Risk Assessment of Urban Infrastructure Vulnerability to Meteorological Disasters: A case study of Dongguan, China DOI Creative Commons
Fan Li, Yan Li, Matteo Rubinato

et al.

International Journal of Disaster Risk Reduction, Journal Year: 2024, Volume and Issue: 114, P. 104943 - 104943

Published: Oct. 31, 2024

Language: Английский

Prediction of drought-flood prone zones in inland mountainous regions under climate change with assessment and enhancement strategies for disaster resilience in high-standard farmland DOI Creative Commons
Yongheng Shen,

Qingxia Guo,

Zhenghao Liu

et al.

Agricultural Water Management, Journal Year: 2025, Volume and Issue: 309, P. 109349 - 109349

Published: Feb. 5, 2025

Language: Английский

Citations

1
Exploring the Intersection of Machine Learning and Big Data: A Survey DOI Creative Commons
Ηλίας Δρίτσας, Μαρία Τρίγκα

Machine Learning and Knowledge Extraction, Journal Year: 2025, Volume and Issue: 7(1), P. 13 - 13

Published: Feb. 7, 2025

The integration of machine learning (ML) with big data has revolutionized industries by enabling the extraction valuable insights from vast and complex datasets. This convergence fueled advancements in various fields, leading to development sophisticated models capable addressing complicated problems. However, application ML environments presents significant challenges, including issues related scalability, quality, model interpretability, privacy, handling diverse high-velocity data. survey provides a comprehensive overview current state applications data, systematically identifying key challenges recent field. By critically analyzing existing methodologies, this paper highlights gaps research proposes future directions for scalable, interpretable, privacy-preserving techniques. Additionally, addresses ethical societal implications emphasizing need responsible equitable approaches harnessing these technologies. presented aim guide contribute ongoing discourse on

Language: Английский

Citations

1
Explainable Artificial Intelligence for Sustainable Urban Water Systems Engineering DOI Creative Commons

Shofia Saghya Infant,

A.S. Vickram,

A. Saravanan

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104349 - 104349

Published: Feb. 1, 2025

Language: Английский

Citations

1
SHAP-Driven Explainable Artificial Intelligence Framework for Wildfire Susceptibility Mapping Using MODIS Active Fire Pixels: An In-Depth Interpretation of Contributing Factors in Izmir, Türkiye DOI Creative Commons
Muzaffer Can İban, Oktay Aksu

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(15), P. 2842 - 2842

Published: Aug. 2, 2024

Wildfire susceptibility maps play a crucial role in preemptively identifying regions at risk of future fires and informing decisions related to wildfire management, thereby aiding mitigating the risks potential damage posed by wildfires. This study employs eXplainable Artificial Intelligence (XAI) techniques, particularly SHapley Additive exPlanations (SHAP), map Izmir Province, Türkiye. Incorporating fifteen conditioning factors spanning topography, climate, anthropogenic influences, vegetation characteristics, machine learning (ML) models (Random Forest, XGBoost, LightGBM) were used predict wildfire-prone areas using freely available active fire pixel data (MODIS Active Fire Collection 6 MCD14ML product). The evaluation trained ML showed that Random Forest (RF) model outperformed XGBoost LightGBM, achieving highest test accuracy (95.6%). All classifiers demonstrated strong predictive performance, but RF excelled sensitivity, specificity, precision, F-1 score, making it preferred for generating conducting SHAP analysis. Unlike prevailing approaches focusing solely on global feature importance, this fills critical gap employing summary dependence plots comprehensively assess each factor’s contribution, enhancing explainability reliability results. analysis reveals clear associations between such as wind speed, temperature, NDVI, slope, distance villages with increased susceptibility, while rainfall streams exhibit nuanced effects. spatial distribution classes highlights areas, flat coastal near settlements agricultural lands, emphasizing need enhanced awareness preventive measures. These insights inform targeted management strategies, highlighting importance tailored interventions like firebreaks management. However, challenges remain, including ensuring selected factors’ adequacy across diverse regions, addressing biases from resampling spatially varied data, refining broader applicability.

Language: Английский

Citations

6
Adversarial Attacks in Machine Learning: Key Insights and Defense Approaches DOI
Yahya Layth Khaleel, Mustafa Abdulfattah Habeeb, Hussein Alnabulsi

et al.

Applied Data Science and Analysis, Journal Year: 2024, Volume and Issue: 2024, P. 121 - 147

Published: Aug. 7, 2024

There is a considerable threat present in genres such as machine learning due to adversarial attacks which include purposely feeding the system with data that will alter decision region. These are committed presenting different models way model would be wrong its classification or prediction. The field of study still relatively young and has develop strong bodies scientific research eliminate gaps current knowledge. This paper provides literature review defenses based on highly cited articles conference published Scopus database. Through assessment 128 systematic articles: 80 original papers 48 till May 15, 2024, this categorizes reviews from domains, Graph Neural Networks, Deep Learning Models for IoT Systems, others. posits findings identified metrics, citation analysis, contributions these studies while suggesting area’s further development robustness’ protection mechanisms. objective work basic background defenses, need maintaining adaptability platforms. In context, contribute building efficient sustainable mechanisms AI applications various industries

Language: Английский

Citations

5
Disaster Management Based on Biodiversity Conservation Using Remote Sensing Data Analysis Using Machine Learning Model DOI
Pokkuluri Kiran Sree,

T. Mounika,

Neha Devi

et al.

Remote Sensing in Earth Systems Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 8, 2025

Language: Английский

Citations

0
Trustworthy and Explainable Federated System for Extracting Descriptive Rules in a Data Streaming Environment DOI Creative Commons

María Asunción Padilla-Rascón,

Ángel Miguel García-Vico, C. J. Carmona

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104137 - 104137

Published: Jan. 1, 2025

Language: Английский

Citations

0
Flood resilience through hybrid deep learning: Advanced forecasting for Taipei's urban drainage system DOI
Li‐Chiu Chang,

Ming-Ting Yang,

Fi‐John Chang

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 379, P. 124835 - 124835

Published: March 7, 2025

Language: Английский

Citations

0
Disaster Management Systems: Utilizing YOLOv9 for Precise Monitoring of River Flood Flow Levels Using Video Surveillance DOI

G. Shankar,

M. Kalaiselvi Geetha,

P. Ezhumalai

et al.

SN Computer Science, Journal Year: 2025, Volume and Issue: 6(3)

Published: March 14, 2025

Language: Английский

Citations

0
Digital technologies for the Sustainable Development Goals DOI Creative Commons
Dharmendra Hariyani, Poonam Hariyani, Sanjeev Mishra

et al.

Green Technologies and Sustainability, Journal Year: 2025, Volume and Issue: unknown, P. 100202 - 100202

Published: March 1, 2025

Language: Английский

Citations

0