Utilizing Polarization Diversity in GBSAR Data-Based Object Classification DOI Creative Commons
Filip Turčinović, Marin Kačan, Dario Bojanjac

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(7), P. 2305 - 2305

Published: April 5, 2024

In recent years, the development of intelligent sensor systems has experienced remarkable growth, particularly in domain microwave and millimeter wave sensing, thanks to increased availability affordable hardware components. With smart Ground-Based Synthetic Aperture Radar (GBSAR) system called GBSAR-Pi, we previously explored object classification applications based on raw radar data. Building upon this foundation, study, analyze potential utilizing polarization information improve performance deep learning models GBSAR The data are obtained with a operating at 24 GHz both vertical (VV) horizontal (HH) polarization, resulting two matrices (VV HH) per observed scene. We present several approaches demonstrating integration such into modified ResNet18 architecture. also introduce novel Siamese architecture tailored accommodate dual input results indicate that simple concatenation method is most promising approach underscore importance considering antenna merging strategies

Language: Английский

Landslide susceptibility mapping (LSM) based on different boosting and hyperparameter optimization algorithms: A case of Wanzhou District, China DOI Creative Commons
Deliang Sun, Jing Wang, Haijia Wen

et al.

Journal of Rock Mechanics and Geotechnical Engineering, Journal Year: 2024, Volume and Issue: 16(8), P. 3221 - 3232

Published: Feb. 7, 2024

Boosting algorithms have been widely utilized in the development of landslide susceptibility mapping (LSM) studies. However, these possess distinct computational strategies and hyperparameters, making it challenging to propose an ideal LSM model. To investigate impact different boosting hyperparameter optimization on LSM, this study constructed a geospatial database comprising 12 conditioning factors, such as elevation, stratum, annual average rainfall. The XGBoost (XGB), LightGBM (LGBM), CatBoost (CB) were employed construct Furthermore, Bayesian (BO), particle swarm (PSO), Hyperband (HO) applied optimizing exhibited varying performances, with CB demonstrating highest precision, followed by LGBM, XGB showing poorer precision. Additionally, displayed HO outperforming PSO BO performance. HO-CB model achieved boasting accuracy 0.764, F1-score 0.777, area under curve (AUC) value 0.837 for training set, AUC 0.863 test set. was interpreted using SHapley Additive exPlanations (SHAP), revealing that slope, curvature, topographic wetness index (TWI), degree relief, elevation significantly influenced landslides area. This offers scientific reference disaster prevention research. examines utilization various Wanzhou District. It proposes HO-CB-SHAP framework effective approach accurately forecast disasters interpret models. limitations exist concerning generalizability data processing, which require further exploration subsequent

Language: Английский

Citations

10
Landslide susceptibility mapping core-base factors and models’ performance variability: a systematic review DOI
Santos Daniel Chicas, Heng Li, Nobuya Mizoue

et al.

Natural Hazards, Journal Year: 2024, Volume and Issue: 120(14), P. 12573 - 12593

Published: May 29, 2024

Language: Английский

Citations

8
Integration of GIS and Machine Learning Techniques for Mapping the Landslide-Prone Areas in the State of Goa, India DOI Creative Commons
Babitha Ganesh, Shweta Vincent, Sameena Pathan

et al.

Journal of the Indian Society of Remote Sensing, Journal Year: 2023, Volume and Issue: 51(7), P. 1479 - 1491

Published: June 6, 2023

Abstract A landslide susceptibility map (LSM) assists in reducing the danger of landslides by locating landslide-prone locations within designated area. One that are prone to India's Western Ghats which Goa is a part. This article presents LSMs prepared for state using four standard machine learning algorithms, namely Logistic Regression (LR ), Support Vector Machine (SVM), K -Nearest Neighbour (KNN), and Random Forest (RF). In order create LSMs, 78-point inventory, as well 14 conditioning factors, has been used, including slope, elevation, aspect, total curvature, plan profile yearly rainfall, Stream Power Index, Topographic Wetness distance road, depth bedrock/soil depth, soil type, lithology, land use cover. The most pertinent features models' construction have chosen Pearson correlation coefficient test method. presence shown be strongly influenced slope terrain, annual rainfall. generated were classified into five levels ranging from very low level high susceptible. prediction accuracy, precision, recall, F1-score, area under ROC (AUC-ROC), True Skill Statistics (TSS) used analyse compare created various methodologies. All these algorithms perform pretty well, evidenced overall accuracy scores 81.90% LR, 83.33% SVM, 81.94% KNN, 86.11% RF. SVM RF better approaches forecasting vulnerability research area, according TSS data. maximum AUC-ROC 86% was achieved algorithm. results performance metrics lead conclusion tree-based approach appropriate producing LSM Goa. this study indicate more areas can found Sattari, Dharbandora, Sanguem, Canacona regions

Language: Английский

Citations

14
Synthetic Aperture Radar for Geosciences DOI Creative Commons
Lingsheng Meng, Chi Yan,

Suna Lv

et al.

