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Detection of Ephemeral Sand River Flow Using Hybrid Sandpiper Optimization-Based CNN Model
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Author(s): Arunadevi Thirumalraj (K. Ramakrishnan College of Technology, India), V. S. Anusuya (New Horizon College of Engineering, India)and B. Manjunatha (New Horizon College of Engineering, India)
Copyright: 2024
Pages: 20
Source title:
Innovations in Machine Learning and IoT for Water Management
Source Author(s)/Editor(s): Abhishek Kumar (Chandigarh University, India), Arun Lal Srivastav (Chitkara University, India), Ashutosh Kumar Dubey (University of Castilla-La Mancha, Spain), Vishal Dutt (AVN Innovations Pvt. Ltd., India)and Narayan Vyas (AVN Innovations Pvt. Ltd., India)
DOI: 10.4018/979-8-3693-1194-3.ch010
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Abstract
Ephemeral sand rivers are a major supply of water in Southern Africa that flow continuously all year. The fact is a sizeable fraction of this water permeates the silt in the riverbed, protecting it from evaporation and keeping it available to farmers throughout the dry season. This study set out to investigate the usefulness of satellite optical data in order to assess the possibility for discovering unexpected surface flows. The spatio-temporal resolution required to identify irregular flows in the comparatively small sand rivers typical of dry regions. A hybrid pre-trained convolutional neural network is used to execute data categorization using the hybrid sandpiper optimization technique. Sentinel-2's higher spatial and temporal resolution allowed for accurate surface water identification even in conditions where river flow had drastically decreased and the riverbeds were heavily hidden by cloud cover. The model suggested in this study fared better than rival models in this field, obtaining a remarkable accuracy rate of 99.77%.
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