FFM: Flood Forecasting Model Using Federated Learning-Reference-Cited by-同舟云学术

FFM: Flood Forecasting Model Using Federated Learning

Published:2023 Issue: Volume:11 Page:24472-24483
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Farooq Muhammad Shoaib¹^ORCID,Tehseen Rabia²,Qureshi Junaid Nasir³^ORCID,Omer Uzma⁴^ORCID,Yaqoob Rimsha⁵,Tanweer Hafiz Abdullah¹,Atal Zabihullah⁶^ORCID

Affiliation:

1. Department of Computer Science, University of Management and Technology, Lahore, Pakistan

2. Department of Computer Science, University of Central Punjab, Lahore, Pakistan

3. Department of Computer Science, Bahria University Lahore Campus, Lahore, Pakistan

4. Department of Computer Science, University of Education, Lahore, Pakistan

5. Institute of Electrical, Electronics and Computer Engineering, University of the Punjab, Lahore, Pakistan

6. Department of Computer Science, Kardan University, Kabul, Afghanistan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10058950.pdf?arnumber=10058950

Reference54 articles.

1. Regional flood frequency analysis in eastern Australia: Bayesian GLS regression-based methods within fixed region and ROI framework – Quantile Regression vs. Parameter Regression Technique

2. Application of artificial neural networks in regional flood frequency analysis: a case study for Australia

3. Flood, landslides, forest fire, and earthquake susceptibility maps using machine learning techniques and their combination

4. A real-time forecast model using artificial neural network for after-runner storm surges on the Tottori coast, Japan

5. A framework for the prediction of earthquake using federated learning

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