Prediction of flood areas using the logistic regression method (case study of the provinces Banten, DKI Jakarta, and West Java)

Disasters that occur in Indonesia continue to increase from year to year. The National Disaster Management Agency (BNPB) noted that throughout 2017 there had been 2,862 disasters. Of these, almost 99 percent are hydrometeorological disasters, namely disasters that are affected by weather and surface...

Full description

Saved in:
Bibliographic Details
Published in:Journal of physics. Conference series 2019-11, Vol.1367 (1), p.12087
Main Authors: Hidayat Jati, Muhammad Ikbal, Suroso, Santoso, Purwanto Bekti
Format: Article
Language:English
Subjects:
Disaster management
Flood
Flood Parameters
Flood predictions
Flooding
Floods
Geographic information systems
GIS
Hydrometeorology
Land use
Landslides
Logistic Regression
Physics
Provinces
Rainfall
Regression analysis
Regression models
Tidal waves
Tornadoes
Volcanic eruptions
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Disasters that occur in Indonesia continue to increase from year to year. The National Disaster Management Agency (BNPB) noted that throughout 2017 there had been 2,862 disasters. Of these, almost 99 percent are hydrometeorological disasters, namely disasters that are affected by weather and surface flow. The details of the disaster include floods (979), tornadoes (886), landslides (848), forest and land fires (96), drought (19), earthquakes (20), tidal waves and abrasions (11), dam volcanic eruptions (3). The western part of Java Island includes three provinces namely West Java Province, DKI Jakarta Province, and Banten Province. Of the three provinces, they are no stranger to hearing floods, especially in the capital city of Jakarta and West Java. Flood problems until now have not been resolved completely even the problem of floods is the tendency to increase both in terms of intensity, frequency, and distribution due to climate change. Based on the above conditions, it is necessary to do a study that can provide information about the main causal factors and predict areas that are likely to experience floods. In achieving these objectives, this study uses a mathematical model of logistic regression analysis and application of Geographic Information Systems (GIS) conducted based on the variables that cause flooding, namely rainfall, topography, slope, flow accumulation, land use, and distance to the nearest river. The modelling results obtained an accuracy rate for predicting flood disasters in the study area using logistic regression which was between 85.05% - 94.39%.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1367/1/012087