School of Mechanical Engineering Seminar
Wednesday, July 10, 2024 at 14:00
Wolfson Building of Mechanical Engineering, Room 206
Classifying High Fluoride Concentrations in Water Based on Data From India's Central Ground Water Board.
Arseni Riabkov
This work was carried out under the supervision of
Dr. Hadas Maman
&
Mr. Asaf Pras
Fluoride is a naturally occurring element that is found in soil, water, and air. While small concentrations of fluoride are beneficial for dental health, excessive exposure to fluoride in the form of ingestion or inhalation can lead to a variety of health problems, including skeletal and dental fluorosis. Click or tap here to enter text. It is estimated that the global total for people affected by Dental fluorosis alone may exceed 70 million. The World Health Organization (WHO) noted that dental fluorosis is associated with fluoride levels in drinking water above 1.5 mg/L and recommends a fluoride concentration of no more than 1.5 mg/L in drinking water as a level at which dental fluorosis should be minimal. Even so, it is important to note that The 1.5 mg guideline value of WHO is not a “fixed” value but is intended to be adapted according to local conditions.
Traditional methods for monitoring fluoride levels in groundwater typically involve various techniques. These range from electrochemical approaches to colorimetric methodologies, which can include naked-eye detection or spectrophotometric measurements. Some of these analytical techniques require manual sample collection and laboratory analysis, which can be time-consuming, and costly. And on top of that may require specialized equipment and expertise. In contrast, machine learning techniques can leverage available data to develop predictive models to estimate groundwater fluoride levels.
In recent years, there has been an increasing interest in using machine learning (ML) and artificial intelligence (AI) techniques to predict fluoride levels in groundwater. These methods have the potential to provide valuable information about fluoride concentrations in areas where data is limited or difficult to obtain. One of the main benefits of using ML and AI techniques for this purpose is their ability to analyze large amounts of data and identify patterns that may not be immediately obvious to human analysts. In particular, ML algorithms such as neural networks and decision trees are effective at identifying complex relationships between different variables.
The ML models in this study were trained Primarily on The India Central Ground Water Board data set, which covers the years 2000 to 2018. And contains more than 150,000 rows of information from a total of about 18,000 groundwater wells which include information on fluoride concentration, PH, Electric Conductivity, Nitrate, Bicarbonate, and Calcium. Additionally, Calcicol concentrations in the ground and precipitation data were added. By training the ML models on this data set, we developed 3 different models that could predict high fluoride levels in groundwater with similar performance. A Random Forest Model, an ADA-boosted Decision tree, and a Multi-layer perceptron model which had the best performance with an accuracy score of 0.78 and a recall score of 0.76.
Using these ML models, it is possible to identify where the fluoride concentration exceeds the WHO-recommended levels and take necessary actions to mitigate the effects of fluoride on human health. Additionally, these models can help to identify potential sources of fluoride in the groundwater and assist in the development of strategies to reduce fluoride levels. Overall, the use of ML and AI techniques for predicting fluoride levels in groundwater can provide valuable information to help protect public health and support sustainable water management.
