Muzahidul Islam, Professor in the Department of Computer Science & Engineering, for his excellent supervision, direction, motivation and support in making this project possible. Water is one of the most important natural resources that nature has given to mankind.
Present State of the Problem
Motivation
Study Region
Challenges
Objectives
Our Contribution
We have also designed an IoT-based architecture where we have considered five water parameters such as temperature, turbidity, pH, conductivity and TDS to investigate and read the water quality live and also collect real-time data by a centralized cloud-based system which is ThingSpeak. The uniqueness of our work is that we have combined three different fields to monitor water quality and also get real-time data without human interaction, which is cost-effective and time-efficient.
Background
Literature Review
The limitation of this research is based on the analysis where they examined the water quality based on the satellite image. 27] based on IoT for monitoring water quality and assessing whether the water was clean or not. This article [32] used chemical sensors to monitor water quality that provides a real-time critical assessment of the applicability of different technologies.
The research gap of this article is to examine less sensor data for water quality monitoring. They discussed the different types of water sensors to measure water quality that can provide real-time data to identify water situations. The main purpose of this article was to check the water quality status by validating the efficiency of the system and generalizing the prediction and evaluation model.
To monitor water quality providing a critical evaluation of the suitability of different approaches in real-time. Urban river water quality monitoring based on self-optimizing machine learning method using multi-source remote sensing data. To check the water quality status by proving the efficiency of the system and generalizing the prediction and evaluation model.
Sensor Layer
Here we used the SEN0244 TDS sensor to determine solid particles in water. The concentration of free ions in water conducts electricity, so the electrical conductivity of water depends on the concentration of ions [40]. It analyzes light transmittance and reflectance, which vary with the total suspended solids (TSS) content of the water, to detect suspended particles present in the water.
It will act as a web server, allowing any Wi-Fi enabled device to interact with the board and control its pins wirelessly. Its low cost, Wi-Fi capability, and full compatibility with the Arduino platform make the D1 Mini very versatile [42]. First, we designed our IoT devices based on the Arduino IDE platform for real-time data acquisition.
As mentioned earlier, we have a DS18B20 sensor for temperature, a SEN0161 sensor for pH level, a SEN0244 sensor for TDS and conductivity, and a SEN0189 sensor for water turbidity. These sensors are connected to the D1 mini, which is a kind of Arduino board with built-in Wi-Fi. As we can see, the esp8266 device that serves as the core microcontroller of the system we proposed has wi-fi capable of IEEE 802.11.
Network Layer
Data Processing Layer
Previous data collection: First, we collected 3180 data from the WHO ideal water data with five water parameters such as temperature, TDS, conductivity, pH and turbidity which are the independent variables, and transmissibility of water is the dependent variable. After collecting the previous data, we preprocessed our datasets by removing noise and outliers. For that reason, we have collected 200 satellite images in the different parts of Chittagong, such as the Karnafuli River, the Halda River, the lakes of Rangamati and the nearest area of the Bay of Bengal.
We have jointly taken satellite map data of Landsat-8 and Sentinel-2 satellites as our image dataset from EarthExplorer (https://earthexplorer.usgs.gov/). After selecting our study area, we have cropped these images using a window and also removed unnecessary information and taken our sample area as input image to assess the water quality, which is shown in Figure 3.8. After collecting image data, we have pre-processed the data and built a model on dataset to classify the images and extract knowledge from these images.
DL model: For image classification, we have applied the Deep Learning technique such as the CNN model to our image data. We have chosen CNN to classify the image data because it has a deep network architecture with weighted layers, which does so. Here we have used sigmoid and ReLU function as activation function based on our system.
Application Layer
Our Project discovers facts and factors that we made a plan of action to framework of implementation in the system with prior analysis of probability of all aspect and this research has good findings with results that can accept as final output of this research.
IoT Result
Both sensors are analog sensors and the D1 mini also does not have a second analog pin. In the table above, we can see the connection of each sensor and transducer to the D1 mini and also to each other. In the last two tables, we can see three device connections for one function, which is required since we used the ads1115 as an analog-to-digital converter.
In the first two tables, these converters are not needed and the sensor interface connection is one-to-one with the D1 mini and the second sensor. First, we have implemented the system with a sample water in a small plastic mug to test the performance of the sensors and other devices, which is shown in Figure 4.2. An open source IoT application called ThingSpeak can store and retrieve data from sensors across a LAN or the Internet using an HTTP server.
For this purpose, we have used the ThingSpeak server as a central server to store our sensor data, analyze the streaming data in real time, communicate with the data and access the data using the API key . To use the data in a software environment for past data analysis, the data collection method also allows data manipulation. Temperature", field 2 represents "pH", field 3 represents "solids", field 4 represents "conductivity", and field 5 represents "turbidity".
Analysis of Past Data
Then we have removed all missing data and outliers from our data sets so that the model is not disturbed by unwanted noise. After performing the filtering process, we have 2751 data with five independent variables and one dependent variable where our actual data was 3180 which is shown in figure 4.7. We have developed our classification model on the WHO ideal water data to show the statistical picture of our data after pre-processing where figure 4.8 shows the bar graph of the target values.
We have also found the outliers for all individual independent variables such as temperature, pH, TDS, conductivity and turbidity using a box plot which is shown in Figure 4.9. Then we have applied several machine learning algorithms such as Decision Tree Classification, Random Forest Classification, Extra Tree Classification and KNN classification to the model building where we have achieved a good accuracy which is almost. We have got accuracy from Decision Tree Classification is 85.29%, Random Forest Classification is 85.66%, Extra Tree Classification is 84.75% and KNN Classification is 79.85%.
From the accuracy results of the above model, we can see that random forest classification gives good accuracy than other classification models, which is shown in Figure 4.11. From past data analyses, we identified the best machine learning model for our datasets, which is random forest classification. Then we get the latest IoT data from ThingS-peak into Google Colab using the read API key and test the data in the best model, which is a Random Forest classifier to show the water quality category of the latest IoT data.
Satellite Image Processing
After splitting data, we applied deep learning techniques like CNN (Convolutional Neural Network) model to classify the images and extract knowledge from those images. To evaluate the effectiveness of the model, we trained our model for 10 periods and also passed the validation data we generated earlier. In figure 4.16 we can observe that the training loss has been reduced to 0.1 and the validation loss has been reduced to 0.2.
Here too, we load and process the image data separately and predict these images using our training model. We then determined the probability of target images showing water or sea accuracy.
Web Interface
Discussion
Our main goal is to remotely monitor water quality to determine if the water is safe for human consumption and domestic use. We have also applied data analytical techniques on previous ideal water data formulated by WHO to investigate water quality by considering some water parameters. Donato et al., “Drinking water insecurity: water quality and access in coastal south-western Bangladesh,” International journal of Environmental Health Research, vol.
Sagar, ‘Water quality monitoring system Using iot’, in 2018 Fourth International Conference on Advances in Electricity, Electronics, Information, Communication and Bioinformatics (AEEICB). Rodriguez, “Strategies for Water Quality Monitoring – An Overview and Future Perspectives,” Science of the Total Environment, vol. Jain, “Real-time water quality monitoring system using internet of things,” in 2017 International Conference on Computer, Communications and Electronics (Comptelix).
Marques, “Internet of Things for water quality monitoring and assessment: a comprehensive review,” Artificial Intelligence for Sustainable Development: Theory, Practice and Future Applications, pp. Yin, “Analysis and prediction of water quality using lstm deep neural networks in an IoT environment,” Sustainability, vol. Wardana et al., “Real-time monitoring system of drinking water quality using internet of things,” in 2022 International Electronics Symposium (IES).
