resources among nodes is …

Artificial Intelligence And Internet Of Things Integration In Pharmaceutical Manufacturing: A Smart Synergy, Reshma Kodumuru, Soumavo Sarkar, Varun Parepally, Jignesh Chandarana

Michigan Tech Publications

Background: The integration of artificial intelligence (AI) with the internet of things (IoTs) represents a significant advancement in pharmaceutical manufacturing and effectively bridges the gap between digital and physical worlds. With AI algorithms integrated into IoTs sensors, there is an improvement in the production process and quality control for better overall efficiency. This integration facilitates enabling machine learning and deep learning for real-time analysis, predictive maintenance, and automation—continuously monitoring key manufacturing parameters. Objective: This paper reviews the current applications and potential impacts of integrating AI and the IoTs in concert with key enabling technologies like cloud computing and data analytics, …

Learning: Human Versus Machine, Sanjay Sarma

Asian Management Insights

Outdated education paradigms must be revamped to reclaim the all-important human quality: agency.

Sanjay Sarma, CEO, President, and Dean of the Asia School of Business, Kuala Lumpur, Malaysia and the Fred Fort Flowers (1941) and Daniel Fort Flowers (1941) Professor in Mechanical Engineering at the Massachusetts Institute of Technology (MIT), shares insights on the artificial intelligence (AI)-agency revolution and how the human brain works.

Predicting The Pathway Involvement Of Compounds Annotated In The Reactome Knowledgebase, Erik D. Huckvale, Hunter N. B. Moseley

Markey Cancer Center Faculty Publications

Background/Objectives: Pathway annotations of non-macromolecular (relatively small) biomolecules facilitate biological and biomedical interpretation of metabolomics datasets. However, low pathway annotation levels of detected biomolecules hinder this type of interpretation. Thus, predicting the pathway involvement of detected but unannotated biomolecules has a high potential to improve metabolomics data analysis and omics integration. Past publications have only made use of the Kyoto Encyclopedia of Genes and Genomes-derived datasets to develop machine learning models to predict pathway involvement. However, to our knowledge, the Reactome knowledgebase has not been utilized to develop these types of predictive models.

Methods: We created a dataset ready for …

Machine Learning With Flight Data Recorder Data For Flight Fuel Consumption Predictions, Adam C. Levandowski

Theses and Dissertations

This study applies advanced Machine Learning (ML) to Flight Data Recorder (FDR) data for fuel consumption predictions. It explores feature engineering, model selection, and Hyper-Parameter Optimization (HPO) across all flight phases. Baseline models like Ordinary Least Squares (OLS) regression, Multi- Layer Perceptrons (MLPs), and decision trees are compared to Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs) with Gated Recurrent Unit (GRU) layers, and XGBoost. Results analyze segmentation strategies, tailored features, and model performance. A counterfactual analysis compares ML models to operational fuel predictions, demonstrating their deployment potential. Findings establish a foundation for future ML-driven advancements in aviation fuel optimization.

Uranium Particle Classification Using Statistical Machine Learning And Deep Neural Networks For Nuclear Forensics, Lee C. Lambert

Theses and Dissertations

The classification of uranium particles from scanning electron microscopy (SEM) imagery is critical to nuclear forensics, but has traditionally relied solely on skilled analysts whose classification accuracy and procedures may vary widely. Existing morphology lexicology [1] provides standardization guidelines to aid analysts but cannot fully address analyst variability. Using a dataset of 1,906 SEM images across 13 unevenly distributed particle classes and 73 magnification levels, final accuracy between statistical and deep learning methods were compared to find the best classification techniques. Ultimately, the deep learning model achieved an impressive 82% accuracy (80% balanced accuracy) on a withheld test set. This …

A Machine Learning/Deep Learning Investigation On Remote Manufacturing Machine State Classification, Ajeet S. Parmar

