Microplastic dynamics and risk projections in West African coastal areas: Developing a vulnerability index, adverse ecological pathways, and mitigation framework using remote-sensed oceanographic profiles DOI
Azubuike V. Chukwuka,

Ayotunde Daniel Adegboyegun,

Femi V. Oluwale

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 953, P. 175963 - 175963

Published: Sept. 1, 2024

Language: Английский

Spatial Data Management Strategies to Improve Green Innovation DOI
Jan Vrba, Muhammad Akbar,

Emmanuel Emmanuel Eze

et al.

Advances in computer and electrical engineering book series, Journal Year: 2025, Volume and Issue: unknown, P. 247 - 272

Published: Jan. 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.

Language: Английский

Citations

0

Municipal Solid Waste Management Using Machine Learning: A Case Study in Sheger City, Koye Sub-city, Ethiopia DOI

Tsegaye Hordofa Gudeta,

Gudeta Tesema Mamo,

Yezeshawal Mengistu Neguse

et al.

European Journal of Theoretical and Applied Sciences, Journal Year: 2025, Volume and Issue: 3(2), P. 511 - 525

Published: April 11, 2025

Municipal Solid Waste Management is an increasingly critical challenge in urban areas, intensified by rapid urbanization, population growth, and evolving consumption patterns. This study investigates the application of machine learning techniques to predict municipal solid waste generation Sheger City, Koye Sub-city, Ethiopia, using data from 2009 2023. Three models, ARIMA, RF, LSTM, were employed forecast trends for period 2024–2028, considering various socio-economic demographic factors. Among LSTM demonstrated highest accuracy, with MSE 1.62 × 10⁸ tonnes, MAE 9,500 R² 0.93. These results outperformed ARIMA (MSE = 3.84 tonnes², 15,200 0.85) RF 2.91 12,800 0.89). The forecasts 8.5% increase total generation, 3,852,150 tonnes 2023 4,177,500 2028. Notable growth expected high-volume streams, including food (13.5% increase) plastic (8.9% increase). findings highlight urgent need enhanced management strategies, expanded recycling programs policy interventions. provides a robust framework leveraging models guide decisions, contributing more sustainable practices rapidly growing cities.

Language: Английский

Citations

0

Microplastic dynamics and risk projections in West African coastal areas: Developing a vulnerability index, adverse ecological pathways, and mitigation framework using remote-sensed oceanographic profiles DOI
Azubuike V. Chukwuka,

Ayotunde Daniel Adegboyegun,

Femi V. Oluwale

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 953, P. 175963 - 175963

Published: Sept. 1, 2024

Language: Английский

Citations

2