Towards Resilient Agriculture to Hostile Climate Change in the Sahel Region: A Case Study of Machine Learning-Based Weather Prediction in Senegal
Abstract
:1. Introduction
1.1. Weather Forecasting and Usefulness of Weather Information in Agriculture
1.2. Problem Statement
1.3. Objectives
- 1.
- To determine the spatial weather distribution in Senegal;
- 2.
- To determine the weather annual cycle in Senegal.
1.4. Brief Literature Review
1.5. Description of the Study Area and Data Source
1.6. Dataset
1.7. Exploratory Data Analysis
1.7.1. Spatial Weather Distribution
1.7.2. Annual Weather Cycle
2. Materials and Methods
2.1. Data Pre-Processing and Transformation
Machine Learning Models
3. Presentation of Results and Discussion
3.1. Relative Humidity
3.2. Minimum Temperature
3.3. Maximum Temperature
3.4. Rainfall
4. Prediction on the Test Dataset
4.1. Spatial Weather Distribution
4.1.1. Relative Humidity
4.1.2. Maximum and Minimum Temperature
4.1.3. Rainfall
4.2. Annual Weather Cycle
4.2.1. Relative Humidity
4.2.2. Maximum Temperature and Minimum Temperature
4.2.3. Rainfall
4.3. Conclusions and Recommendations
4.4. Limitation of the Study
4.5. Future Work
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Station | Latitude | Longitude |
---|---|---|
Rosso | 16.5 N | −15.817 W |
Saint-Louis | 16.05 N | −16.483 W |
Cap-skiring | 12.4 N | −16.75 W |
Diourbel | 14.65 N | −16.233 W |
Kaolack | 14.133 N | −16.067 W |
Kedougou | 12.567 N | −12.217 W |
Kolda | 12.883 N | −14.967 W |
Linguere | 15.367 N | −15.117 W |
Matam | 15.617 N | −13.25 W |
Tambacounda | 13.767 N | −13.683 W |
Model | MAE | MSE | RMSE | |
---|---|---|---|---|
Ensemble Model | 4.0126 | 29.9885 | 5.4428 | 0.9335 |
Light Gradient Boosting Machine | 4.0693 | 30.6936 | 5.5040 | 0.9317 |
CatBoost Regressor | 4.0619 | 30.7052 | 5.5046 | 0.9317 |
Gradient Boosting Regressor | 4.0863 | 30.8061 | 5.5140 | 0.9314 |
Extreme Gradient Boosting | 4.1601 | 32.1831 | 5.6292 | 0.9280 |
Random Forest Regressor | 4.2284 | 32.9041 | 5.7005 | 0.9270 |
Orthogonal Matching Pursuit | 4.2385 | 33.1158 | 5.7223 | 0.9268 |
Extra Trees Regressor | 4.2533 | 33.3111 | 5.7349 | 0.9260 |
K Neighbors Regressor | 4.4810 | 36.5971 | 6.0138 | 0.9184 |
AdaBoost Regressor | 5.7023 | 48.4746 | 6.9384 | 0.8954 |
Decision Tree Regressor | 5.9400 | 65.2236 | 8.0390 | 0.8553 |
Model | MAE | MSE | RMSE | |
---|---|---|---|---|
Ensemble Model | 0.7908 | 1.1329 | 1.0515 | 0.9018 |
Gradient Boosting Regressor | 0.7953 | 1.1481 | 1.0582 | 0.9006 |
Light Gradient Boosting Machine | 0.7966 | 1.1508 | 1.0595 | 0.9004 |
CatBoost Regressor | 0.7983 | 1.1554 | 1.0614 | 0.9001 |
Extreme Gradient Boosting | 0.8107 | 1.1893 | 1.0771 | 0.8971 |
Orthogonal Matching Pursuit | 0.8199 | 1.2034 | 1.0840 | 0.8956 |
Random Forest Regressor | 0.8248 | 1.2200 | 1.0922 | 0.8942 |
Extra Trees Regressor | 0.8301 | 1.2326 | 1.0982 | 0.8931 |
K Neighbors Regressor | 0.8793 | 1.3646 | 1.1573 | 0.8815 |
AdaBoost Regressor | 0.8961 | 1.4137 | 1.1727 | 0.8787 |
Decision Tree Regressor | 1.1877 | 2.4335 | 1.5515 | 0.7865 |
Model | MAE | MSE | RMSE | |
---|---|---|---|---|
Ensemble Model | 1.2515 | 2.8038 | 1.6591 | 0.8205 |
