Design of efficient VARMA autoencoder-based predictive model for analyzing presence and concentration of microplastics in wastewater.

Due to their detrimental effects on the environment and human health, the rising amount of microplastics in wastewater has become a growing concern. To address this problem, a predictive model is required to analyze the presence and concentration of microplastics in wastewater with precision. This paper proposes the development of a VARMA (Vector Autoregressive Moving Average) autoencoder-based predictive model. To extract meaningful features from the input data, the model employs autoencoders, a type of neural network that can learn and compress complex data patterns. The VARMA model is then used to capture the dynamic relationship between the extracted features and the output variables of interest, namely the presence and concentration of microplastics in wastewater. The proposed model has multiple applications, including providing insights into the efficacy of various wastewater treatment methods in removing microplastics, predicting the potential sources of microplastics in wastewater, and assisting in the development of strategies to reduce the total amount of microplastics in the environment. The model proposed in this work has several advantages over existing models, including its ability to handle non-linear relationships and its efficiency in processing large datasets. In addition, the VARMA autoencoder-based model's capacity to capture dynamic relationships between features and target variables makes it ideal for analyzing the presence and concentration of microplastics in wastewater, where the data is frequently complex and nonlinear. [ABSTRACT FROM AUTHOR]