Application of Transformer-Based Deep Learning Models for Predicting the Suitability of Water for Agricultural Purposes

Water is the most vital component for the sustainability of living beings on Earth. From plants to human beings, every single living being on Earth needs water for its survival. In this research, a novel model has been developed in order to predict the suitability of water for agricultural purposes. This research developed the ALBERT Base v2 model for detecting water quality and suitability and a model named the ALBERT Water Potability Detection (ALBERT-WPD) model, customized from the ALBERT Base v2 transformer model. The model was tested using a dataset from Kaggle, and the performance was evaluated. The research used ten parameters. The performance of both models was measured using metrics, accuracy, precision, recall, and F1-score. In this research, traditional models (CNN and RNN) were developed and compared against the ALBERT model to measure its performance and its efficiency in water potability prediction. The findings revealed that the ALBERT models gained higher accuracies than the traditional models: the Base v2 model gained 91% and the altered ALBERT-WPD rendered 96% accuracy. The classification results (precision, recall, and F1-score) obtained for the ALBERT-WPD model for the potability class were 93%, 98%, and 96% and those for the non-potability class were 98%, 95%, and 96%, respectively. The study found that using transformer models for water potability detection procures higher accuracy with the model optimization method. The study concludes that using transformer models (BERT-based) in water potability detection procures higher accuracy (>95%) with fewer parameters in comparison with traditional models (CNN and RNN) which utilize more parameters. The findings show that the transformer models exhibit rapid data processing and handle large datasets efficiently; the handling of such datasets is complicated when using traditional models, as they have vanishing gradient and encounter temporal data loss challenges. Thus, the significance of the proposed research dwells within the use of “transformers” as an advanced machine learning model to predict water potability and quality, showing that transformers are the future of machine learning.