Extending Multi-Output Methods for Long-Term Aboveground Biomass Time Series Forecasting Using Convolutional Neural Networks
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| Publicado en: | Machine Learning and Knowledge Extraction vol. 6, no. 3 (2024), p. 1633 |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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|---|---|---|---|
| 001 | 3110556099 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2504-4990 | ||
| 024 | 7 | |a 10.3390/make6030079 |2 doi | |
| 035 | |a 3110556099 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 100 | 1 | |a Noa-Yarasca, Efrain |u Texas A&M AgriLife Research, Blackland Research and Extension Center, Temple, TX 76502, USA; <email>javier.osorio@ag.tamu.edu</email> | |
| 245 | 1 | |a Extending Multi-Output Methods for Long-Term Aboveground Biomass Time Series Forecasting Using Convolutional Neural Networks | |
| 260 | |b MDPI AG |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Accurate aboveground vegetation biomass forecasting is essential for livestock management, climate impact assessments, and ecosystem health. While artificial intelligence (AI) techniques have advanced time series forecasting, a research gap in predicting aboveground biomass time series beyond single values persists. This study introduces RECMO and DirRecMO, two multi-output methods for forecasting aboveground vegetation biomass. Using convolutional neural networks, their efficacy is evaluated across short-, medium-, and long-term horizons on six Kenyan grassland biomass datasets, and compared with that of existing single-output methods (Recursive, Direct, and DirRec) and multi-output methods (MIMO and DIRMO). The results indicate that single-output methods are superior for short-term predictions, while both single-output and multi-output methods exhibit a comparable effectiveness in long-term forecasts. RECMO and DirRecMO outperform established multi-output methods, demonstrating a promising potential for biomass forecasting. This study underscores the significant impact of multi-output size on forecast accuracy, highlighting the need for optimal size adjustments and showcasing the proposed methods’ flexibility in long-term forecasts. Short-term predictions show less significant differences among methods, complicating the identification of the best performer. However, clear distinctions emerge in medium- and long-term forecasts, underscoring the greater importance of method choice for long-term predictions. Moreover, as the forecast horizon extends, errors escalate across all methods, reflecting the challenges of predicting distant future periods. This study suggests advancing hybrid models (e.g., RECMO and DirRecMO) to improve extended horizon forecasting. Future research should enhance adaptability, investigate multi-output impacts, and conduct comparative studies across diverse domains, datasets, and AI algorithms for robust insights. | |
| 610 | 4 | |a Agency for International Development | |
| 651 | 4 | |a Kenya | |
| 651 | 4 | |a East Africa | |
| 653 | |a Livestock | ||
| 653 | |a Collaboration | ||
| 653 | |a Forecasting | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Biodiversity | ||
| 653 | |a Biomass | ||
| 653 | |a Time series | ||
| 653 | |a Climate change | ||
| 653 | |a Comparative studies | ||
| 653 | |a Vegetation | ||
| 653 | |a Datasets | ||
| 653 | |a Growth models | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Sustainable development | ||
| 653 | |a Neural networks | ||
| 653 | |a Grasslands | ||
| 653 | |a Effectiveness | ||
| 653 | |a Algorithms | ||
| 653 | |a Methods | ||
| 653 | |a Predictions | ||
| 700 | 1 | |a Osorio Leyton, Javier M |u Texas A&M AgriLife Research, Blackland Research and Extension Center, Temple, TX 76502, USA; <email>javier.osorio@ag.tamu.edu</email> | |
| 700 | 1 | |a Angerer, Jay P |u USDA Agricultural Research Service—Livestock and Range Research Laboratory, Miles City, MT 59301, USA; <email>jay.angerer@usda.gov</email> | |
| 773 | 0 | |t Machine Learning and Knowledge Extraction |g vol. 6, no. 3 (2024), p. 1633 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3110556099/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3110556099/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3110556099/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |