| dc.description.abstract | The impact of climate change is increasingly severe, particularly in the agricultural sector. Soil moisture, a key factor in plant growth and agricultural yield, also plays a significant role in the stability of land ecosystems and the sustainable use of water resources. Therefore, enhancing the accuracy of soil moisture prediction trends is crucial for informed agricultural decision-making. This study, utilizing the Random Forest method in machine learning, aims to predict soil moisture changes at depths of 10 cm and 20 cm. By leveraging historical soil moisture data from the Wufeng Agricultural Research Station in Taichung, along with other environmental variables such as hourly rainfall and cumulative rainfall, a Random Forest model was meticulously constructed. The model training process involved determining optimal parameter combinations, such as training days and prediction days, through a careful process of manual trial and error, ensuring the reliability of the study′s findings.
The prediction results for this site indicate that cumulative rainfall has the greatest impact on the model. Whether considering the entire data period, only the rainy season, or fitting a curve, it can be observed that at depths of 10 cm and 20 cm, predictions are more accurate when the cumulative rainfall period is 6 to 8 days. The exception is at a depth of 10 cm during the rainy season, where an optimal rainfall period could not be determined. When considering the entire data period at depths of 10 cm and 20 cm, choosing the inflection point of the decline as the optimal cumulative days, the MAPE (%) values are 25.18 and 5.13, respectively. During the rainy season, at a depth of 20 cm, the MAPE (%) is 6.63. At 10 cm and 20 cm depths, the prediction RMSE (%) values are 2.37 and 2.03 for the appropriate fitting range concerning the difference between the training and predicting results, respectively.
Future research could consider adding more meteorological variables, comparing the results of the Random Forest model with hydrological and physical models, or further exploring applications during drought periods to improve prediction accuracy. This would provide better data for agricultural decision-makers to reference. | en_US |