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Research Article
Shelendra Pal
Teerthanker Mahaveer University
Corresponding Author
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Landslides have the potential to cause significant property damage as well as fatalities. Landslides are identified by real-time heuristic data analysis acquired using wireless sensor networks (WSNs) in changing environments. People can abandon dangerous locations earlier when landslides are forecasted. In this paper, the early warning prediction system developed using machine learning; Artificial Neural Networks (ANNs) provide precise predictions. The weight coefficients of ANN can be adjusted exactly enough by network functional training. In the case of unbalanced data distribution, the proposed ANN model is unable to learn the sample data pattern. This results in incorrect prediction and therefore, a switching method is utilized to switch between alternative predictors based on the current environmental state. Furthermore, the proposed model has been developed to forecast and compensate for errors during the prediction phase. Thus, the proposed model can enhance precise prediction, and an early warning prediction system of landslides can issue warnings 44.2 minutes before a landslide occurrence.
Keywords
Landslide, Warning prediction, Artificial Neural Networks (ANNs), Back-Propagation Neural Network (BPNN)
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CURRENT STATUS: Under Review
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Posted 26 Oct, 2021
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Editorial decision: Minor Revision
On 01 Nov, 2021
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On 29 Sep, 2021
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This preprint is under consideration at Soft Computing. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Shelendra Pal
Teerthanker Mahaveer University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 26 Oct, 2021
No community comments so far
Editorial decision: Minor Revision
On 01 Nov, 2021
First submitted
On 29 Sep, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Landslides have the potential to cause significant property damage as well as fatalities. Landslides are identified by real-time heuristic data analysis acquired using wireless sensor networks (WSNs) in changing environments. People can abandon dangerous locations earlier when landslides are forecasted. In this paper, the early warning prediction system developed using machine learning; Artificial Neural Networks (ANNs) provide precise predictions. The weight coefficients of ANN can be adjusted exactly enough by network functional training. In the case of unbalanced data distribution, the proposed ANN model is unable to learn the sample data pattern. This results in incorrect prediction and therefore, a switching method is utilized to switch between alternative predictors based on the current environmental state. Furthermore, the proposed model has been developed to forecast and compensate for errors during the prediction phase. Thus, the proposed model can enhance precise prediction, and an early warning prediction system of landslides can issue warnings 44.2 minutes before a landslide occurrence.
This preprint is available for download as a PDF.
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