Comparative study of random forest and Decision Trees in the modeling of methylene blue adsorption

Globally, there is an increasing issue of freshwater shortage due to population growth and industrial activities [1]. This problem is worsened by the release of contaminated water into water bodies by various industries. To tackle this challenge, it is important to effectively treat and reuse polluted water [2]. Adsorption is a promising method for wastewater treatment because it is simple and operates under mild conditions. However, the success of this process relies on the performance of the adsorbents used, technically and economically [3]. In this study, we have developed and applied a new adsorbent for the removal of methylene blue (MB) from contaminated water samples. The adsorption data collected were analyzed, and Decision Trees (DT) and Random Forest (RF) models were used to simulate and predict the efficiency of the adsorption process. In conclusion, this study highlights the potential of the synthesized chitosan-graphene oxide-clay adsorbent for removing MB from contaminated water samples. The developed DT and RF models provide reliable tools for simulating and predicting the adsorption process, facilitating the optimization and cost-effective implementation of this treatment method. Further research in this field will continue to advance our understanding and application of adsorption processes, ultimately leading to a cleaner and more accessible water supply for everyone.