Analysis of Biomass Sustainability Indicators from a Machine Learning Perspective
This work addresses the need for accurate real-time assessment of sustainability indicators for the biomass community, but it is incremental as it applies existing methods to a specific dataset.
The study tackled the problem of predicting biomass sustainability indicators by evaluating ten machine learning models on crop residue data, finding that Random Forest performed best in estimating soil erosion factor, soil conditioning index, and organic matter factor.
Plant biomass estimation is critical due to the variability of different environmental factors and crop management practices associated with it. The assessment is largely impacted by the accurate prediction of different environmental sustainability indicators. A robust model to predict sustainability indicators is a must for the biomass community. This study proposes a robust model for biomass sustainability prediction by analyzing sustainability indicators using machine learning models. The prospect of ensemble learning was also investigated to analyze the regression problem. All experiments were carried out on a crop residue data from the Ohio state. Ten machine learning models, namely, linear regression, ridge regression, multilayer perceptron, k-nearest neighbors, support vector machine, decision tree, gradient boosting, random forest, stacking and voting, were analyzed to estimate three biomass sustainability indicators, namely soil erosion factor, soil conditioning index, and organic matter factor. The performance of the model was assessed using cross-correlation (R2), root mean squared error and mean absolute error metrics. The results showed that Random Forest was the best performing model to assess sustainability indicators. The analyzed model can now serve as a guide for assessing sustainability indicators in real time.