Uporaba strojnega učenja za določitev poplavljenih območij – primer poplav v Selški dolini leta 2007

Application of data mining for determination of flooded areas – Selška valley 2007 floods case study

Author(s):

Peter Lamovec, Krištof Oštir

Abstract:

This paper discusses the usefulness of machine-learning procedures for determining flooded areas in the upper part of the Selška valley. The area was affected by torrential rains on 18.9.2007. Rapid identification of flooded areas is essential for effective implementation of rescue operations and damage assessments. In this case, satellite images are very useful because they enable quick identification of flooded areas even in very large areas. To determine the flooded areas, machine learning techniques were applied to different input data. SPOT satellite image (multispectral and panchromatic), NDVI index, relief and its derivatives (altitude, slope, curvature), distance from rivers and land use were used. The learning samples consisted of 400, 255 and 49 sample points, which were used to build three different classification models.

Keywords:

machine learning, decision trees, classification, satellite images, floods

DOI: 10.15292/geodetski-vestnik.2010.04.661-675

Citation:

Peter Lamovec, Krištof Oštir (2010). Uporaba strojnega učenja za določitev poplavljenih območij – primer poplav v Selški dolini leta 2007. | Application of data mining for determination of flooded areas – Selška valley 2007 floods case study. Geodetski vestnik, 54 (4), 661-675. DOI: 10.15292/geodetski-vestnik.2010.04.661-675