A comparison between natural and Head/tail breaks in LSI (Landslide Susceptibility Index) classification for landslide susceptibility mapping: A case study in Ponorogo, East Java, Indonesia

Basofi, Arif; Fariza, Arna; Ahsan, Ahmad; Kamal, Imam Mustafa

doi:10.1109/icsitech.2015.7407828

Cited by 14 publications

(7 citation statements)

References 5 publications

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“…Furthermore, the raw risk values were classified into five grades using the natural breaks method (Basofi et al., 2015). Figure 9 and Figures S6 and S7 in Supporting Information S1 display the proportions of grids with drought risk at five levels for each region in the subperiods under the four scenarios.…”

Section: Resultsmentioning

confidence: 99%

Projecting Global Drought Risk Under Various SSP‐RCP Scenarios

et al. 2023

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Drought risk assessment can identify high‐risk areas and bridge the gap between impacts and adaptation. It is thus vital to investigate changes in drought risk and exposed social economy under climate change. Here, the future global meteorological drought risk is projected for the 2021–2100 period under four combined scenarios of Representative Concentration Pathways and Shared Socioeconomic Pathways (SSPs), i.e., SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5. And then the exposed population and Gross Domestic Product (GDP) under high risk are analyzed. Drought risk will further strengthen in the future under SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5, with large increases distributed in South Asia, the Mediterranean, East Asia, Southeast Asia, and Central America. The strongest increasing trends in drought risk are concentrated in equatorial regions including South Asia, West Africa, and Central America. A large percentage of the population and economy are exposed to high drought risk. The exposed population under the highest risk level is the largest under SSP5‐8.5 and the smallest under SSP3‐7.0 for regions in North America, while being the largest under SSP3‐7.0 for other regions. The exposed GDP under the highest risk level is the highest under SSP5‐8.5 and the lowest under SSP3‐7.0, while the disparity among diverse scenarios is larger for the rich regions than the developing ones.

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Section: Resultsmentioning

confidence: 99%

Projecting Global Drought Risk Under Various SSP‐RCP Scenarios

et al. 2023

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“…A similar case is the distribution of susceptibility values, to which it seems to adapt satisfactorily. However, in a study of susceptibility comparing NB with HT, Basofi et al (2015) found that the low susceptibility values includes too many data with the HT classification, and is less significant than NB. The present study therefore attempts to provide a better adaptation of the HT method for the five classes described above.…”

Section: Head/tail Breaks (Ht)mentioning

confidence: 99%

A ROC analysis-based classification method for landslide susceptibility maps

et al. 2018

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A landslide susceptibility map is a crucial tool for land-use spatial planning and management in mountainous areas. An essential issue in such maps is the determination of risk thresholds. To this end, the map is zoned into a limited number of classes. Adopting one classification system or another will not only affect the map's readability and final appearance, but most importantly, it may affect the decision-making tasks required for effective land management. The present study compares and evaluates the reliability of some of the most commonly used classification methods, applied to a susceptibility map produced for the area of La Marina (Alicante, Spain). A new classification method based on ROC analysis is proposed, which extracts all the useful information from the initial dataset (terrain characteristics and landslide inventory) and includes, for the first time, the concept of misclassification costs. This process yields a more objective differentiation of risk levels that relies less on the intrinsic structure of the terrain characteristics. The results reveal a considerable difference between the classification methods used to define the most hazardous zones (in over 20% of the surface) and highlight the need to establish a standard method for producing classified susceptibility maps. The method proposed in the study is particularly notable for its consistency, stability and homogeneity, and may mark the starting point for consensus on a generalisable classification method.

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“…Sumber pengetahuan tentang faktor-faktor penyebab longsor dapat bersifat subjektif atau dapat dilihat dari pendekatan objektif [12]. Penggunaan metode AHP untuk menilai kerentanan longsor berdasarkan beberapa faktor juga di kembangkan untuk penilaian area longsor di Ponorogo [13], Bantul [14], dan Pangandaran [15]. AHP telah berhasil menghasilkan nilai rangking dari setiap area berdasarkan faktor-faktor penyebab longsor.…”

Section: Gambar 1 Tren Kejadian Bencana DI Kabupaten Pacitan 2010-2020unclassified

“…Namun untuk memberikan informasi tingkat kerentanan, beberapa penelitian menggunakan klasifikasi pembobotan [9][12][14] [15] yang mempunyai kelemahan dalam menentukan batasbatas yang tepat. Metode klasifikasi natural break merupakan pendekatan untuk membagi nilai rangking hasil perhitungan AHP berdasarkan nilai varian, hal ini juga digunakan untuk mengelompokkan tingkat kerentanan longsor dengan lebih baik [13].…”

Section: Gambar 1 Tren Kejadian Bencana DI Kabupaten Pacitan 2010-2020unclassified

“…Metode klasifikasi natural break menghasilkan klasifikasi dengan urutan nilai terbaik ke dalam beberapa kelas yang berbeda [16]. Metode klasifikasi ini terbukti memiliki adaptasi yang baik dan akurasi yang tinggi pada pembagian unit lingkungan geografis [13] [16]. Klasifikasi natural break merupakan sebuah metode pengklasifikasian, pada penelitian ini dilakukan untuk mengetahui status tingkat kerawanan longsor pada suatu wilayah berdasarkan nilai prioritas global hasil perhitungan AHP.…”

Section: B Klasifikasi Jenks Natural-breakunclassified

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Spatial Mapping of Landslide Susceptibility Level in Pacitan District Using Analytical Hierarchy Process and Natural Break

2022

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Pacitan district has a high potential for landslides. Landslide is a hydrometeorological disaster that causes loss of life, property loss, and environmental damage. Disaster preparedness is very necessary for the wider community in dealing with landslide emergency response situations. Applications to determine the level of vulnerability to landslides are very useful to minimize the impact and losses on the Pacitan community. This study aims to make an application for assessing the level of landslide susceptibility using the analytical hierarchy process and natural break based on the factors that cause landslides in the sub-district or village of Pacitan district. The factors that cause landslides in Pacitan district consist of weather, history of landslides, land slope, and history of earthquakes. The results of the AHP and natural break classifications are visualized in the form of a spatial map into 3 categories, namely high, medium and low vulnerability levels. The results of the AHP classification and natural break in the 2016-2020 data have a good average GVF value of 0.77. This shows that in general, the results of the 2016-2020 data classification are correct. Mobile device-based applications provide convenience for the public in accessing information as an effort to improve landslide disaster preparedness.

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A comparison between natural and Head/tail breaks in LSI (Landslide Susceptibility Index) classification for landslide susceptibility mapping: A case study in Ponorogo, East Java, Indonesia

Cited by 14 publications

References 5 publications

Projecting Global Drought Risk Under Various SSP‐RCP Scenarios

Projecting Global Drought Risk Under Various SSP‐RCP Scenarios

A ROC analysis-based classification method for landslide susceptibility maps

Spatial Mapping of Landslide Susceptibility Level in Pacitan District Using Analytical Hierarchy Process and Natural Break

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