## 4 Bigram and Traditional Results
### Assessing Water Quality and Potability
#### 4.2.2 Traditional Analysis
In [51], the authors investigate the application of FEYN and Q-Lattice machine learning methods for data classification in water potability. By integrating these techniques with various datasets, the researchers attained a 68% accuracy level in classifying water potability, showcasing the efficacy of this approach. Neural networks with memory, Long Short-Term Memory (LSTM), were used in [65] to create a deep learning water potability classification model for rural water sources in the Philippines. The model achieved a high accuracy of 99.80% using MLP, while LSTM performed better in accuracy and recall compared to GRU, GRU had better precision than LSTM. The quality of the river Ganges was determined in the study referenced in [66]. Several parameters were used to classify the water using various classification algorithms, including Random Forest, a supervised machine learning algorithm. This model achieved an accuracy of around 99 %, significantly higher than other methods for predicting water quality. In [50], an Internet of Things framework was developed for water quality monitoring and potability classification using portable sensor nodes. The system achieved an accuracy of 93.33% compared to conventional industrial water laboratory tests, and it is capable of communicating the water potability status to households with minimal delay. A consumption model was developed in [67]. The model, developed in Python, utilizes a dataset from Kaggle and the MinMax scaling method from the Sklearn library. The Keras library aids in implementing the seven dense layers in the neural network. The resulting model achieves around 70% accuracy. In [68], the authors employ Artificial Intelligence, specifically a convolutional neural network (CNN), to predict water quality more efficiently than traditional methods. The CNN outperforms five other machine learning algorithms, achieving 97% accuracy in predicting water potability based on eight water property parameters. Additionally, the CNN reduces classification processing time, demonstrating its effectiveness in maintaining water quality balance and protecting human health and the ecosystem. A machine learning method to detect potable-reclaimed water pipe cross-connection events using multiple water quality parameters is explored in [62]. A Pearson correlation coefficient (PCC)-SVM-based method was developed, providing high detection performance and reliability, even during abnormal readings. Detection accuracy exceeded 88%, with a false positive rate below 12%, demonstrating the method's practical applicability for early warning systems. In [54], a CNN along with a processing pipeline is utilized to tackle the challenge of detecting toxin-producing phytoplankton in water, surpassing the constraints of conventional techniques. The authors put forward a cutting-edge, fully automated approach that employs digital microscopy and machine learning for the analysis of multi-specimen images. This system adeptly detects, segments, and classifies phytoplankton, providing a reliable and consistent instrument for specialists to assess water quality and potability. In [59], the authors use Random Forest and multiple spatial environment factors to model groundwater nitrate pollution in Yinchuan Region. Remote sensing and GIS methods were employed to create training and test sets. Model performance was assessed using MAE, RMSE, and \(R2\), with positive results. Key factors influencing nitrate levels included proximity to the Yellow River, meteorological elements, water level elevation, urban land, and arable land. The study offers an integrated approach to groundwaterquality analysis and sustainable management in the Yinchuan Region. The development of more accurate machine learning models for predicting irrigation parameters and water quality is studied in [60]. LSTM, MLR, and ANN models are used. These models predict six irrigation water quality parameters and are validated using MSE, correlation coefficients, RMSE, and MAE.