DEVELOPMENT OF LAND-USE REGRESSION MODELS FOR PARTICULATE MATTER ESTIMATION IN NATIONAL CAPITAL REGION, PHILIPPINES
Abstract. Regression models are commonly used to estimate unknown variables, such as environmental parameters. Multiple Linear Regression (MLR) is one of the techniques used to model air quality and measure air pollutant concentrations. Specifically, a technique called Land-Use Regression (LUR) enables the user to generate air pollutant models using geographical layers as input parameters. The study aims to generate models for fine and coarse particulate matter (PM2.5 and PM10, respectively) using LUR for the National Capital Region in 2019. Independent variables considered in this study are road network, traffic count, Normalized Difference Vegetation Index (NDVI), population density, and elevation. The final model results showed significant estimates based on the model parameters. For PM2.5, the model resulted in high values of R2 and adjusted R2 and an RMSE of 0.77 μg/m3. For PM10, model parameters showed that the generated final model for PM10 was significant with a 55% R2 value. Maps were then generated using the final LUR models of PM2.5 and PM10. The models can be improved by adding more types of input variables and longer observation periods.
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Erschienen in
-
DEVELOPMENT OF LAND-USE REGRESSION MODELS FOR PARTICULATE MATTER ESTIMATION IN NATIONAL CAPITAL REGION, PHILIPPINES ; volume:XLVIII-4/W8-2023 ; year:2024 ; pages:437-444 ; extent:8
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; XLVIII-4/W8-2023 (2024), 437-444 (gesamt 8)
- Klassifikation
-
Soziale Probleme, Sozialdienste, Versicherungen
- Urheber
-
Torres, R. A. B.
Ramos, R. V.
Recto, B. A. B.
Tamondong, A. M.
- DOI
-
10.5194/isprs-archives-XLVIII-4-W8-2023-437-2024
- URN
-
urn:nbn:de:101:1-2405020439390.357007841381
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:48 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Torres, R. A. B.
- Ramos, R. V.
- Recto, B. A. B.
- Tamondong, A. M.
Ähnliche Objekte (12)
Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany
ESTIMATING AIRBORNE PARTICULATE MATTER IN THE NATIONAL CAPITAL REGION, PHILIPPINES USING MULTIPLE LINEAR REGRESSION AND GRADIENT BOOSTING ALGORITHM ON MODIS MAIAC AEROSOL OPTICAL DEPTH
Predicting traffic noise using land-use regression—a scalable approach
Human capital, marriage and regression
Donor-assisted land-use planning in the Philippines: insights from a multi-level survey
Donor-Assisted Land-use Planning in the Philippines: Insights from a Multi-Level Survey
Donor-Assisted Land-use Planning in the Philippines: Insights from a Multi-Level Survey
Donor-Assisted Land-use Planning in the Philippines: Insights from a Multi-Level Survey
Hybrid Neural Networks and Boosted Regression Tree Models for Predicting Roadside Particulate Matter
Managing Surges in Capital Inflows: The Philippines Case
Managing capital flows: The case of the Philippines
Managing Capital Flows: The Case of the Philippines
Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany
ESTIMATING AIRBORNE PARTICULATE MATTER IN THE NATIONAL CAPITAL REGION, PHILIPPINES USING MULTIPLE LINEAR REGRESSION AND GRADIENT BOOSTING ALGORITHM ON MODIS MAIAC AEROSOL OPTICAL DEPTH
Predicting traffic noise using land-use regression—a scalable approach
Human capital, marriage and regression
Donor-assisted land-use planning in the Philippines: insights from a multi-level survey
Donor-Assisted Land-use Planning in the Philippines: Insights from a Multi-Level Survey
Donor-Assisted Land-use Planning in the Philippines: Insights from a Multi-Level Survey
Donor-Assisted Land-use Planning in the Philippines: Insights from a Multi-Level Survey
Hybrid Neural Networks and Boosted Regression Tree Models for Predicting Roadside Particulate Matter
Managing Surges in Capital Inflows: The Philippines Case
Managing capital flows: The case of the Philippines
Managing Capital Flows: The Case of the Philippines
Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany
ESTIMATING AIRBORNE PARTICULATE MATTER IN THE NATIONAL CAPITAL REGION, PHILIPPINES USING MULTIPLE LINEAR REGRESSION AND GRADIENT BOOSTING ALGORITHM ON MODIS MAIAC AEROSOL OPTICAL DEPTH
Predicting traffic noise using land-use regression—a scalable approach
Human capital, marriage and regression
Donor-assisted land-use planning in the Philippines: insights from a multi-level survey
Donor-Assisted Land-use Planning in the Philippines: Insights from a Multi-Level Survey
Donor-Assisted Land-use Planning in the Philippines: Insights from a Multi-Level Survey
Donor-Assisted Land-use Planning in the Philippines: Insights from a Multi-Level Survey
Hybrid Neural Networks and Boosted Regression Tree Models for Predicting Roadside Particulate Matter
Managing Surges in Capital Inflows: The Philippines Case
Managing capital flows: The case of the Philippines