Reviews of Geophysics, Journal Year: 2024, Volume and Issue: 62(3)

Published: Sept. 1, 2024

Abstract Synthetic Aperture Radar (SAR) has emerged as a pivotal technology in geosciences, offering unparalleled insights into Earth's surface. Indeed, its ability to provide high‐resolution, all‐weather, and day‐night imaging revolutionized our understanding of various geophysical processes. Recent advancements SAR technology, that is, developing new satellite missions, enhancing signal processing techniques, integrating machine learning algorithms, have significantly broadened the scope depth geosciences. Therefore, it is essential summarize SAR's comprehensive applications for especially emphasizing recent technologies applications. Moreover, current SAR‐related review papers primarily focused on or data techniques. Hence, integrates with features needed highlight significance addressing challenges well explore potential solving complex geoscience problems. Spurred by these requirements, this comprehensively in‐depth reviews broadly including aspects air‐sea dynamics, oceanography, geography, disaster hazard monitoring, climate change, geosciences fusion. For each applied field, scientific produced because are demonstrated combining techniques characteristics phenomena Further outlooks also explored, such other conducting interdisciplinary research offer With support deep learning, synergy will enhance capability model, simulate, forecast greater accuracy reliability.

Language: Английский

Citations

6
The Identification and Influence Factor Analysis of Landslides Using SBAS-InSAR Technique: A Case Study of Hongya Village, China DOI Creative Commons

Zhanxi Wei,

Yingjun Li,

Jianhui Dong

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(18), P. 8413 - 8413

Published: Sept. 19, 2024

On 1 September 2022, a landslide in Hongya Village, Weiyuan Town, Huzhu Tu Autonomous County, Qinghai Province, caused significant casualties and economic losses. To mitigate such risks, InSAR technology is employed due to its wide coverage, all-weather operation, cost-effectiveness detecting landslides. In this study, focusing on the SBAS-InSAR Sentinel-1A satellite data from July 2021 September/October 2022 were used accurately identify areas of active landslides analyze deformation trends, combination with geological characteristics rainfall data. The results showed that strong was detected middle back maximum rate approximately -13 mm/year. surface consisted mainly Upper Pleistocene wind-deposited loess, which extremely sensitive water. closely related rainfall, increased increase rainfall. research prove ascending descending orbit based highly feasible field monitoring great practical significance for disaster prevention mitigation.

Language: Английский

Citations

4
Demystifying machine learning for predictive analytics in construction DOI
Juan Manuel Dávila Delgado

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 463 - 486

Published: Jan. 1, 2025

Language: Английский

Citations

0
Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam DOI Open Access
Huu Duy Nguyen, Quoc Huy Nguyen, Quan Vu Viet Du

et al.

Geological Journal, Journal Year: 2023, Volume and Issue: 59(2), P. 636 - 658

Published: Sept. 20, 2023

Landslides lead to widespread devastation and significant loss of life in mountainous regions around the world. Susceptibility assessments can provide critical data help decision‐makers, for example, local authorities other organizations, mitigating landslide risk, although accuracy existing studies needs be improved. This study aims assess susceptibility Thua Thien Hue province Vietnam using deep neural networks (DNNs) swarm‐based optimization algorithms, namely Adam, stochastic gradient descent (SGD), Artificial Rabbits Optimization (ARO), Tuna Swarm (TSO), Sand Cat (SCSO), Honey Badger Algorithm (HBA), Marine Predators (MPA) Particle (PSO). The locations 945 landslides occurring between 2012 2022, along with 14 conditioning factors, were used as input build DNN DNN‐hybrid models. performance proposed models was evaluated statistical indices receiver operating characteristic curve, area under curve (AUC), root mean square error, absolute error (MAE), R 2 accuracy. All had a high prediction. DNN‐MPA model highest AUC value (0.95), followed by DNN‐HBA DNN‐ARO DNN‐Adam DNN‐SGD DNN‐TSO (0.93), DNN‐PSO (0.9) finally DNN‐SCSO (0.83). High‐precision have identified that majority western region is very highly susceptible landslides. Models like aforementioned ones support decision‐makers updating large‐scale sustainable land‐use strategies.

Language: Английский

Citations

8
A novel prediction model construction and result interpretation method for slope deformation of deep excavated expansive soil canals DOI
Jiang Hu, Xing Li

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 236, P. 121326 - 121326

Published: Aug. 26, 2023

Language: Английский

Citations

6
Risk Assessment of Multi-Hazards in Hangzhou: A Socioeconomic and Risk Mapping Approach Using the CatBoost-SHAP Model DOI Creative Commons

Bofan Yu,

Jiaxing Yan,

Yunan Li

et al.

International Journal of Disaster Risk Science, Journal Year: 2024, Volume and Issue: 15(4), P. 640 - 656

Published: Aug. 1, 2024

Abstract As the global push for sustainable urban development progresses, this study, set against backdrop of Hangzhou City, one China’s megacities, addressed conflict between expansion and occurrence geological hazards. Focusing on predominant hazards troubling Hangzhou—urban road collapse, land subsidence, karst collapse—we introduced a Categorical Boosting-SHapley Additive exPlanations (CatBoost-SHAP) model. This model not only demonstrates strong performance in predicting selected typical hazards, with area under curve (AUC) values reaching 0.92, 0.94, respectively, but also, through incorporation explainable SHAP, visually presents prediction process, interrelations evaluation factors, weight each factor. Additionally, study undertook multi-hazard evaluation, producing susceptibility zoning map multiple while performing tailored analysis by integrating economic population density factors Hangzhou. research enables decision makers to transcend “black box” limitations machine learning, facilitating informed making strategic resource allocation scheduling based demographic area. approach holds potential offer valuable insights cities worldwide.

Language: Английский

Citations

2
Machine learning and deep learning-based landslide susceptibility mapping using geospatial techniques in Wayanad, Kerala state, India DOI Creative Commons

P. LOKESH,

C Madhesh,

Aneesh Mathew

et al.

HydroResearch, Journal Year: 2024, Volume and Issue: 8, P. 113 - 126

Published: Oct. 12, 2024

Language: Английский

Citations

2