Integrated Explainable Ensemble Machine Learning Prediction of Injury Severity in Agricultural Accidents DOI Creative Commons
Omer Mermer,

Eddie Zhang,

İbrahim Demir

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 7, 2025

Abstract Agricultural injuries remain a significant occupational hazard, causing substantial human and economic losses worldwide. This study investigates the prediction of agricultural injury severity using both linear ensemble machine learning (ML) models applies explainable AI (XAI) techniques to understand contribution input features. Data from AgInjuryNews (2015–2024) was preprocessed extract relevant attributes such as location, time, age, safety measures. The dataset comprised 2,421 incidents categorized fatal or non-fatal. Various ML models, including Naïve Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GB), were trained evaluated standard performance metrics. Ensemble demonstrated superior accuracy recall compared with XGBoost achieving 100% for injuries. However, all faced challenges in predicting non-fatal due class imbalance. SHAP analysis provided insights into feature importance, gender, time emerging most influential predictors across models. research highlights effectiveness while emphasizing need balanced datasets XAI actionable insights. findings have practical implications enhancing guiding policy interventions. Highlights analyzed (2015– 2024) utilized predict severity, focusing on outcomes. Forest, outperformed recall, especially injuries, although predictions imbalance observed. Key identified through included providing interpretable factors influencing severity. integration enhanced transparency predictions, enabling stakeholders prioritize targeted interventions effectively. potential combining improve practices provides foundation addressing data future studies. Graphical

Language: Английский

MultiRS flood mapper: a google earth engine application for water extent mapping with multimodal remote sensing and quantile-based postprocessing DOI
Zhouyayan Li, İbrahim Demir

Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: 176, P. 106022 - 106022

Published: March 14, 2024

Language: Английский

Citations

4
A Contemporary Systematic Review of Cyberinfrastructure Systems and Applications for Flood and Drought Data Analytics and Communication DOI Creative Commons
Serhan Yeşilköy, Özlem Baydaroğlu, Nikhil Kumar Singh

et al.

Environmental Research Communications, Journal Year: 2024, Volume and Issue: 6(10), P. 102003 - 102003

Published: Oct. 1, 2024

Abstract Hydrometeorological disasters, including floods and droughts, have intensified in both frequency severity recent years. This trend underscores the critical role of timely monitoring, accurate forecasting, effective warning systems facilitating proactive responses. Today’s information offer a vast intricate mesh data, encompassing satellite imagery, meteorological metrics, predictive modeling. Easily accessible to general public, these cyberinfrastructures simulate potential disaster scenarios, serving as invaluable aids decision-making processes. review collates key literature on water-related systems, underscoring transformative impact emerging Internet technologies. These advancements promise enhanced flood drought timeliness greater preparedness through improved management, analysis, visualization, data sharing. Moreover, aid hydrometeorological predictions, foster development web-based educational platforms, support frameworks, digital twins, metaverse applications contexts. They further bolster scientific research development, enrich climate change vulnerability strengthen associated cyberinfrastructures. article delves into prospective developments realm natural pinpointing primary challenges gaps current highlighting intersections with future artificial intelligence solutions.

Language: Английский

Citations

4
Comprehensive analysis of riverine flood impact on bridge and transportation network: Iowa case study DOI

E.B. Duran,

Yazeed Alabbad, Jerry Mount

et al.

International Journal of River Basin Management, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 14

Published: Jan. 29, 2025

Language: Английский

Citations

0
Distributed hydrodynamic modelling for assessing flood impacts on crops: Assessing flood-resilient crop management in a coastal basin of central Italy DOI Creative Commons

Thomas Lucaora,

Antonio Annis, Fernando Nardi

et al.

Agricultural Water Management, Journal Year: 2025, Volume and Issue: 309, P. 109352 - 109352

Published: Jan. 31, 2025

Language: Английский

Citations

0
Integrated Explainable Ensemble Machine Learning Prediction of Injury Severity in Agricultural Accidents DOI Creative Commons
Omer Mermer,

Eddie Zhang,

İbrahim Demir

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 7, 2025

Abstract Agricultural injuries remain a significant occupational hazard, causing substantial human and economic losses worldwide. This study investigates the prediction of agricultural injury severity using both linear ensemble machine learning (ML) models applies explainable AI (XAI) techniques to understand contribution input features. Data from AgInjuryNews (2015–2024) was preprocessed extract relevant attributes such as location, time, age, safety measures. The dataset comprised 2,421 incidents categorized fatal or non-fatal. Various ML models, including Naïve Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GB), were trained evaluated standard performance metrics. Ensemble demonstrated superior accuracy recall compared with XGBoost achieving 100% for injuries. However, all faced challenges in predicting non-fatal due class imbalance. SHAP analysis provided insights into feature importance, gender, time emerging most influential predictors across models. research highlights effectiveness while emphasizing need balanced datasets XAI actionable insights. findings have practical implications enhancing guiding policy interventions. Highlights analyzed (2015– 2024) utilized predict severity, focusing on outcomes. Forest, outperformed recall, especially injuries, although predictions imbalance observed. Key identified through included providing interpretable factors influencing severity. integration enhanced transparency predictions, enabling stakeholders prioritize targeted interventions effectively. potential combining improve practices provides foundation addressing data future studies. Graphical

Language: Английский

Citations

0