Light Gradient Boosting Machine | 1.2618 | 2.8418 | 1.6694 | 0.8176 |
Gradient Boosting Regressor | 1.2678 | 2.8478 | 1.6725 | 0.8175 |
CatBoost Regressor | 1.2624 | 2.8501 | 1.6716 | 0.8171 |
Extreme Gradient Boosting | 1.2878 | 2.9636 | 1.7031 | 0.8095 |
Random Forest Regressor | 1.3031 | 3.0114 | 1.7195 | 0.8071 |
Extra Trees Regressor | 1.3116 | 3.0473 | 1.7298 | 0.8048 |
Orthogonal Matching Pursuit | 1.3240 | 3.1079 | 1.7519 | 0.8016 |
K Neighbors Regressor | 1.3811 | 3.3403 | 1.8128 | 0.7865 |
AdaBoost Regressor | 1.4331 | 3.3870 | 1.8281 | 0.7841 |
Decision Tree Regressor | 1.8775 | 6.1235 | 2.4593 | 0.6098 |
Model | MAE | MSE | RMSE | |
---|---|---|---|---|
Ensemble Model | 0.2142 | 0.1681 | 0.4100 | 0.7733 |
CatBoost Regressor | 0.2150 | 0.1691 | 0.4112 | 0.7719 |
Light Gradient Boosting Machine | 0.2146 | 0.1695 | 0.4117 | 0.7714 |
Gradient Boosting Regressor | 0.2221 | 0.1752 | 0.4185 | 0.7638 |
Extreme Gradient Boosting | 0.2178 | 0.1752 | 0.4185 | 0.7638 |
Random Forest Regressor | 0.2212 | 0.1797 | 0.4238 | 0.7578 |
Extra Trees Regressor | 0.2246 | 0.1851 | 0.4302 | 0.7504 |
K Neighbors Regressor | 0.2316 | 0.2022 | 0.4496 | 0.7272 |
AdaBoost Regressor | 0.3803 | 0.2851 | 0.5325 | 0.6147 |
Orthogonal Matching Pursuit | 0.4127 | 0.3336 | 0.5775 | 0.5502 |
Decision Tree Regressor | 0.2910 | 0.3452 | 0.5875 | 0.5343 |
Authors | Model | Parameter | MAE | RMSE |
---|---|---|---|---|
Our results | Relative Humidity | 0.1873 | 0.1369 | |
Ensemble Model | Minimum Temperature | 0.1881 | 0.1429 | |
Maximum Temperature | 0.1898 | 0.144 | ||
Rainfall | 0.2987 | 0.1787 | ||
[20] | Random Forest | 4.49 | 8.82 | |
Multivariate Linear Regression | Rainfall | 4.97 | 8.61 | |
XGBoost | 3.58 | 7.85 | ||
[33] | XGBoost | Rainfall | 8.8 | 2.7 |
[34] | Linear Regression | Maximum Temperature | 3.10 | 1.78 |
[35] | Indoor Air Temperature | 0.3535 | 0.476 | |
Random Forest | Indoor Relative Humidity | 1.47 | 2.429 |
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Nyasulu, C.; Diattara, A.; Traore, A.; Deme, A.; Ba, C. Towards Resilient Agriculture to Hostile Climate Change in the Sahel Region: A Case Study of Machine Learning-Based Weather Prediction in Senegal. Agriculture 2022, 12, 1473. https://doi.org/10.3390/agriculture12091473
Nyasulu C, Diattara A, Traore A, Deme A, Ba C. Towards Resilient Agriculture to Hostile Climate Change in the Sahel Region: A Case Study of Machine Learning-Based Weather Prediction in Senegal. Agriculture. 2022; 12(9):1473. https://doi.org/10.3390/agriculture12091473
Chicago/Turabian StyleNyasulu, Chimango, Awa Diattara, Assitan Traore, Abdoulaye Deme, and Cheikh Ba. 2022. "Towards Resilient Agriculture to Hostile Climate Change in the Sahel Region: A Case Study of Machine Learning-Based Weather Prediction in Senegal" Agriculture 12, no. 9: 1473. https://doi.org/10.3390/agriculture12091473
APA StyleNyasulu, C., Diattara, A., Traore, A., Deme, A., & Ba, C. (2022). Towards Resilient Agriculture to Hostile Climate Change in the Sahel Region: A Case Study of Machine Learning-Based Weather Prediction in Senegal. Agriculture, 12(9), 1473. https://doi.org/10.3390/agriculture12091473