Comparison of multi-criteria-analytical hierarchy process and machine learning-boosted tree models for regional flood susceptibility mapping: a case study from Slovakia

Vojtek, Matej; Vojteková, Jana; Costache, Romulus; Pham, Quoc Bao; Lee, Sunmin; Arshad, Arfan; Sahoo, Satiprasad; Linh, Nguyễn Thị Thùy; Anh, Duong Tran

doi:10.1080/19475705.2021.1912835

Cited by 64 publications

(18 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Based on the recent approach by Costacheetal. 59 , 60 and Vojtek et al 61 , we developed a new advanced GSLDI and the SRs grazing pressure using ML algorithms. The proposed modeling framework consists of: (1) land erosion inventory (manually identifying erosion and no-erosion sampling points based on aerial imagery); (2) developing geographical characteristics (17 parameters, e.g., lithological structure, altitude) for each sampling point, (3) standardizing the dataset (55 subsequent attributes e.g., classes/features), and (4) ML model training process.…”

Section: Methods and Datamentioning

confidence: 99%

“…Currently, management of natural environmental hazards (e.g., floods, landslides, gully can be assessed, mapped and predicted using machine learning (ML, a branch of artificial intelligence, algorithms and tools. More devoted literature using ML combined with diverse statistical methods (e.g., random forest—RF; boosted regression tree—BRT can be found in valuable scientific works focused on vulnerable territories (e.g., Himalayan regions, for example by Roy et al 55 , Chowdhuri et al 56 , 57 Pal et al 58 , Costache et al 59 , 60 and Vojtek et al 61 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Designing grazing susceptibility to land degradation index (GSLDI) in hilly areas

Minea

Ciobotaru

Ioana-Toroimac

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Evaluation of grazing impacts on land degradation processes is a difficult task due to the heterogeneity and complex interacting factors involved. In this paper, we designed a new methodology based on a predictive index of grazing susceptibility to land degradation index (GSLDI) built on artificial intelligence to assess land degradation susceptibility in areas affected by small ruminants (SRs) of sheep and goats grazing. The data for model training, validation, and testing consisted of sampling points (erosion and no-erosion) taken from aerial imagery. Seventeen environmental factors (e.g., derivatives of the digital elevation model, small ruminants’ stock), and 55 subsequent attributes (e.g., classes/features) were assigned to each sampling point. The impact of SRs stock density on the land degradation process has been evaluated and estimated with two extreme SRs’ density scenarios: absence (no stock), and double density (overstocking). We applied the GSLDI methodology to the Curvature Subcarpathians, a region that experiences the highest erosion rates in Romania, and found that SRs grazing is not the major contributor to land degradation, accounting for only 4.6%. This methodology could be replicated in other steep slope grazing areas as a tool to assess and predict susceptible to land degradation, and to establish common strategies for sustainable land-use practices.

show abstract

Section: Methods and Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Designing grazing susceptibility to land degradation index (GSLDI) in hilly areas

Minea

Ciobotaru

Ioana-Toroimac

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…MCDA merupakan suatu alat pendekatan atau metode dalam pengambilan keputusan berdasarkan alternatif/ opsi solusi dari multi kriteria yang ada (Jaya et al, 2020). Analytical Hierarchy Process (AHP) dapat didefinisikan sebagai metode untuk menguraikan permasalahan kompleks terkait banyaknya kriteria yang tidak terstruktur menjadi bagian-bagian yang lebih sederhana (sistem hierarkis) dengan hasil evaluasi yang didapatkan adalah faktor prioritas/ bobot (Vojtek et al, 2021)…”

Section: Pendahuluanunclassified

IDENTIFIKASI KERENTANAN BANJIR BANDANG MENGGUNAKAN METODE AHP (ANALYTICAL HIERARCHY PROCESS) BERBASIS SIG DI SUB DAS AIR MULAK, KABUPATEN LAHAT (Identification of Flash Flood Vulnerability Using GIS-Based AHP (Analytical Hierarchy Process) Method in Air Mulak Sub-watershed, Lahat Regency)

