Water Quality Potability

Keywords: Water, Quality

Background

As a basic human right and an essential component of effective health protection measures, access to clean drinking water is essential for preserving good health. Given its effects on development and health, this issue is significant on a national, regional, and local level. Investments in water supply and sanitation have been shown to have a positive economic impact in some places. This is true because the savings in unfavorable health consequences and healthcare expenditures surpass the costs of carrying out interventions. The project's goal was to predict water quality using deep learning methods, specifically an Artificial Neural Network (ANN) model. Using TensorFlow or Keras' Sequential API, the ANN model was created. Numerous factors, including pH, hardness, solids, chloramines, sulfate, and others, affect the quality of water. The mobility of water was the aim column in this instance. The ANN model's architecture had four hidden layers.

Aim & Objectives

Aim
To Predict the Water Quality Potability Using DL Techniques
Objective
Develop an effective Artificial Neural Network (ANN) model to predict water quality based on diverse features like pH, hardness, and chloramines, with the goal of enhancing water safety and accessibility. The project aims to showcase the potential of deep learning techniques, specifically ANN models, in accurately categorizing and forecasting water quality, contributing to real-world applications in water regulation and monitoring to ensure clean and safe drinking water for all.

Research Methodology

ANN Technique used

Software & Tools

Jupyter, Google Collab

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