Theses and Dissertations

Determining the extent of manufacturing capabilities with respect to adversarial or hostile nations is a topic of significant importance to the Department of Defense. Manufacturing capabilities can serve as indications of a nation's industrial power and its economy of force in warfare. Remotely detecting machine operations via electromagnetic sensors may be possible via Deep Learning (DL) and Machine Learning (ML) algorithms. To predict machine states, sensor data is collected externally from a machine shop on a college campus to monitor the operating states of lathes and mills in individual and concurrent operation. Furthermore, several sensors are placed in various positions, …

Learning To Dogfight: Proximal Policy Optimization Vs. Double Deep Q Network For 2v2 Air Combat With Directed Energy Weapons In Afsim, Caden W. Wilson

Theses and Dissertations

This research utilizes reinforcement learning (RL) to train two blue agents each imbued with a directed energy weapon (DEW) in a 2v2 within visual range air combat maneuvering problem. A phased solution approach is employed to repeatedly tune and train several RL algorithm implementations: Proximal Policy Optimization (PPO) and Double Deep Q Network (DDQN). Phase I of training includes reward shaping for basic flight elements such as altitude, airspeed, and target proximity. Phase II of training builds off policies developed in Phase I, but rewards emphasize winning the aerial engagement by any means necessary. DDQN significantly outperforms PPO in Phase …

Isotopic Analysis Of Lithium Hydroxide Monohydrate Using Laser-Induced Breakdown Self-Reversal Isotopic Spectrometry (Libris) And Machine Learning, Madison R. Moran

Theses and Dissertations

High-resolution Laser-Induced Breakdown Self-Reversal Isotopic Spectrometry (LIBRIS) is implemented to record the 15.8 pm Li I 670.8 nm isotopic shift in LiOH · H₂O samples of varying 6, 7Li abundance. A simple univariate linear regression demonstrates an acquired isotopic shift of 13.813 ± 1.21 pm in samples varying from 3 to 95 ⁶Li at%. Supervised machine learning regressions are trained on self-reversal wavelength locations in order to quantify ⁶Li abundance. A stacked ensemble using two base learners yields the superlative characterization of 6Li abundance with RMSE of 5.66 at% and detection limit of 18.8 at%. Using …

Comparison Of Deep Learning And Traditional Machine Learning Models For Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers, Kesheng Wang, Donald A. Adjeroh, Wei Fang, Suzy M. Walter, Danqing Xiao, Ubolrat Piamjariyakul, Chun Xu

Health & Biomedical Sciences Faculty Publications

Mild cognitive impairment (MCI) is a clinical condition characterized by a decline in cognitive ability and progression of cognitive impairment. It is often considered a transitional stage between normal aging and Alzheimer’s disease (AD). This study aimed to compare deep learning (DL) and traditional machine learning (ML) methods in predicting MCI using plasma proteomic biomarkers. A total of 239 adults were selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort along with a pool of 146 plasma proteomic biomarkers. We evaluated seven traditional ML models (support vector machines (SVMs), logistic regression (LR), naïve Bayes (NB), random forest (RF), k-nearest neighbor …

Icu Admission Prediction For Patients With Kawasaki Disease Or Mis-C Using Machine Learning., Jiwon Woo, Rebecca Mosier, Rishima Mukherjee, Ashraf S. Harahsheh, Supriya S. Jain, Geetha Raghuveer, Balasubramanian Sundaram, Simon Lee, Michael A. Portman, Nagib Dahdah, Marianna Fabi, Todd T. Nowlen, Audrey Dionne, Mona El Ganzoury, Tyler H. Harris, Benjamin T. Barnes, Frederic Dallaire, Paul Dancey, Kambiz Norozi, Mahmoud Alsalehi, Elif Seda Selamet Tierney, Jacqueline R. Szmuszkovicz, Pei-Ni Jone, Deepa Prasad, Anji T. Yetman, Nilanjana Misra, Mark D. Hicar, Deepika Thacker, Nadine F. Choueiter, Elisa Fernandez Cooke, Daniel Mauriello, Tapas Mondal, Matthew D. Elias, Kimberly E. Mchugh, Shae A. Merves, Luis Martin Garrido-Garcia, Michael Khoury, Guillermo Larios, Bhargava Chinni, Kaashvi Pruthi, Wenyu Yang, Joseph Greenstein, Casey Taylor, Pedrom Farid, Brian W. Mccrindle, Cedric Manlhiot, International Kawasaki Disease Registry

Manuscripts, Articles, Book Chapters and Other Papers

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) and Kawasaki disease (KD) show a broad spectrum of clinical severity, from a relatively benign clinical course to requiring admission to the intensive care unit (ICU). With either, clinical deterioration may be rapid and unexpected.

