Опубликована: Янв. 1, 2024
Язык: Английский
Water, Год журнала: 2025, Номер 17(5), С. 676 - 676
Опубликована: Фев. 26, 2025
Harmful algal blooms (HABs) have emerged as a significant environmental challenge, impacting aquatic ecosystems, drinking water supply systems, and human health due to the combined effects of activities climate change. This study investigates performance deep learning models, particularly Transformer model, there are limited studies exploring its effectiveness in HAB prediction. The chlorophyll-a (Chl-a) concentration, commonly used indicator phytoplankton biomass proxy for occurrences, is target variable. We consider multiple influencing parameters—including physical, chemical, biological quality monitoring data from stations located west Lake Erie—and employ SHapley Additive exPlanations (SHAP) values an explainable artificial intelligence (XAI) tool identify key input features affecting HABs. Our findings highlight superiority especially Transformer, capturing complex dynamics parameters providing actionable insights ecological management. SHAP analysis identifies Particulate Organic Carbon, Nitrogen, total phosphorus critical factors predictions. contributes development advanced predictive models HABs, aiding early detection proactive management strategies.
Язык: Английский
Процитировано
2
Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106172 - 106172
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
1
Environmental Research Communications, Год журнала: 2024, Номер 6(7), С. 075027 - 075027
Опубликована: Июль 1, 2024
Abstract With the worldwide growing threat of flooding, assessing flood risks for human societies and associated social vulnerability has become a necessary but challenging task. Earlier research indicates that islands usually face heightened due to higher population density, isolation, oceanic activities, while there is an existing lack experience in island-focused risk under complex interactions between geography socioeconomics. In this context, our study employs high-resolution hazard data principal component analysis (PCA) method comprehensively assess exposure Prince Edward Island (PEI), Canada, where limited been delivered on assessments. The findings reveal exposed populations are closely related distribution areas, with increasingly severe impact from current future climate conditions, especially island’s north shore. Exposed buildings exhibit concentrated at different levels community centers, change projected significantly worsen building compared population, possibly urban agglomeration effect. most populated cities towns show highest vulnerabilities PEI, results reflect relatively less economic structure islands. Recommendations management coming stage include necessity particular actions, recognizing centers as critical sites responses, incorporating hazards into planning mitigate impacts continuous urbanization ecosystem services prevention.
Язык: Английский
Процитировано
5
Journal of Hydrology, Год журнала: 2024, Номер unknown, С. 132177 - 132177
Опубликована: Окт. 1, 2024
Язык: Английский
Процитировано
4
Italian Economic Journal, Год журнала: 2025, Номер unknown
Опубликована: Янв. 31, 2025
Язык: Английский
Процитировано
0
Remote Sensing, Год журнала: 2025, Номер 17(4), С. 714 - 714
Опубликована: Фев. 19, 2025
Yu’nan County is located in the Pacific Rim geological disaster-prone area. Frequent landslides are an important cause of population, property, and infrastructure losses, which directly threaten sustainable development regional social economy. Based on field survey data, this paper employs coefficient variation method (CV) improved TOPSIS model (Kullback-Leibler-Technique for Order Preference by Similarity to Ideal Solution) assess vulnerability landslide disasters 182 administrative villages County. Also, it conducts a ranking comprehensive analysis their levels. Finally, accuracy evaluation results validated applying losses incurred from per unit area within same year. The indicate significant spatial variability across County, with 68 out exhibiting moderate levels or higher. This suggests high risk widespread damage potential disasters. Among these, Xincheng village has highest score, while Chongtai lowest, 0.979 difference vulnerabilities. By comparing actual landslides, found that predicted CV-KL-TOPSIS more consistent results. Furthermore, among ten sub-factors, population density, building value, road value contribute most significantly overall weight 0.269, 0.152, 0.105, respectively, suggesting mountainous areas where relatively concentrated, hazards reflection characteristics local economic level. framework indicators proposed can systematically accurately evaluate landslide-prone areas, provide reference urban planning management areas.
Язык: Английский
Процитировано
0
Agricultural Water Management, Год журнала: 2025, Номер 312, С. 109423 - 109423
Опубликована: Март 30, 2025
Язык: Английский
Процитировано
0
Advances in computer and electrical engineering book series, Год журнала: 2025, Номер unknown, С. 247 - 272
Опубликована: Янв. 17, 2025
Effective management of spatial data can drive green innovation by identifying environmental challenges such as air and water quality, deforestation, soil health, climate vulnerability. Spatial supports pollution detection forest cover analysis, along with sampling for erosion assessment. It also guide targeted initiatives like clean efforts sustainable forestry. Moreover, it optimize resource allocation pinpointing renewable energy sources materials. tailor innovations to local contexts, inform urban planning, enhance waste agriculture practices, monitor impact. Key strategies involve collecting high-quality from diverse sources, integrating into accessible platforms, ensuring quality. Collaboration knowledge sharing data's role in innovation. Challenges access, ownership, privacy concerns necessitate solutions open policies, clear agreements, capacity-building programs.
Язык: Английский
Процитировано
0
Applied Sciences, Год журнала: 2025, Номер 15(9), С. 4824 - 4824
Опубликована: Апрель 26, 2025
Harmful Algal Blooms (HABs), predominantly driven by cyanobacteria, pose significant risks to water quality, public health, and aquatic ecosystems. Lake Erie, particularly its western basin, has been severely impacted HABs, largely due nutrient pollution climatic changes. This study aims identify key physical, chemical, biological drivers influencing HABs using a multivariate regression analysis. Water quality data, collected from multiple monitoring stations in Erie 2013 2020, were analyzed develop predictive models for chlorophyll-a (Chl-a) total suspended solids (TSS). The correlation analysis revealed that particulate organic nitrogen, turbidity, carbon the most influential variables predicting Chl-a TSS concentrations. Two developed, achieving high accuracy with R2 values of 0.973 0.958 TSS. demonstrates robustness techniques identifying HAB drivers, providing framework applicable other systems. These findings will contribute better prediction management strategies, ultimately helping protect resources health.
Язык: Английский
Процитировано
0
International Journal of Disaster Risk Reduction, Год журнала: 2025, Номер unknown, С. 105594 - 105594
Опубликована: Май 1, 2025
Язык: Английский
Процитировано
0