Water Analysis of Different Water Bodies of Lake of Bangladesh
This repository contains the code and data related to the water analysis of different water bodies of Lake in Bangladesh. The project aims to analyze the water quality and different parameters using an IoT device and sensors data. The repository also includes machine learning codes, visualizations, and statistical analysis of the water to draw different inferences and decisions. Background
Bangladesh is known for its vast water resources, including rivers, lakes, and wetlands. However, these water bodies are under severe threat due to various human activities, including pollution, climate change, and overexploitation. The deterioration of water quality has adverse effects on human health and the environment. Therefore, it is essential to monitor the water quality of these water bodies continuously.
To address this issue, we developed an IoT device with sensors that can measure various water quality parameters such as pH, dissolved oxygen, temperature, and turbidity. We installed these devices in different water bodies of the Lake in Bangladesh and collected data over time. Data
The data collected from the IoT devices are available in the data/ directory. The data are stored in CSV format, with each file containing the data from a specific sensor in a specific water body. The data include the timestamp, pH, dissolved oxygen, temperature, and turbidity. Analysis
The analysis of the water data is divided into three parts: data preprocessing, machine learning, and visualization. Data Preprocessing
The first step in analyzing the data is to preprocess the data. This involves cleaning the data, handling missing values, and converting the data into a format that can be used for machine learning. The data preprocessing code is available in the preprocessing/ directory. Machine Learning
The second step is to develop machine learning models to predict the water quality parameters based on the sensor data. We used different algorithms such as Random Forest, Support Vector Machines, and Neural Networks to develop these models. The machine learning code is available in the machine_learning/ directory. Visualization and Statistical Analysis
The third step is to visualize the data and draw different inferences and decisions. We used different visualization tools such as Matplotlib and Seaborn to plot the data and draw different conclusions. The statistical analysis code is available in the visualization/ directory. Conclusion
The analysis of the water quality parameters using IoT devices and machine learning can provide valuable insights into the water quality of different water bodies in Bangladesh. The analysis can help in identifying the sources of pollution and taking appropriate actions to improve the water quality. The code and data in this repository can be used as a reference for similar projects in other regions.