OBJECTIVES: The aim of the study was to develop a machine learning (ML) model to predict future ICU admission for patients with KD or MIS-C to augment clinical decision-making.

METHODS: We developed a prediction model for ICU admission using 2,539 patientsfeatures, we developed predictive snapshot and window ML models with logistic regression, XGBoost, and random forest. Performance was …

A Comprehensive Survey Of Data-Driven Solutions For Lorawan: Challenges And Future Directions, Poonam Maurya, Abhishek Hazra, Preti Kumari, Troels Bundgaard Sørensen, Sajal K. Das

Computer Science Faculty Research & Creative Works

Long-range Wide-area Network (LoRaWAN) is an innovative and prominent communication protocol in the domain of Low-power Wide-area Networks (LPWAN), known for its ability to provide long-range communication with low energy consumption. However, the practical implementation of the LoRaWAN protocol, operating at the Medium Access Control layer and specially built to work upon the LoRa physical layer, presents numerous research challenges, including network congestion, interference, optimal resource allocation, collisions, scalability, and security. To mitigate these challenges effectively, the adoption of cutting-edge data-driven technologies such as Deep Learning (DL) and Machine Learning (ML) emerges as a promising approach. Interestingly, very few existing …

Machine Learning-Enhanced Surface-Enhanced Spectroscopic Detection Of Polycyclic Aromatic Hydrocarbons In The Human Placenta, Oara Neumann, Yilong Ju, Andres B Sanchez-Alvarado, Guodong Zhou, Weiwu Jiang, Bhagavatula Moorthy, Melissa A Suter, Ankit Patel, Peter Nordlander, Naomi J Halas

Faculty, Staff and Students Publications

The detection and identification of polycyclic aromatic hydrocarbons (PAHs) and their derivatives, polycyclic aromatic compounds (PACs), are essential for environmental and health monitoring, for assessing toxicological exposure and their associated health risks. PAHs/PACs are the most dangerous chemicals found in tobacco smoke, and cigarette use during pregnancy can convey these molecules to the developing fetus through the placenta. This exposure is associated with many negative health outcomes, from premature birth to sudden infant death syndrome and adverse neurodevelopmental disorders. This study demonstrates the use of surface-enhanced Raman and surface-enhanced infrared absorption spectroscopies for direct detection of PAHs/PACs in human placental …

Toward Quantifying Interpolation Uncertainty In Set-Line Spacing Hydrographic Surveys, Elias Adediran, Christos Kastrisios, Kim Lowell, Glen Rice, Qi Zhang

Faculty Publications

The oceans remain one of Earth’s last great unknowns, with about 74% still unmapped to modern standards. Consequently, interpolation is employed to create seamless digital bathymetric models (DBMs) from incomplete hydrographic datasets, but this introduces unquantified depth uncertainties. This study aims to estimate and characterize uncertainties arising from set-line spacing hydrographic surveys, which are important for nautical charting, navigational safety, and many other applications. By sampling at different line spacings four complete coverage testbeds that vary in slope and roughness, the study interpolates across entire testbed areas using Spline, Inverse Distance Weighting, and Linear interpolation. The resulting interpolation uncertainties are …

A Deep Learning-Based System For Automatic Detection Of Emesis With High Accuracy In Suncus Murinus, Zengbing Lu, Yimeng Qiao, Xiaofei Huang, Dexuan Cui, Julia Y H Liu, Man Piu Ngan, Luping Liu, Zhixin Huang, Zi-Tong Li, Lingqing Yang, Aleena Khalid, Yingyi Deng, Sze Wa Chan, Longlong Tu, John A Rudd

