Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning Techniques
This paper presents the application of a multiple number of statistical methods and machine learning techniques to model the relationship between rice yield and climate variables of a major region in Sri Lanka, which contributes significantly to the country’s paddy harvest. Rainfall, temperature (mi...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2021/6646126 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832546991636217856 |
|---|---|
| author | Lasini Wickramasinghe Rukmal Weliwatta Piyal Ekanayake Jeevani Jayasinghe |
| author_facet | Lasini Wickramasinghe Rukmal Weliwatta Piyal Ekanayake Jeevani Jayasinghe |
| author_sort | Lasini Wickramasinghe |
| collection | DOAJ |
| description | This paper presents the application of a multiple number of statistical methods and machine learning techniques to model the relationship between rice yield and climate variables of a major region in Sri Lanka, which contributes significantly to the country’s paddy harvest. Rainfall, temperature (minimum and maximum), evaporation, average wind speed (morning and evening), and sunshine hours are the climatic factors considered for modeling. Rice harvest and yield data over the last three decades and monthly climatic data were used to develop the prediction model by applying artificial neural networks (ANNs), support vector machine regression (SVMR), multiple linear regression (MLR), Gaussian process regression (GPR), power regression (PR), and robust regression (RR). The performance of each model was assessed in terms of the mean squared error (MSE), correlation coefficient (R), mean absolute percentage error (MAPE), root mean squared error ratio (RSR), BIAS value, and the Nash number, and it was found that the GPR-based model is the most accurate among them. Climate data collected until early 2019 (Maha season of year 2018) were used to develop the model, and an independent validation was performed by applying data of the Yala season of year 2019. The developed model can be used to forecast the future rice yield with very high accuracy. |
| format | Article |
| id | doaj-art-135d5c6e33ac4c9eb1d9965471f414ac |
| institution | Kabale University |
| issn | 2314-4629 2314-4785 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-135d5c6e33ac4c9eb1d9965471f414ac2025-02-03T06:46:15ZengWileyJournal of Mathematics2314-46292314-47852021-01-01202110.1155/2021/66461266646126Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning TechniquesLasini Wickramasinghe0Rukmal Weliwatta1Piyal Ekanayake2Jeevani Jayasinghe3Faculty of Applied Sciences, Wayamba University of Sri Lanka, Kuliyapitiya, Sri LankaFaculty of Applied Sciences, Wayamba University of Sri Lanka, Kuliyapitiya, Sri LankaFaculty of Applied Sciences, Wayamba University of Sri Lanka, Kuliyapitiya, Sri LankaFaculty of Applied Sciences, Wayamba University of Sri Lanka, Kuliyapitiya, Sri LankaThis paper presents the application of a multiple number of statistical methods and machine learning techniques to model the relationship between rice yield and climate variables of a major region in Sri Lanka, which contributes significantly to the country’s paddy harvest. Rainfall, temperature (minimum and maximum), evaporation, average wind speed (morning and evening), and sunshine hours are the climatic factors considered for modeling. Rice harvest and yield data over the last three decades and monthly climatic data were used to develop the prediction model by applying artificial neural networks (ANNs), support vector machine regression (SVMR), multiple linear regression (MLR), Gaussian process regression (GPR), power regression (PR), and robust regression (RR). The performance of each model was assessed in terms of the mean squared error (MSE), correlation coefficient (R), mean absolute percentage error (MAPE), root mean squared error ratio (RSR), BIAS value, and the Nash number, and it was found that the GPR-based model is the most accurate among them. Climate data collected until early 2019 (Maha season of year 2018) were used to develop the model, and an independent validation was performed by applying data of the Yala season of year 2019. The developed model can be used to forecast the future rice yield with very high accuracy.http://dx.doi.org/10.1155/2021/6646126 |
| spellingShingle | Lasini Wickramasinghe Rukmal Weliwatta Piyal Ekanayake Jeevani Jayasinghe Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning Techniques Journal of Mathematics |
| title | Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning Techniques |
| title_full | Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning Techniques |
| title_fullStr | Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning Techniques |
| title_full_unstemmed | Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning Techniques |
| title_short | Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning Techniques |
| title_sort | modeling the relationship between rice yield and climate variables using statistical and machine learning techniques |
| url | http://dx.doi.org/10.1155/2021/6646126 |
| work_keys_str_mv | AT lasiniwickramasinghe modelingtherelationshipbetweenriceyieldandclimatevariablesusingstatisticalandmachinelearningtechniques AT rukmalweliwatta modelingtherelationshipbetweenriceyieldandclimatevariablesusingstatisticalandmachinelearningtechniques AT piyalekanayake modelingtherelationshipbetweenriceyieldandclimatevariablesusingstatisticalandmachinelearningtechniques AT jeevanijayasinghe modelingtherelationshipbetweenriceyieldandclimatevariablesusingstatisticalandmachinelearningtechniques |