Ensemble IDO Method for Outlier Detection and N2O Emission Prediction in Agriculture

Abstract

Nitrous oxide (N2O) emissions from agricultural activities significantly contribute to climate change, necessitating accurate predictive models to inform mitigation strategies. This study proposes an ensemble framework combining Isolation Forest, DBSCAN, and One-Class SVM to enhance outlier detection in N2O emission datasets. The dataset, consisting of 2, 246 rows and 21 columns, was preprocessed to address missing values and normalize data. Outlier detection was performed using each method individually, followed by integration through hard and soft voting techniques. The results revealed that Isolation Forest identified 113 outliers, DBSCAN detected 1, 801, and One-Class SVM found 118. Hard voting identified 165 outliers, while soft voting detected 734, ensuring a refined dataset for subsequent modeling. The ensemble approach improved the accuracy of the XGBoost model for N2O emission prediction. The best results were obtained using the Random Search Cross Validation hyperparameter tuning, with a test size is 20%, achieving a CV MSE of 0.0215, MSE of 0.0144, RMSE of 0.1200, MAE of 0.0723, and an R2 of 0.6750. This study demonstrates the effectiveness of combining multiple outlier detection methods to enhance data quality and model performance, supporting more reliable predictions of N2O emissions.