Dhiniati¹,

Dinata²

2022

JPPDAS

View full text Add to dashboard Cite

The high rainfall for a long time resulted in flash floods that hit three villages in Lahat Regency, submerged dozens of residents' houses, damaged several public facilities, and cut off-road access. From this incident, spatial information is needed related to areas prone to flash floods so that the negative impacts can be minimized. This study aims to identify the vulnerability of flash floods in the Air Mulak Watershed. This research uses the AHP (Analytical Hierarchy Process) method for rational decision-making of all parameters, integrated with GIS (Geographical Information System) applications. The results of the consistency test for all parameters, the CR value of 0.97% is smaller than 10% and is considered consistent. The results of scoring all parameters are then overlaid by giving weight to each parameter, and it can be seen that the distribution of flash flood susceptibility in Air Mulak Sub-Watershed with a very high percentage of 0.20%, high class of 4.75%, moderate class of 15.78%, low at 43.50%, and very low at 35.78%. The results showed that the parameters that greatly affect flash flooding in the Air Mulak Sub-Watershed are altitude, river density, slope, and distance from the river because the area is where water accumulates from upstream of the river when it rains with high intensity.

show abstract

“…The weighting mechanism employed the SAW method, giving equal weights to each analysis result. Mechanism development could be used in the Analytic Hierarchy Process (AHP) method based on expert-based assessments or weights derived through a community-based method that has been empirically tested [63,64].…”

Section: Limitations and Future Possible Directionmentioning

confidence: 99%

School Location Analysis by Integrating the Accessibility, Natural and Biological Hazards to Support Equal Access to Education

Sakti

Rahadianto

Pradhan

et al. 2021

IJGI

View full text Add to dashboard Cite

This study proposes a new model for land suitability for educational facilities based on spatial product development to determine the optimal locations for achieving education targets in West Java, Indonesia. Single-aspect approaches, such as accessibility and spatial hazard analyses, have not been widely applied in suitability assessments on the location of educational facilities. Model development was performed based on analyses of the economic value of the land and on the integration of various parameters across three main aspects: accessibility, comfort, and a multi-natural/biohazard (disaster) risk index. Based on the maps of disaster hazards, higher flood-prone areas are found to be in gentle slopes and located in large cities. Higher risks of landslides are spread throughout the study area, while higher levels of earthquake risk are predominantly in the south, close to the active faults and megathrusts present. Presently, many schools are located in very high vulnerability zones (2057 elementary, 572 junior high, 157 senior high, and 313 vocational high schools). The comfort-level map revealed 13,459 schools located in areas with very low and low comfort levels, whereas only 2377 schools are in locations of high or very high comfort levels. Based on the school accessibility map, higher levels are located in the larger cities of West Java, whereas schools with lower accessibility are documented far from these urban areas. In particular, senior high school accessibility is predominant in areas of lower accessibility levels, as there are comparatively fewer facilities available in West Java. Overall, higher levels of suitability are spread throughout West Java. These distribution results revealed an expansion of the availability of schools by area: senior high schools, 303,973.1 ha; vocational high schools, 94,170.51 ha; and junior high schools, 12,981.78 ha. Changes in elementary schools (3936.69 ha) were insignificant, as the current number of elementary schools is relatively much higher. This study represents the first to attempt to integrate these four parameters—accessibility, multi natural hazard, biohazard, comfort index, and land value—to determine potential areas for new schools to achieve educational equity targets.

show abstract

Comparison of multi-criteria-analytical hierarchy process and machine learning-boosted tree models for regional flood susceptibility mapping: a case study from Slovakia

Cited by 64 publications

References 51 publications

Designing grazing susceptibility to land degradation index (GSLDI) in hilly areas

Designing grazing susceptibility to land degradation index (GSLDI) in hilly areas

IDENTIFIKASI KERENTANAN BANJIR BANDANG MENGGUNAKAN METODE AHP (ANALYTICAL HIERARCHY PROCESS) BERBASIS SIG DI SUB DAS AIR MULAK, KABUPATEN LAHAT (Identification of Flash Flood Vulnerability Using GIS-Based AHP (Analytical Hierarchy Process) Method in Air Mulak Sub-watershed, Lahat Regency)

School Location Analysis by Integrating the Accessibility, Natural and Biological Hazards to Support Equal Access to Education

Contact Info

Product

Resources

About