COMBINING NEURAL NETWORKS AND GEOSTATISTICS FOR LANDSLIDE HAZARD ASSESSMENT OF SAN SALVADOR METROPOLITAN AREA, EL SALVADOR

Revista de Matemática: Teoría y Aplicaciones

卷:23

Ricardo Ríos¹  Giovanni Molina²  Alexandre Ribó³  Roberto Mejía³ 

[1] Department of Mathematics, Science and Mathematics Faculty, University of El Salvador, El Salvador.;

[2] Ministry of Environment and Natural Resources of El Salvador, El Salvador.;

[3] National Institute of Health, Ministry of Health of El Salvador, El Salvador.;

关键词: landslide;    hazard assessment;    El Salvador;    ANN;    geostatistics;    artificial neural networks;    kriging;   

DOI  :  10.15517/rmta.v23i1.22439

来源: DOAJ

【 摘 要 】

This contribution describes the creation of a landslide hazard assessment model for San Salvador, a department in El Salvador. The analysis started with an aerial photointerpretation from Ministry of Environment and Natural Resources of El Salvador (MARN Spanish acronym), where 4792 landslides were identified and georeferenced along with 7 conditioning factors including: geomorphology, geology, rainfall intensity, peak ground acceleration, slope angle, distance to road, and distance to geological fault. Artificial Neural Networks (ANN) were utilized to assess the susceptibility to landslides, achieving results where more than 80% of landslide were properly classified using in-sample and out of sample criteria. Logistic regression was used as base of comparison. Logistic regression obtained a lower performance. To complete the analysis we have performed interpolation of the points using the kriging method from geostatistical approach. Finally, the results show that is possible to derive a landslide hazard map, making use of a combination of ANNs and geostatistical techniques, thus the present study can help landslide mitigation in El Salvador.

【 授权许可】

Unknown