Flood Susceptibility Assessment Using Machine Learning in Andhra Pradesh
Kovvuri Teja Praveen Reddy
, Pappala Venkata Ramana , Yerramsetti Krishna , Yedida Durga Naga Manikanta , Laxmi Narayana Pasupuleti
ANN, LULC, Flood Hazard, Andhra Pradesh, disaster risk management
Floods are among the most devastating natural hazards, causing significant damage to human life, infrastructure, and the environment. Accurate identification of flood-prone areas is therefore essential for effective disaster risk management and mitigation planning. This study aims to assess flood susceptibility in Andhra Pradesh, India, by machine learning (ML) models. A set of eight flood conditioning factors viz., rainfall, elevation, slope, drainage density, distance from river, topographic wetness index (TWI), soil texture, and land use/land cover (LULC) were derived from remote sensing and spatial datasets. A flood inventory database comprising 120 points (50 flood and 70 non-flood locations) was developed and used for model training and validation. Three machine learning models, namely Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were employed to predict flood susceptibility. Model performance was evaluated using statistical indicators including Precision. Flood susceptibility maps were generated and classified into five categories: very low, low, moderate, high, and very high susceptibility. The results reveal that high susceptibility zones are predominantly concentrated in coastal and river basin regions of Andhra Pradesh. The findings demonstrate that the machine learning techniques provides a robust and reliable framework for flood susceptibility assessment. The generated maps can support policymakers, planners, and disaster management authorities in implementing effective flood mitigation and land-use planning strategies.
"Flood Susceptibility Assessment Using Machine Learning in Andhra Pradesh", JETNR - JOURNAL OF EMERGING TRENDS AND NOVEL RESEARCH (www.JETNR.org), ISSN:2984-9276, Vol.4, Issue 4, page no.b706-b711, April-2026, Available :https://rjpn.org/JETNR/papers/JETNR2604227.pdf
Volume 4
Issue 4,
April-2026
Pages : b706-b711
Paper Reg. ID: JETNR_233841
Published Paper Id: JETNR2604227
Downloads: 00041
Research Area: Science and Technology
Country: surampalem, Andhra Pradesh, India
ISSN: 2984-9276 | IMPACT FACTOR: 9.87 Calculated By Google Scholar | ESTD YEAR: 2023
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.87 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: RJPN (IJPublication) Janvi Wave
