Mapping landslide susceptibility using data-driven methods

2017 ◽  
Vol 589 ◽  
pp. 250-267 ◽  
Author(s):  
J.L. Zêzere ◽  
S. Pereira ◽  
R. Melo ◽  
S.C. Oliveira ◽  
R.A.C. Garcia
Keyword(s):  
Landslide Susceptibility ◽  
Data Driven ◽  
Using Data

A similarity-based approach to sampling absence data for landslide susceptibility mapping using data-driven methods

CATENA ◽  
2019 ◽  
Vol 183 ◽  
pp. 104188 ◽  
Author(s):  
A-Xing Zhu ◽  
Yamin Miao ◽  
Junzhi Liu ◽  
Shibiao Bai ◽  
Canying Zeng ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Data Driven ◽  
Landslide Susceptibility Mapping ◽  
Using Data

scholarly journals AERIAL PHOTOGRAMMETRY AND MACHINE LEARNING BASED REGIONAL LANDSLIDE SUSCEPTIBILITY ASSESSMENT FOR AN EARTHQUAKE PRONE AREA IN TURKEY

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 713-720
Author(s):  
G. Karakas ◽  
S. Kocaman ◽  
C. Gokceoglu
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Landslide Susceptibility ◽  
Data Driven ◽  
Natural Phenomenon ◽  
Landslide Occurrence ◽  
Conditioning Factors ◽  
Aerial Photogrammetry ◽  
Prone Area ◽  
Using Data

Abstract. Landslide is a frequently observed natural phenomenon and a geohazard with destructive effects on economies, society and the environment. Production of up-to-date landslide susceptibility (LS) maps is an essential process for landslide hazard mitigation. Obtaining up-to-date and accurate data for the production of LS maps is also important and this task can be achieved by using aerial photogrammetric techniques, which can produce geospatial data with high resolution. The produced geospatial datasets can be integrated in data-driven methods for obtaining accurate LS maps. In the present study, LS map was produced by using data-driven machine learning (ML) methods, i.e. random forest (RF). An earthquake and landslide prone area from the south-eastern part of Turkey was selected as the study area. Topographical derivatives were extracted from digital surface models (DSMs) produced by using aerial photogrammetric datasets with 30 cm ground sampling distances. The lithological parameters were employed in the study together with an accurate landslide inventory, which were also delineated by using the high-resolution DSMs and orthophotos. The relationships between the landslide occurrence and the pre-defined conditioning factors were analyzed using the frequency ratio (FR) method. The results show that the RF method exhibits high prediction performance in the study area with an area under curve (AUC) value of 0.92.

Development of a Pediatric Early Warning System Using Data-Driven Vital Signs

PEDIATRICS ◽  
2016 ◽  
Vol 137 (Supplement 3) ◽  
pp. 256A-256A
Author(s):  
Catherine Ross ◽  
Iliana Harrysson ◽  
Lynda Knight ◽  
Veena Goel ◽  
Sarah Poole ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Vital Signs ◽  
Warning System ◽  
Data Driven ◽  
Using Data

Enhanced Resilient State Estimation Using Data-Driven Auxiliary Models

2020 ◽  
Vol 16 (1) ◽  
pp. 639-647 ◽  
Author(s):  
Olugbenga Moses Anubi ◽  
Charalambos Konstantinou
Keyword(s):  
State Estimation ◽  
Data Driven ◽  
Using Data

scholarly journals Integration of various scales for measurement of insomnia

2021 ◽  
pp. 263208432110100
Author(s):  
Satyendra Nath Chakrabartty
Keyword(s):  
Test Scores ◽  
Data Driven ◽  
Weighted Sum ◽  
Future Studies ◽  
Normal Probability ◽  
Equivalent Test ◽  
Item Scores ◽  
Using Data ◽  
Set Up ◽  
Different Levels

Background Scales for evaluating insomnia differ in number of items, response format, and result in different scores distributions and score ranges and may not facilitate meaningful comparisons. Objectives Transform ordinal item-scores of three scales of insomnia to continuous, equidistant, monotonic, normally distributed scores, avoiding limitations of summative scoring of Likert scales. Methods Equidistant item-scores by weighted sum using data-driven weights to different levels of different items, considering cell frequencies of Item-Levels matrix, followed by normalization and conversion to [1, 10]. Equivalent test-scores (as sum of transformed item- scores) for a pair of scales were found by Normal Probability curves. Empirical illustration given. Results Transformed test-scores are continuous, monotonic and followed Normal distribution with no outliers and tied scores. Such test-scores facilitate ranking, better classification and meaningful comparison of scales of different lengths and formats and finding equivalent score combinations of two scales. For a given value of transformed test-score of a scale, easy alternate method avoiding integration proposed to find equivalent scores of another scales. Equivalent scores of scales help to relate various cut-off scores of different scales and uniformity in interpretations. Integration of various scales of insomnia is achieved by finding one-to-one correspondence among the equivalent score of various scales with correlation over 0.99 Conclusion Resultant test-scores facilitated undertaking analysis in parametric set up. Considering the theoretical advantages including meaningfulness of operations, better comparison, use of such method of transforming scores of Likert items/test is recommended test and items, Future studies were suggested.

Prediction of industrial debutanizer column compositions using data-driven ANFIS- and ANN-based approaches

2021 ◽  
Author(s):  
Syeda Anmol Fatima ◽  
Nasser Ramli ◽  
Syed Ali Ammar Taqvi ◽  
Haslinda Zabiri
Keyword(s):  
Data Driven ◽  
Using Data
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