Towards Low Cost Nowcasting of Flash Floods in Mauritius: Assessing the Effectiveness of a Combined Approach Involving Wireless Sensor Network and Machine Learning
Reference (Identifier)
MRR-20-00026
Title
Towards Low Cost Nowcasting of Flash Floods in Mauritius: Assessing the Effectiveness of a Combined Approach Involving Wireless Sensor Network and Machine Learning
Broad Theme Classification
1. Natural Sciences
Second Level Classification
1.2 Computer and Information Sciences
Country Specific Sectors
3. Climate Change
Institution
Co-Author 1 Name
Dr. Girish Bekaroo
Co-Author 2 Name
Dr. Kumar Dookhitram
Co-Author 3 Name
Mr. Nikhilesh Shankhur
Page Range
1-116
Date Published
2 December 2019
Abstract
This study aims at assessing the effectiveness of a low-cost prototype to nowcast flash floods in the Mauritian context. A system based on simulation, using Wireless Sensor Network and modern Machine Learning techniques has been designed and implemented for the prediction of flash floods. The Wireless Sensor Network component reads and collects different features from river flow and rainfall monitoring. It has been tested through simulation and is estimated to have a relatively low unit cost. The Machine Learning component of the system is based on a deep learning approach with the implementation of the Recurrent Neural Network (RNN), and has been trained and tested using simulated datasets. The efficiency of the model has been further optimised with the application of Genetic Algorithm, and experiments demonstrate a relatively low error in predictions. The results achieved in this study cannot be generalized for the Mauritian context at large, but serve as an approach for the development of an automated flash flood nowcasting system based on rainfall and water flow monitoring in rivers/canals.
Keywords
Flash floods,nowcasting,wireless sensor network,machine learning
Language
English
Publisher
Mauritius Research and Innovation Council
Content Classification
Report
Funding Agency(ies)
Mauritius Research and Innovation Council
Note/Remarks
Status of project: completed in December 2019
Date Uploaded
2 June 2020
Size
6.9 MB
File Format
.pdf
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