Nguyen, Sharma, et al (2024) Improving the prediction of biocharBiochar is a carbon-rich material created from biomass decomposition in low-oxygen conditions. It has important applications in environmental remediation, soil improvement, agriculture, carbon sequestration, energy storage, and sustainable materials, promoting efficiency and reducing waste in various contexts while addressing climate change challenges. More production from various biomassBiomass is a complex biological organic or non-organic solid product derived from living or recently living organism and available naturally. Various types of wastes such as animal manure, waste paper, sludge and many industrial wastes are also treated as biomass because like natural biomass these More sources through the implementation of eXplainable machine learning approaches. International Journal of Clean Energy. https://doi.org/10.1080/15435075.2024.2326076

In a study exploring the possibilities of explainable machine learning techniques, researchers investigate biochar production prediction, an area experiencing rapid growth. The paper demonstrates advancements in sensitivity analysis methodology, optimization of training hyperparameters, and ensemble techniques, simplifying and enhancing forecasting of biochar output and composition from various biomass sources.

The study argues for the importance of white-box models, which offer transparency and comprehensibility, in contrast to the increasing suspicion surrounding black-box models. These explainable AI systems not only ensure accurate forecasts but also provide detailed explanations of the mechanisms generating the outcomes.

For prediction models to gain confidence and for biochar production processes to enable informed decision-making, the emphasis must be on interpretability and openness. The paper synthesizes critical features of biochar prediction through a rigorous assessment of current literature and the authors’ own experience.

Explainable machine learning techniques are advocated for their potential to encourage ecologically responsible decision-making by improving forecast accuracy and transparency. Biochar is positioned as a participant in solving global concerns related to soil health and climate change, contributing to environmental sustainability and renewable energy consumption.

By promoting the adoption of explainable ML techniques, the study aims to propel the field towards a more transparent, informed, and sustainable future.