| Autor: |
Nurnabi Meherul Alam, Sabyasachi Mitra, Surendra Kumar Pandey, Chayna Jana, Mrinmoy Ray, Sourav Ghosh, Sonali Paul Mazumdar, S. Vishnu Shankar, Ritesh Saha, Gouranga Kar |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Water, Vol 16, Iss 13, p 1891 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2073-4441 |
| DOI: |
10.3390/w16131891 |
| Popis: |
Rainfall serves as a lifeline for crop cultivation in many agriculture-dependent countries including India. Being spatio-temporal data, the forecasting of rainfall becomes a more complex and tedious process. Application of conventional time series models and machine learning techniques will not be a suitable choice as they may not adequately account for the complex spatial and temporal dependencies integrated within the data. This demands some data-driven techniques that can handle the intrinsic patterns such as non-linearity, non-stationarity, and non-normality. Space–Time Autoregressive Moving Average (STARMA) models were highly known for its ability to capture both spatial and temporal dependencies, offering a comprehensive framework for analyzing complex datasets. Spatial Weight Matrix (SWM) developed by the STARMA model helps in integrating the spatial effects of the neighboring sites. The study employed a novel dataset consisting of annual rainfall measurements spanning over 50 (1970–2019) years from 119 different locations (grid of 0.25 × 0.25 degree resolution) of West Bengal, a state of India. These extensive datasets were split into testing and training groups that enable the better understanding of the rainfall patterns at a granular level. The study findings demonstrated a notable improvement in forecasting accuracy by the STARMA model that can exhibit promising implications for agricultural management and planning, particularly in regions vulnerable to climate variability. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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