A Remote Sensing-Based Approach for Debris-Flow Susceptibility Assessment Using Artificial Neural Networks and Logistic Regression Modeling

Racha Elkadiri; Mohamed Sultan; Ahmed Youssef; Tamer Elbayoumi; Ronald Chase; Ali Bulkhi; Mohamed Al-Katheeri

A Remote Sensing-Based Approach for Debris-Flow Susceptibility Assessment Using Artificial Neural Networks and Logistic Regression Modeling

Racha Elkadiri, Mohamed Sultan, Ahmed Youssef, Tamer Elbayoumi, Ronald Chase, Ali Bulkhi, Mohamed Al-Katheeri

Mathematics and Statistics

Research output: Contribution to journal › Article

Original language	English
Journal	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume	7
Issue number	12
State	Published - 2014

Cite this

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title = "A Remote Sensing-Based Approach for Debris-Flow Susceptibility Assessment Using Artificial Neural Networks and Logistic Regression Modeling",

author = "Racha Elkadiri and Mohamed Sultan and Ahmed Youssef and Tamer Elbayoumi and Ronald Chase and Ali Bulkhi and Mohamed Al-Katheeri",

year = "2014",

language = "English",

volume = "7",

journal = "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing",

number = "12",

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TY - JOUR

T1 - A Remote Sensing-Based Approach for Debris-Flow Susceptibility Assessment Using Artificial Neural Networks and Logistic Regression Modeling

AU - Elkadiri, Racha

AU - Sultan, Mohamed

AU - Youssef, Ahmed

AU - Elbayoumi, Tamer

AU - Chase, Ronald

AU - Bulkhi, Ali

AU - Al-Katheeri, Mohamed

PY - 2014

Y1 - 2014

M3 - Article

VL - 7

JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

IS - 12

ER -