Children’s Nutrition Research Center Staff Publications

Quantifying emesis in Suncus murinus (S. murinus) has traditionally relied on direct observation or reviewing recorded behaviour, which are laborious, time-consuming processes that are susceptible to operator error. With rapid advancements in deep learning, automated animal behaviour quantification tools with high accuracy have emerged. In this study, we pioneere the use of both three-dimensional convolutional neural networks and self-attention mechanisms to develop the Automatic Emesis Detection (AED) tool for the quantification of emesis in S. murinus, achieving an overall accuracy of 98.92%. Specifically, we use motion-induced emesis videos as training datasets, with validation results demonstrating an accuracy of 99.42% for …

Systematic Evaluation Of Regression Models For The Prediction Of Influenza Cases, Mukul Kandwal

Theses

This study systematically evaluates machine learning models to forecast influenza outbreaks, aiming to enhance public health preparedness through accurate predictions. Laboratory-confirmed influenza case data from the New York State Department of Health (2009–2023) were used to train and test five regression models—XGBoost, Neural Network (MLP regressor), K-Nearest Neighbors (KNN), Random Forest, and Support Vector Regression (SVR)—with preprocessing techniques including differencing and phasing. A leave-one-season-out cross-validation framework and Symmetric Mean Absolute Percentage Error (SMAPE) were employed to rigorously assess performance. Key findings reveal that the Neural Network (MLP regressor) outperformed other models, achieving the lowest SMAPE score (23.7), underscoring the critical …

Applicable And Generalizable Machine Learning For Intelligent Welding In Automotive Manufacturing, Peng Edward Wang, Xijia Zhao

Faculty Scholarship

This review paper examines the application and challenges of machine learning (ML) in intelligent welding processes within the automotive industry, focusing on resistance spot welding (RSW) and laser welding. RSW is predominant in body-in-white assembly, while laser welding is critical for electric vehicle battery packs due to its precision and compatibility with dissimilar materials. The paper categorizes ML applications into three key areas: sensing, in-process decision-making, and post-process optimization. It reviews supervised learning models for defect detection and weld quality prediction, unsupervised learning for feature extraction and data clustering, and emerging generalizable ML approaches like transfer learning and federated learning …

Retracted: A Review Of Breast Cancer Histological Image Classification: Challenges And Limitations, Israa Faisal Jassam, Abdulrahman Abbas Mukhlif, Ahmed Adil Nafea, Mustafa Adnan Tharthar, Ahmed Isam Khudhair

Iraqi Journal for Computer Science and Mathematics

This paper comprehensively reviews the classification of breast cancer histological images. The paper discusses the research objectives, methodologies used, and conclusions drawn, as well as suggestions for the future. The study is based on the ICIAR 2018 database, which is considered one of the largest databases available to support this research. The paper also addresses major challenges such as lack of data, variation in tissue preparation, class imbalance, and computational requirements. Advanced techniques such as deep learning (DL), transfer learning and data augmentation are explored, along with innovative models such as convolutional neural networks (CNNs) and generative adversarial networks (GANs). …

Prediction Of Obstructive Coronary Artery Disease Using Coronary Calcification And Epicardial Adipose Tissue Assessments From Ct Calcium Scoring Scans, Juhwan Lee, Tao Hu, Ammar Hoori, Hao Wu, Justin N. Kim, Robert Gilkeson, Sanjay Rajagopalan, David L. Wilson

Faculty Scholarship

Background: Low-cost/no-cost non-contrast CT calcium scoring (CTCS) exams can provide direct evidence of coronary atherosclerosis. In this study, using features from CTCS images, we developed a novel machine learning model to predict obstructive coronary artery disease (CAD), as defined by the coronary artery disease-reporting and data system (CAD-RADS). Methods: This study analyzed 1324 patients from the SCOT-HEART trial who underwent both CTCS and CT angiography. Obstructive CAD was defined as CAD-RADS 4A-5, while CAD-RADS 0–3 were considered non-obstructive CAD. We analyzed clinical, Agatston-score-derived, and epicardial fat-omics features to predict obstructive CAD. The most predictive features were selected using elastic net …

