Table Of ContentSpringer Geography
Wuttichai Boonpook
Zhaohui Lin
Pakorn Meksangsouy
Parichat Wetchayont Editors
Applied
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Proceedings of ICGGS 2022
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Wuttichai Boonpook • Zhaohui Lin
Pakorn Meksangsouy • Parichat Wetchayont
Editors
Applied Geography
and Geoinformatics
for Sustainable Development
Proceedings of ICGGS 2022
Editors
Wuttichai Boonpook Zhaohui Lin
Department of Geography International Center for Climate
Faculty of Social Science and Environment Science (ICCES)
Srinakharinwirot University Institute of Atmospheric Physics
Bangkok, Thailand Chinese Academy of Sciences
Beijing, China
Pakorn Meksangsouy
Department of Geography Parichat Wetchayont
Faculty of Social Science Department of Geography
Srinakharinwirot University Faculty of Social Science
Bangkok, Thailand Srinakharinwirot University
Bangkok, Thailand
ISSN 2194-315X ISSN 2194-3168 (electronic)
Springer Geography
ISBN 978-3-031-16216-9 ISBN 978-3-031-16217-6 (eBook)
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Contents
Flood Susceptibility Mapping Using a Frequency Ratio Model:
A Case Study of Chai Nat Province, Thailand . . . . . . . . . . . . . . . . . . . . . . . 1
Chanita Duangyiwa and Pannee Cheewinsiriwat
Influence of Hydrosphere Material Knowledge on the Attitude
of High School Students in Conducting Water Conservation
in Brebes Regency, Indonesia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Ristiani, Dede Rohmat, and Iwan Setiawan
Optimizing Multi-reservoir Systems with the Aid of Genetic
Algorithm: Mahanadi Reservoir Project Complex, Chhattisgarh . . . . . . . 35
Shashikant Verma, A. D. Prasad, and Mani Kant Verma
Monitoring of Morphological Change in Lam Phachi River
Using Geo- informatics System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Thanat Saprathet, Chudech Losiri, Asamaporn Sitthi,
and Jeerapong Laonamsai
Developing Scenario of Plastic Waste Leakage in the Jakarta
Hydrology Environment Using Seasonal Data Conditions
and Socioeconomic Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
Aprilia Nidia Rinasti, Kavinda Gunasekara, Ekbordin Winijkul,
Sarawut Ninsawat, and Thammarat Koottatep
Measurement of PM , PM , NO, and SO Using Sensors . . . . . . . . . . . . 89
10 2.5 2 2
Vinit Lambey and A. D. Prasad
Encoding Social Media Wording Indexes to Analyze PM Problem
2.5
Perception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
Sureeporn Nipithwittaya
Noise Mapping of Different Zones in an Urban Area During
Deepawali Festival . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
Vishal Kumar, A. V. Ahirwar, and A. D. Prasad
v
vi Contents
Digital Twins in Farming with the Implementation of Agricultural
Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
Aakash Thapa and Teerayut Horanont
A Cross-comparison Between Rice Crop Monitoring Systems:
GISTDA and International Asian Harvest mOnitoring System
for Rice (INAHOR): JAXA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
Kanjana Koedkurang, Patiwet Chalearmpong, Matawee Srisawat,
and Panu Nuangjumnong
Evaluation MODIS and Sentinel-2 Data for Detecting Crop Residue
Burned Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
Chanarun Saisaward and Sarawut Ninsawat
Machine Learning Approach with Environmental Pollution
and Geospatial Information for Mapping Poverty in Thailand . . . . . . . . . 159
Mahmud Isnan, Teerayut Horanont, and Anon Plangprasopchok
Integration of Machine Learning Algorithms and Time-Series
Satellite Images on Land Use/Land Cover Mapping with Google
Earth Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
Guntaga Logavitool, Kritchayan Intarat, and Teerayut Horanont
Sugarcane and Cassava Classification Using Machine Learning
Approach Based on Multi-temporal Remote Sensing Data Analysis . . . . . 183
Jirawat Daraneesrisuk, Sarawut Ninsawat, Chudech Losiri,
and Asamaporn Sitthi
Google Earth Engine Algorithm for Evaluating the Performance
of Landsat OLI-8 and Sentinel-2 in Mangrove Monitoring . . . . . . . . . . . . 195
Asamaporn Sitthi
Estimation of Aboveground Biomass and Carbon Stock Using
Remote Sensing Data in Sakaerat Environmental Research Station,
Thailand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207
Sinlapachat Pungpa, Sirilak Chumkiew, and Pantip Piyatadsananon
Determination of Land Suitability for Oil Palm with Multi-dimension
Decision Support Using Analytic Network Process (ANP) in Southern
Thailand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
Chomchanok Arunplod, Apichon Witayangkurn,
and Daosaowaluk Kongtong
Land Use Change and Ecosystem Service Variations in Huai Luang
River Basin, Udon Thani Province, Thailand . . . . . . . . . . . . . . . . . . . . . . . . 239
Sathaporn Monprapussorn
The Ability to Access Attractions for the Elderly Using Public
Transport in Bangkok Metropolis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247
Sutatip Chavanavesskul and Pakorn Meksangsouy
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259
Flood Susceptibility Mapping Using
a Frequency Ratio Model: A Case Study
of Chai Nat Province, Thailand
Chanita Duangyiwa and Pannee Cheewinsiriwat
Abstract Flooding has become more prevalent in many regions of Southeast Asian
countries in recent decades. Intense precipitation, settlement in low-lying areas,
population growth, and rapid urbanization can enhance vulnerability to floods and
lead to serious hazards. This study developed flood susceptibility mapping for the
Chai Nat province of Thailand using flood-conditioning factors and the frequency
ratio (FR) method. The flood inventory (2005–2017) was randomly separated into a
training dataset for FR analysis and a testing dataset for model validation. Eleven
flood-conditioning parameters, i.e., altitude, slope, curvature, the topographic wet-
ness index, rainfall, distance to drainage, drainage density, soil drainage, land use,
the normalized difference vegetation index, and road density, were considered for
this study. While constructing the flood susceptibility index, the relative frequency
and predictor rate were used to create the flooding probability for each factor class
and the weight of each factor in the model. The values for the flood susceptibility
index were classified into five categories and used to make a flood susceptibility
map. The area under the curve (AUC) was used to validate the model prediction.
