research-article

Multilayered Ensemble Learning for short-term forecasting in agro-climatology

Authors:

Jade Guisiano,

Raja Chiky,

Julien Orensanz,

Shohreh AhvarAuthors Info & Claims

ICCTA '20: Proceedings of the 2020 6th International Conference on Computer and Technology Applications

Pages 7 - 11

https://doi.org/10.1145/3397125.3397130

Published: 29 May 2020 Publication History

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Abstract

Agriculture is one of the areas whose activities depend heavily on weather forecasts. Indeed, in order to optimize their production, farmers must be able to anticipate climate conditions favorable or not to their activities by deploying the appropriate action plans. For this purpose, they consult the data daily from various suppliers of weather forecasts. However, the reliability of the forecasts of each supplier is variable according to the period, the climate or the geographical area. Farmers, therefore, have to arbitrate between suppliers daily. This paper proposes a new set of learning architecture that significantly improves the accuracy of weather short-term forecasts for the next 1-12h in order to assist farmers in decision-making.

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Cited By

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Nti IAdekoya AWeyori BNyarko-Boateng O(2021)Applications of artificial intelligence in engineering and manufacturing: a systematic reviewJournal of Intelligent Manufacturing10.1007/s10845-021-01771-633:6(1581-1601)Online publication date: 15-Apr-2021
https://doi.org/10.1007/s10845-021-01771-6

Index Terms

Multilayered Ensemble Learning for short-term forecasting in agro-climatology
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Ensemble methods

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Published In

ICCTA '20: Proceedings of the 2020 6th International Conference on Computer and Technology Applications

April 2020

178 pages

ISBN:9781450377492

DOI:10.1145/3397125

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

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Published: 29 May 2020

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ICCTA '20

ICCTA '20: 2020 6th International Conference on Computer and Technology Applications

April 14 - 16, 2020

Antalya, Turkey

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Nti IAdekoya AWeyori BNyarko-Boateng O(2021)Applications of artificial intelligence in engineering and manufacturing: a systematic reviewJournal of Intelligent Manufacturing10.1007/s10845-021-01771-633:6(1581-1601)Online publication date: 15-Apr-2021
https://doi.org/10.1007/s10845-021-01771-6

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Short-term natural gas consumption forecasting

Coupling the short-term global forecast system weather data with a variable source area hydrologic model

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