Smart Green Energy Management For Campus: An Integrated Machine Learning And Reinforcement Learning Model, Charan Teja Madabathula, Kunal Agrawal, Vijen Mehta, Swathi Kasarabada, Sai Srimai Kommamuri, Guannan Liu, Jerry Gao

Faculty Research, Scholarly, and Creative Activity

Highlights: What are the main findings? A smart green energy management system integrating machine-learning model and reinforcement learning is proposed in this work to optimize energy consumption and solar generation for a campus environment. The integrated model demonstrated superior prediction performance with an RMSE of 14.72 for energy consumption and 75.45 for solar generation. What is the implication of the main finding? An efficient solution for campuses is provided to reduce grid dependency and enhance energy sustainability. A benchmark is established for future green campus initiatives by integrating advanced machine learning and reinforcement learning techniques. The increasing demand for energy …

On The Books In South Carolina: Mining For Jim Crow Laws, Kate F. Boyd, Vandana Srivastava, Lance Dupre, Christopher Frear, Nitin Gupta, Bobby Donaldson

Faculty and Staff Publications

On the Books in South Carolina: Mining for Jim Crow Laws is a collections-as-data and machine learning project by the University of South Carolina Libraries (USC), sub awarded by the University of North Carolina at Chapel Hill (UNC), and made possible by The Andrew W. Mellon Foundation, for the period of May 2022 - December 2024. Following UNC’s steps from their first year of the grant, the USC project created a text corpus of South Carolina state legislature acts passed in the period from Reconstruction through the Civil Rights Movement (1868-1968). The USC team then utilized machine learning techniques to …

Uav-Based Phytoforensics: Hyperspectral Image Analysis To Remotely Detect Explosives Using Maize (Zea Mays), Paul V. Manley, Stephen M. Via, Joel G. Burken

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Remnant explosive devices are a deadly nuisance to both military personnel and civilians. Traditional mine detection and clearing is dangerous, time-consuming, and expensive. And routine production and testing of explosives can create groundwater contamination issues. Remote detection methods could be rapidly deployed in vegetated areas containing explosives as they are known to cause stress in vegetation that is detectable with hyperspectral sensors. Hyperspectral imagery was employed in a mesocosm study comparing stress from a natural source (drought) to that of plants exposed to two different concentrations of Royal Demolition Explosive (RDX; 250 mg kg−1, 500 mg kg−1). Classification was accomplished …

Exploring Key Factors Influencing Depressive Symptoms Among Middle-Aged And Elderly Adult Population: A Machine Learning-Based Method, Ngoc Doan Thu Tran, Yi Zhen Tan, Sapphire Lin, Fang Zhao, Yee Sien Ng, Dong Ma, Jeonggil Ko, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Objective: This paper aims to investigate the key factors, including demographics, socioeconomics, physical wellbeing, lifestyle, daily activities and loneliness that can impact depressive symptoms in the middle-aged and elderly population using machine learning techniques. By identifying the most important predictors of depressive symptoms through the analysis, the findings can have important implications for early depression detection and intervention. Participants: For our cross-sectional study, we recruited a total of 976 volunteers, with a specific focus on individuals aged 50 and above. Each participant was requested to provide their demographic, socioeconomic information and undergo several physical health tests. Additionally, they were asked …

Assessing Water Quantity And Quality In The Mississippi River Valley Alluvial Aquifer And Coastal Louisiana Through Integrated Airborne Electromagnetic And Borehole Data, Michael George Henin Attia Khalil

LSU Doctoral Dissertations

Numerical modeling has contributed significantly to the understanding of groundwater systems. Many challenges are associated with constructing groundwater models which include an accurate understanding of the geology and aquifer parameters estimation. Traditionally boreholes are a successful way to capture geological features, however, boreholes often have sparse data. Airborne electromagnetic (AEM) data allows for efficient and cost-effective surveying of large areas, providing valuable information about the subsurface electrical resistivity. By bridging the gap between boreholes, AEM data offers a broader view of the aquifer system's structure and heterogeneity. However, interpreting geophysical AEM data has uncertainties. Developing a framework to apply the …

Robust Cluster Prediction Across Data Types Validates Association Of Sex And Therapy Response In Gbm, David L Gibbs, Gino Cioffi, Boris Aguilar, Kristin A Waite, Edward Pan, Jacob Mandel, Yoshie Umemura, Jingqin Luo, Joshua B Rubin, David Pot, Jill Barnholtz-Sloan

Faculty, Staff and Students Publications

Background: Previous studies have described sex-specific patient subtyping in glioblastoma. The cluster labels associated with these "legacy data" were used to train a predictive model capable of recapitulating this clustering in contemporary contexts.

Methods: We used robust ensemble machine learning to train a model using gene microarray data to perform multi-platform predictions including RNA-seq and potentially scRNA-seq.

Results: The engineered feature set was composed of many previously reported genes that are associated with patient prognosis. Interestingly, these well-known genes formed a predictive signature only for female patients, and the application of the predictive signature to male patients produced unexpected results. …

Farm Resilience, Management Practices, And Producer Sentiment: Segmenting Us Farms Using Machine Learning Algorithms, Margaret Lippsmeyer, Michael Langemeier, James R. Mintert, Nathan Thompson

Journal of Applied Farm Economics

Farm managers possess a broad spectrum of capabilities, ranging from tech-savvy, strategically focused managers to those grappling with operational challenges. Our study examines survey data from 403 US commercial producers in order to identify subsets of producers who differ in terms of resilience, management practices, producer sentiment, and other farm characteristics. Utilizing various supervised and unsupervised machine learning techniques, we uncover key farm characteristics that capture the most pronounced variations in survey responses. We then cluster the dataset using these key variables, maximizing separation between clusters and minimizing differences within clusters using Ward’s hierarchical clustering. Fisher’s exact tests are implemented …

Predicting Customer Churn In B2b Consulting Services, Mediya Alfalasi

Theses

The aim of this research was to develop a machine learning model that can predict customer churn in the consulting industry. The goal was to help businesses identify customers who might leave, allowing them to take action before it happens. To achieve this, a dataset was created by combining different data sources to reflect real-world customer behaviors. After cleaning the data and addressing issues like missing values and data imbalance, four machine learning algorithms—XGBoost, Neural Network, Random Forest, Logistic Regression, and Support Vector Machines (SVM)—were used to build the prediction models. The findings showed that XGBoost performed the best, achieving …

Fake News Detection On X (Twitter), Sultan Mayoof Almutaiwei

Theses

The most important innovation globally is the internet, which is used by a huge number of people all around the world. People use the internet for different purposes. By using different social media platforms people share content and news. Thus, some people use these internet platforms to share false news or fake news among people. Propaganda against a person, group, organization, or political party may be present in this news. For an individual, it is difficult to distinguish between false and true news over the internet. Therefore, machine learning is the smart solution that can identify fake news on the …

Landslide Susceptibility Assessment Of The Wanzhou District: Merging Landslide Susceptibility Modelling (Lsm) With Insar-Derived Ground Deformation Map, Chao Zhou, Lulu Gan, Ying Cao, Yue Wang, Samuele Segoni, Xuguo Shi, Mahdi Motagh, Ramesh P. Singh

Mathematics, Physics, and Computer Science Faculty Articles and Research

The prevalent catalog-based Landslide Susceptibility Modelling (LSM) operates under the assumption that future landslide occurrences mirror past and current patterns. Due to growing urban expansion and climate change, certain landslides follow new patterns of occurrence, disrupting the foundational assumption of catalog-based LSM and leading to constraints in the effectiveness of traditional susceptibility maps. Here, to address this problem, we proposed a method to produce more accurate and dynamic landslide susceptibility maps by coupling advanced Ensemble Machine Learning (EML) and Multi-Temporal Interferometric SAR (MT-InSAR). The Wanzhou District in Three Gorges Reservoir area of China is considered as the test site. The …