LOW DATA REQUIREMENTS FRAMEWORK for LANDSLIDE SUSCEPTIBILITY PREDICTION USING 3D ALOS PALSAR IMAGES and NEURAL NETWORKS

Küçük Resim Yok

Tarih

2019

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

International Society for Photogrammetry and Remote Sensing

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

Development of landslides susceptibility (LS) predictors based on 3D data is an active area of research in the recent years. Predicting landslides susceptibility maps help to secure human lives and maintaining infrastructures from this risk. Several advanced frameworks proposed with high input data to improve the predictors. The aim of this paper is to develop low data requirement framework for LS predictors development. This framework is only using one input 3D ALOS PALSAR image. The framework has three stages. (A) data pre-processing, (B) deriving explanatory factors, and (C) neural networks training and testing. Exactly. 22 input spatial factors were extracted from ALOS PALSAR image. Extracted factors were utilized to develop the FFNN predictor. The structure of the predictor is 22 factors (input layer) × 150 neurons (hidden layer) × 1 (output layer). Furthermore, 5829 sample points utilized during the training stage, while 745810 points sent to the trained predictor to create LS map. Based on confusion matrix metric, performance accuracy (89.3% training and 82.3 testing), While (95.22% training and 84.7% testing) based on Receiver Operating Characteristic curve. Out of the study area in Karabuk, 3.53 km2 (3.03%) were located in very high susceptibility category. Lastly, the application of the proposed framework showed that it is capable develop low data requirement predictors with high accuracy. Framework provide guideline data for future development in taxing topographic circumstances and large scale of data coverage. In addition, the framework handled the inconsistency in data quality and data updating problem. © 2019 S. K. M. Abujayyab.

Açıklama

6th International Conference on Geomatics and Geospatial Technology, GGT 2019 -- 1 October 2019 through 3 October 2019 -- Kuala Lumpur -- 158822

Anahtar Kelimeler

ALOS PALSAR, landslide susceptibility, low requirements framework., neural networks, topographic attributes

Kaynak

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

WoS Q Değeri

Scopus Q Değeri

N/A

Cilt

42

Sayı

4/W16

Künye