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| DOI | 10.1007/s00477-024-02700-8 |
| Dynamic clustering of spatial-temporal rainfall and temperature data over multi-sites in Yemen using multivariate functional approach | |
| 发表日期 | 2024 |
| ISSN | 1436-3240 |
| EISSN | 1436-3259 |
| 英文摘要 | Analyzing Multivariate Functional Data (MFD) presents growing challenges in the context of climate change modeling due to many issues, such as coarse resolution, model complexity, and big data processing. In this regard, we introduced a Multivariate Functional Model-Based Clustering (MFMBC) method to analyze Multivariate Functional Rainfall and Temperature (MFRT) data. The data was collected spanning four decades (Jan.1980-Apr.2022) over 37 locations in Yemen. The main objective is to identify the underlying spatial-temporal dynamic structure of MFRT data and model the association/interrelationship between data. The proposed MFMBC method consists of three key phases: projecting MFRT data variation through Multivariate Functional Principal Component Analysis (MFPCA), identifying optimal clusters with Bayesian Information Criteria (BIC), and optimizing model parameters using Expectation-Maximization (EM) algorithm. According to the findings, three ideal clusters for MFRT data profiles were identified and labeled as severe, moderate, and high temperatures, which correspond to heavy, moderate, and light rainfall patterns. Cluster 1 had a negative nexus characterized by slight changes and low-peak rainfall with high changes and large-peak temperatures. Cluster 2 exhibited a natural nexus with a mild pattern in both rainfall and temperature. Cluster 3 had positive-nexus displayed significant variations with large-volume peaks in rainfall and temperature. Overall, these results help in assessing the complex interaction between rainfall and temperature over the spatial-temporal domain and offer valuable insights for policy-makers to address climate-related challenges. |
| 英文关键词 | Multivariate functional data; Multivariate functional model-based clustering; Spatial-temporal modeling; Climate change; Yemen |
| 语种 | 英语 |
| WOS研究方向 | Engineering ; Environmental Sciences & Ecology ; Mathematics ; Water Resources |
| WOS类目 | Engineering, Environmental ; Engineering, Civil ; Environmental Sciences ; Statistics & Probability ; Water Resources |
| WOS记录号 | WOS:001194670100003 |
| 来源期刊 | STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/288815 |
| 作者单位 | Jiangxi University of Finance & Economics; Taiz University; Wuhan University of Technology; Central South University |
| 推荐引用方式 GB/T 7714 | . Dynamic clustering of spatial-temporal rainfall and temperature data over multi-sites in Yemen using multivariate functional approach[J],2024. |
| APA | (2024).Dynamic clustering of spatial-temporal rainfall and temperature data over multi-sites in Yemen using multivariate functional approach.STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT. |
| MLA | "Dynamic clustering of spatial-temporal rainfall and temperature data over multi-sites in Yemen using multivariate functional approach".STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT (2024). |
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