KNOWLEDGE MANAGEMENT APPROACH IN COMPARATIVE STUDY OF AIR POLLUTION PREDICTION MODEL DOI Creative Commons
Siti Rohajawati,

Hutanti SETYODEWI,

Ferryansyah Muji Agustian TRESNANTO

et al.

Applied Computer Science, Journal Year: 2024, Volume and Issue: 20(1), P. 173 - 188

Published: March 30, 2024

This study utilizes knowledge management (KM) to highlight a documentation-centric approach that is enhanced through artificial intelligence. Knowledge can improve the decision-making process for predicting models involved datasets, such as air pollution. Currently, pollution has become serious global issue, impacting almost every major city worldwide. As capital and central hub various activities, Jakarta experiences heightened levels of activity, resulting in increased vehicular traffic elevated levels. The comparative aims measure accuracy naïve bayes, decision trees, random forest prediction models. Additionally, uses evaluation measurements assess how well machine learning performs, utilizing confusion matrix. dataset’s duration three years, from 2019 until 2021, obtained Open Data. found achieved best results with an rate 94%, followed by tree at 93%, bayes had lowest 81%. Hence, emerges reliable predictive model

Language: Английский

Novel Groundwater Quality Index (GWQI) model: A Reliable Approach for the Assessment of Groundwater DOI Creative Commons
Abdul Majed Sajib, Apoorva Bamal, Mir Talas Mahammad Diganta

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104265 - 104265

Published: Feb. 1, 2025

Language: Английский

Citations

2
Enhancing water quality management through artificial intelligence and machine learning technologies DOI
Aakriti Chauhan, Purnima Mehta, Arun Lal Srivastav

et al.

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 69 - 88

Published: Jan. 1, 2025

Language: Английский

Citations

0
Towards Safer Water: AI-Driven Predictive Analytics for Disease Detection DOI Creative Commons

Jaya Zalte,

Harshal Shah

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Abstract Water quality is a critical factor for human health and environmental sustainability. Rapid urbanization industrialization have led to significant water contamination, increasing the prevalence of waterborne diseases. This study investigates presence pathogens in sources across Gujarat region, utilizing machine learning models analyze contamination patterns. Various classifiers, including HistGradientBoosting, Random Forest, AdaBoost, Bagging, Decision Tree, LSTM, were employed predict identify pathogens. Among these, Forest Bagging classifiers exhibited highest accuracy at 98.53%. Furthermore, Explainable AI techniques, specifically SHapley Additive exPlanations (SHAP), used interpret features influencing levels. The highlights need proactive monitoring pathogen detection prevent disease outbreaks.

Language: Английский

Citations

0
Aquatic System Assessment of Potentially Toxic Elements in El Manzala Lake, Egypt: A Statistical and Machine Learning Approach DOI Creative Commons

Asmaa Nour Aly Al-Falal,

Salah Elsayed,

Ezzat A. El Fadaly

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105027 - 105027

Published: April 1, 2025

Language: Английский

Citations

0
KNOWLEDGE MANAGEMENT APPROACH IN COMPARATIVE STUDY OF AIR POLLUTION PREDICTION MODEL DOI Creative Commons
Siti Rohajawati,

Hutanti SETYODEWI,

Ferryansyah Muji Agustian TRESNANTO

et al.

Applied Computer Science, Journal Year: 2024, Volume and Issue: 20(1), P. 173 - 188

Published: March 30, 2024

This study utilizes knowledge management (KM) to highlight a documentation-centric approach that is enhanced through artificial intelligence. Knowledge can improve the decision-making process for predicting models involved datasets, such as air pollution. Currently, pollution has become serious global issue, impacting almost every major city worldwide. As capital and central hub various activities, Jakarta experiences heightened levels of activity, resulting in increased vehicular traffic elevated levels. The comparative aims measure accuracy naïve bayes, decision trees, random forest prediction models. Additionally, uses evaluation measurements assess how well machine learning performs, utilizing confusion matrix. dataset’s duration three years, from 2019 until 2021, obtained Open Data. found achieved best results with an rate 94%, followed by tree at 93%, bayes had lowest 81%. Hence, emerges reliable predictive model

Language: Английский

Citations

1