TY  - CONF
VL  - 48
EP  - 369
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148704453&doi=10.5194%2fisprs-archives-XLVIII-4-W6-2022-363-2023&partnerID=40&md5=3dac90dea25dbfb4bbb34fb4761891fd
A1  - Tella, A.
A1  - Ul Mustafa, M.R.
A1  - Balogun, A.O.
A1  - Okolie, C.J.
A1  - Yamusa, I.B.
A1  - Ibrahim, M.B.
SN  - 16821750
PB  - International Society for Photogrammetry and Remote Sensing
Y1  - 2023///
KW  - Disasters; Forecasting; Hazards; Rivers; Urban planning
KW  -  Flood hazards; Flood susceptibility; Floodings; Logistics regressions; Malaysia; Natural disasters; Number of peoples; Socio-economics; Spatial prediction; Urban flooding
KW  -  Floods
ID  - scholars18816
SP  - 363
TI  - SPATIAL PREDICTION OF FLOOD IN KUALA LUMPUR CITY OF MALAYSIA USING LOGISTIC REGRESSION
N1  - cited By 1; Conference of 2022 Geoinformation Week: Broadening Geospatial Science and Technology ; Conference Date: 14 November 2022 Through 17 November 2022; Conference Code:186733
N2  - Flooding is one of the most prevalent natural disasters affecting people worldwide. Flooding is a devastating natural disaster in Malaysia regarding the number of people affected, socioeconomic damage, severity, and scale of the impact. Urban flooding is currently a major concern due to the possible consequences and frequency with which it occurs in urban areas as urbanization and population increase. Due to the paved surfaces, paved roads, high population, and buildings that prevent water infiltration and movement to the nearby river, urban floods pose a significant threat to the sustainability of lives and properties in the city. The recent floods in Kuala Lumpur in December 2021 and January 2022 affected many buildings, infrastructure, and lives. As a result, this city needs to model the susceptibility of flood-prone areas for an early warning system against future flood hazards in Kuala Lumpur. This is because flooding can never be eradicated but can be minimized and managed. Therefore, this study integrates geospatial technology and a statistical model (logistic regression) to assess flood hazards in Kuala Lumpur. Ten flood conditioning factors such as altitude, slope, TWI, drainage density, distance to river, LULC, NDVI, NDWI, rainfall and MNDWI were used to predict the areas susceptible to flood. The prediction shows an overall accuracy of 0.84, precision of 0.91, recall of 0.72, and F1-score of 0.80. Distance to river, MNDWI, TWI, and LULC are the critical variables that showed high significance in the model prediction. Thus, stakeholders should prioritize urban planning and increase the drainage system to avoid flood effects. Copyright © 2023 A. Tella et al.
AV  - none
ER  -