Graph Learning-based Regional Heavy Rainfall Prediction Using Low-Cost Rain Gauges
Shranjeno v:
| izdano v: | arXiv.org (Dec 22, 2024), p. n/a |
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| Glavni avtor: | |
| Izdano: |
Cornell University Library, arXiv.org
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| Teme: | |
| Online dostop: | Citation/Abstract Full text outside of ProQuest |
| Oznake: |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3148980742 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3148980742 | ||
| 045 | 0 | |b d20241222 | |
| 100 | 1 | |a Salcedo, Edwin | |
| 245 | 1 | |a Graph Learning-based Regional Heavy Rainfall Prediction Using Low-Cost Rain Gauges | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 22, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Accurate and timely prediction of heavy rainfall events is crucial for effective flood risk management and disaster preparedness. By monitoring, analysing, and evaluating rainfall data at a local level, it is not only possible to take effective actions to prevent any severe climate variation but also to improve the planning of surface and underground hydrological resources. However, developing countries often lack the weather stations to collect data continuously due to the high cost of installation and maintenance. In light of this, the contribution of the present paper is twofold: first, we propose a low-cost IoT system for automatic recording, monitoring, and prediction of rainfall in rural regions. Second, we propose a novel approach to regional heavy rainfall prediction by implementing graph neural networks (GNNs), which are particularly well-suited for capturing the complex spatial dependencies inherent in rainfall patterns. The proposed approach was tested using a historical dataset spanning 72 months, with daily measurements, and experimental results demonstrated the effectiveness of the proposed method in predicting heavy rainfall events, making this approach particularly attractive for regions with limited resources or where traditional weather radar or station coverage is sparse. | |
| 653 | |a Regional development | ||
| 653 | |a Weather stations | ||
| 653 | |a Risk management | ||
| 653 | |a Low cost | ||
| 653 | |a Flood management | ||
| 653 | |a Emergency preparedness | ||
| 653 | |a Installation costs | ||
| 653 | |a Rainfall | ||
| 653 | |a Graph neural networks | ||
| 653 | |a Spatial dependencies | ||
| 653 | |a Meteorological radar | ||
| 653 | |a Effectiveness | ||
| 653 | |a Regional planning | ||
| 653 | |a Monitoring | ||
| 653 | |a Machine learning | ||
| 653 | |a Data collection | ||
| 653 | |a Rain | ||
| 653 | |a Developing countries--LDCs | ||
| 653 | |a Flood predictions | ||
| 773 | 0 | |t arXiv.org |g (Dec 22, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3148980742/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.16842 |