Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.number | 21 | - |
dc.citation.startPage | 3499 | - |
dc.citation.title | REMOTE SENSING | - |
dc.citation.volume | 12 | - |
dc.contributor.author | Park, Sumin | - |
dc.contributor.author | Im, Jungho | - |
dc.contributor.author | Han, Daehyeon | - |
dc.contributor.author | Rhee, Jinyoung | - |
dc.date.accessioned | 2023-12-21T16:43:02Z | - |
dc.date.available | 2023-12-21T16:43:02Z | - |
dc.date.created | 2020-12-07 | - |
dc.date.issued | 2020-11 | - |
dc.description.abstract | Drought forecasting is essential for effectively managing drought-related damage and providing relevant drought information to decision-makers so they can make appropriate decisions in response to drought. Although there have been great efforts in drought-forecasting research, drought forecasting on a short-term scale (up to two weeks) is still difficult. In this research, drought-forecasting models on a short-term scale (8 days) were developed considering the temporal patterns of satellite-based drought indices and numerical model outputs through the synergistic use of convolutional long short term memory (ConvLSTM) and random forest (RF) approaches over a part of East Asia. Two widely used drought indices-Scaled Drought Condition Index (SDCI) and Standardized Precipitation Index (SPI)-were used as target variables. Through the combination of temporal patterns and the upcoming weather conditions (numerical model outputs), the overall performances of drought-forecasting models (ConvLSTM and RF combined) produced competitive results in terms of r (0.90 and 0.93 for validation SDCI and SPI, respectively) and nRMSE (0.11 and 0.08 for validation of SDCI and SPI, respectively). Furthermore, our short-term drought-forecasting model can be effective regardless of drought intensification or alleviation. The proposed drought-forecasting model can be operationally used, providing useful information on upcoming drought conditions with high resolution (0.05 degrees). | - |
dc.identifier.bibliographicCitation | REMOTE SENSING, v.12, no.21, pp.3499 | - |
dc.identifier.doi | 10.3390/rs12213499 | - |
dc.identifier.issn | 2072-4292 | - |
dc.identifier.scopusid | 2-s2.0-85094149661 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/48837 | - |
dc.identifier.url | https://www.mdpi.com/2072-4292/12/21/3499 | - |
dc.identifier.wosid | 000589191300001 | - |
dc.language | 영어 | - |
dc.publisher | MDPI | - |
dc.title | Short-Term Forecasting of Satellite-Based Drought Indices Using Their Temporal Patterns and Numerical Model Output | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.relation.journalWebOfScienceCategory | Remote Sensing | - |
dc.relation.journalResearchArea | Remote Sensing | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | drought forecasting | - |
dc.subject.keywordAuthor | SDCI | - |
dc.subject.keywordAuthor | SPI | - |
dc.subject.keywordAuthor | ConvLSTM | - |
dc.subject.keywordAuthor | RF | - |
dc.subject.keywordAuthor | numerical model output | - |
dc.subject.keywordPlus | AWASH RIVER-BASIN | - |
dc.subject.keywordPlus | TIME-SERIES | - |
dc.subject.keywordPlus | AGRICULTURAL DROUGHT | - |
dc.subject.keywordPlus | WAVELET TRANSFORMS | - |
dc.subject.keywordPlus | CLIMATE FORECAST | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
dc.subject.keywordPlus | LONG | - |
dc.subject.keywordPlus | PRECIPITATION | - |
dc.subject.keywordPlus | TEMPERATURE | - |
dc.subject.keywordPlus | SPI | - |
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