Machine Learning Modelling of the Relationship between Weather and Paddy Yield in Sri Lanka
This paper presents the development of crop-weather models for the paddy yield in Sri Lanka based on nine weather indices, namely, rainfall, relative humidity (minimum and maximum), temperature (minimum and maximum), wind speed (morning and evening), evaporation, and sunshine hours. The statistics o...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2021/9941899 |
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| author | Piyal Ekanayake Windhya Rankothge Rukmal Weliwatta Jeevani W. Jayasinghe |
| author_facet | Piyal Ekanayake Windhya Rankothge Rukmal Weliwatta Jeevani W. Jayasinghe |
| author_sort | Piyal Ekanayake |
| collection | DOAJ |
| description | This paper presents the development of crop-weather models for the paddy yield in Sri Lanka based on nine weather indices, namely, rainfall, relative humidity (minimum and maximum), temperature (minimum and maximum), wind speed (morning and evening), evaporation, and sunshine hours. The statistics of seven geographical regions, which contribute to about two-thirds of the country’s total paddy production, were used for this study. The significance of the weather indices on the paddy yield was explored by employing Random Forest (RF) and the variable importance of each of them was determined. Pearson’s correlation and Spearman’s correlation were used to identify the behavior of correlation in a positive or negative direction. Further, the pairwise correlation among the weather indices was examined. The results indicate that the minimum relative humidity and the maximum temperature during the paddy cultivation period are the most influential weather indices. Moreover, RF was used to develop a paddy yield prediction model and four more techniques, namely, Power Regression (PR), Multiple Linear Regression (MLR) with stepwise selection, forward (step-up) selection, and backward (step-down) elimination, were used to benchmark the performance of the machine learning technique. Their performances were compared in terms of the Root Mean Squared Error (RMSE), Correlation Coefficient (R), Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE). As per the results, RF is a reliable and accurate model for the prediction of paddy yield in Sri Lanka, demonstrating a very high R of 0.99 and the least MAPE of 1.4%. |
| format | Article |
| id | doaj-art-71029559f4b945ceb4d1aa4964bd9cb3 |
| institution | Kabale University |
| issn | 2314-4629 2314-4785 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
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| series | Journal of Mathematics |
| spelling | doaj-art-71029559f4b945ceb4d1aa4964bd9cb32025-02-03T01:05:26ZengWileyJournal of Mathematics2314-46292314-47852021-01-01202110.1155/2021/99418999941899Machine Learning Modelling of the Relationship between Weather and Paddy Yield in Sri LankaPiyal Ekanayake0Windhya Rankothge1Rukmal Weliwatta2Jeevani W. Jayasinghe3Faculty of Applied Sciences, Wayamba University of Sri Lanka, Kuliyapitiya, Sri LankaFaculty of Computing, Sri Lanka Institute of Information Technology, Colombo, 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 development of crop-weather models for the paddy yield in Sri Lanka based on nine weather indices, namely, rainfall, relative humidity (minimum and maximum), temperature (minimum and maximum), wind speed (morning and evening), evaporation, and sunshine hours. The statistics of seven geographical regions, which contribute to about two-thirds of the country’s total paddy production, were used for this study. The significance of the weather indices on the paddy yield was explored by employing Random Forest (RF) and the variable importance of each of them was determined. Pearson’s correlation and Spearman’s correlation were used to identify the behavior of correlation in a positive or negative direction. Further, the pairwise correlation among the weather indices was examined. The results indicate that the minimum relative humidity and the maximum temperature during the paddy cultivation period are the most influential weather indices. Moreover, RF was used to develop a paddy yield prediction model and four more techniques, namely, Power Regression (PR), Multiple Linear Regression (MLR) with stepwise selection, forward (step-up) selection, and backward (step-down) elimination, were used to benchmark the performance of the machine learning technique. Their performances were compared in terms of the Root Mean Squared Error (RMSE), Correlation Coefficient (R), Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE). As per the results, RF is a reliable and accurate model for the prediction of paddy yield in Sri Lanka, demonstrating a very high R of 0.99 and the least MAPE of 1.4%.http://dx.doi.org/10.1155/2021/9941899 |
| spellingShingle | Piyal Ekanayake Windhya Rankothge Rukmal Weliwatta Jeevani W. Jayasinghe Machine Learning Modelling of the Relationship between Weather and Paddy Yield in Sri Lanka Journal of Mathematics |
| title | Machine Learning Modelling of the Relationship between Weather and Paddy Yield in Sri Lanka |
| title_full | Machine Learning Modelling of the Relationship between Weather and Paddy Yield in Sri Lanka |
| title_fullStr | Machine Learning Modelling of the Relationship between Weather and Paddy Yield in Sri Lanka |
| title_full_unstemmed | Machine Learning Modelling of the Relationship between Weather and Paddy Yield in Sri Lanka |
| title_short | Machine Learning Modelling of the Relationship between Weather and Paddy Yield in Sri Lanka |
| title_sort | machine learning modelling of the relationship between weather and paddy yield in sri lanka |
| url | http://dx.doi.org/10.1155/2021/9941899 |
| work_keys_str_mv | AT piyalekanayake machinelearningmodellingoftherelationshipbetweenweatherandpaddyyieldinsrilanka AT windhyarankothge machinelearningmodellingoftherelationshipbetweenweatherandpaddyyieldinsrilanka AT rukmalweliwatta machinelearningmodellingoftherelationshipbetweenweatherandpaddyyieldinsrilanka AT jeevaniwjayasinghe machinelearningmodellingoftherelationshipbetweenweatherandpaddyyieldinsrilanka |