The results indicate that the AUC values for the success and prediction rates are
74.2% and 75.1%, respectively.
Keywords Flood susceptibility · Frequency ratio · Predictor rate · GIS · Thailand
C. Duangyiwa (*) · P. Cheewinsiriwat
Center of Excellence in Geography and Geoinformatics, Faculty of Arts, Chulalongkorn
University, Bangkok, Thailand
e-mail: [email protected]
© The Author(s), under exclusive license to Springer Nature 1
Switzerland AG 2023
W. Boonpook et al. (eds.), Applied Geography and Geoinformatics for
Sustainable Development, Springer Geography,
https://doi.org/10.1007/978-3-031-16217-6_1
2 C. Duangyiwa and P. Cheewinsiriwat
1 Introduction
Flooding has become more frequent in recent years due to climate change. Most
Southeast Asian countries, including Myanmar, Indonesia, Malaysia, Vietnam, and
Thailand, have been affected by flooding in recent decades [1]. Intense precipitation
is the most common cause of flooding, but socioeconomic factors, such as settle-
ment in low-lying areas, rapid urbanization, and economic development, have
increased flood risk exposure [2, 3]. Early human settlements were clustered near
rivers and on low-lying terrain. Trends of preferential settlement and urbanization
have resulted in a significant increase in the number of people living in the area.
These expose areas to different types of floods, such as river overflow, surface water
flow, coastal inundation, and flash flooding [4, 5].
Chai Nat is a province in the Chao Phraya River basin, the central part of
Thailand. Most of the region is lowland, bordered by rivers and irrigation canals. In
the past, flooding in Chai Nat has resulted from high precipitation in the upstream
basin. Extreme weather events that involve variations in rainfall patterns and/or
tropical storms could increase overall precipitation and runoff volume. In addition,
the development of social and economic systems may affect the spatial patterns of
flood risk. Population growth could influence changes in types of land use. The
construction of new structures and infrastructure may limit floodplain drainage and
increase its susceptibility to flooding [6]. Hence, the development of flood modeling
to predict vulnerable areas could benefit flood adaptation and mitigation.
The frequency ratio (FR) is a bivariate statistical model that has been used to map
flood susceptibility in several studies, in Malaysia, Korea, China, Iran, Papua New
Guinea, Pakistan, and Thailand [7–15], among others. The FR approach investi-
gated the ratio of the probability of flooding to the probability of nonflooding for
given characteristics to assess the influences of classes of each conditioning factor
on flood occurrence [10]. Various flood-hazard-influencing factors, such as eleva-
tion, slope, aspect, curvature, land use, rainfall, drainage density, soil drainage,
topographic wetness index (TWI), and distance to river, must be specified to develop
FR model [9, 11, 12]. The success and prediction rates in preliminary studies using
the FR model varied between 64–96% and 60–97%, respectively [10–15].
The FR model has been used in a few studies to assess flood risk in Thailand.
Anuchan and Iamchuen [14] employed the FR approach to map flood susceptibility
in southern Thailand’s Songkhla Lake basin. In the study, only six contributing
parameters were used: elevation, slope, drainage density, road density, soil drain-
age, and land use. Seejata et al. [15] used the FR process to create a flood hazard
map in Sukhothai province, taking into account eight parameters: rainfall, elevation,
slope, soil drainage, land use, drainage density, road density, and distance to the
river. The validation results from these studies demonstrated that the FR approach
was successful for flood risk mapping in both areas. In Chai Nat, there is currently
no clear evidence for influencing factors or their relationship to flood occurrences.
Therefore, this study simulates flood susceptibility mapping in Chai Nat using the
Flood Susceptibility Mapping Using a Frequency Ratio Model: A Case Study of Chai… 3
geospatial FR technique. The 11 significant elements, i.e., altitude, slope, curvature,
TWI, rainfall, distance to drainage, drainage density, soil drainage, land use, nor-
malized difference vegetation index (NDVI), and road density, were used in
this model.
2 Study Area
Chai Nat is located in the central region of Thailand, covering an area of 2470 km2
(Fig. 1). Floodplains characterize the topography of this area, with elevations lower
than 296 m above sea level. The study area is situated in the Chao Phraya River
basin, the largest in Thailand, covering approximately 30% of the country’s total
area [16, 17]. The basin includes two major river systems: the Chao Phraya and the
Tha Chin. The Chao Phraya River begins at the confluence of the Ping, Wang, Yom,
and Nan Rivers, all of which originate in northern Thailand. The Tha Chin River is
a tributary of the Chao Phraya River, which branches off the main river in Chai Nat
and flows to the Gulf of Thailand [18].
The average annual precipitation is approximately 1000 mm. The wettest month
is September, and the area has an average of 20 rainy days and 240 mm precipitation
per month. The average temperature is about 27.8 °C. The temperature and precipi-
tation records for the period 1970–2020 indicate that the greatest maximum tem-
perature was 41.8 °C in April 2016 and the highest maximum precipitation was
130.1 mm in April 2000 [19].
Fig. 1 Location of Chai Nat, Thailand
