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IoT and Satellite Image Driven Water Quality Monitoring and Assessment Method in Coastal Region

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Evolutionary Artificial Intelligence (ICEASSM 2017)

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

Water is a gift of creator and fundamental resource of human life, agriculture, and industry. As a result of the combined impacts of growing population, climatic changes, pollution, high financial demands, and excessive salt extraction from the sea, safe water is becoming threat to get from natural resource. Some unambiguous and comprehensive coordination of planning and sustainable action must take to ensure access to freshwater for urban and rural people. So, it must have a coordinated and sustainable system in place to periodically check on water purity. Monitoring water quality is essential, especially for remote areas. This paper leads to monitor and identify water quality in real time without human interaction to assess whether the water is drinkable and suitable for domestic usage. In this research, we have designed an IoT-driven system that consists of five water parameter sensors such as temperature, EC (electric conductivity), pH, turbidity, and TDS (total dissolved solids) for assessing the water quality. The obtained IoT sensor data is sent to the cloud server so that we can monitor the water quality in real time. We have also collected 200 satellite map data of both Sentinel-2 and Landsat-8 satellites jointly from EarthExplorer to characterize the water quality in two categories such as freshwater and seawater. So, we have applied the Deep Learning method CNN (convolutional neural network) for the model built and predict the categories of water individually based on the map area. For analyzing the past data, we have taken 3180 ideal water data from the WHO to examine the water quality by considering five water parameters. Then we have built our model by applying some machine learning algorithms such as Decision Tree, Random Forest, Extra Tree, and K-NN classification to find good accuracy which are 85.29%, 85.66%, 84.75%, and 79.85%, respectively. Following that, we cross-match the results depending on location by comparing the satellite data with the IoT real-time data.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, United International University, Dhaka, Bangladesh

    Jasrin Shiddike, Afsana Ahmed, Mohammad Farshid & A. K. M. Muzahidul Islam

Authors
  1. Jasrin Shiddike
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  2. Afsana Ahmed
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  3. Mohammad Farshid
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  4. A. K. M. Muzahidul Islam
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Corresponding author

Correspondence to Jasrin Shiddike .

Editor information

Editors and Affiliations

  1. Faculty of Innovation and Technology, Taylor’s University, Subang Jaya, Selangor, Malaysia

    David Asirvatham

  2. University of Southeast Norway, Notodden, Norway

    Francisco M. Gonzalez-Longatt

  3. Gdansk University of Technology, Gdańsk, Poland

    Przemyslaw Falkowski-Gilski

  4. Professor of Computer Engineering, Papua New Guinea University of Technology, Lae, Papua New Guinea

    R. Kanthavel

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Shiddike, J., Ahmed, A., Farshid, M., Muzahidul Islam, A.K.M. (2024). IoT and Satellite Image Driven Water Quality Monitoring and Assessment Method in Coastal Region. In: Asirvatham, D., Gonzalez-Longatt, F.M., Falkowski-Gilski, P., Kanthavel, R. (eds) Evolutionary Artificial Intelligence. ICEASSM 2017. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-8438-1_3

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