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Spatio-temporal Analysis and Modeling of Coastal areas for Water Salinity Prediction

S. B.,R. Priyadarshini,Shrutilipi Bhattacharjee,S. S.,P. U,Soumyasri Ghosh

Published 2023 in 2023 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

ABSTRACT

Salinity is an important parameter affecting the quality of water, and excessive amounts adversely affect vege-tation growth and aquatic organism populations. Natural factors like tidal waves, natural calamities etc., and man-made factors like unchecked disposal of industrial wastes, domestic/ urban sewage, and fish hatchery activities can cause significant increases in water salinity. In this article, an approach that utilizes multimodal data like Landsat 8 optical observations and the SMAP salinity data product for predicting water salinity indices in the coastal region is proposed. Machine Learning models such as K-nearest neighbor (KNN), Gradient Boost (GB), Extremely Randomized Tree (ERT), Random Forest Regression (RFR), Decision Tree (DT), Multiple Linear Regression (MLR), Lasso Regression (LR), and Ridge Regression (RR) are used for salinity prediction. Empirical experiments revealed that the ERT model outperformed other ML models, with a $R^{2}$ of 0.92 and RMSE of 0.25 psu.

PUBLICATION RECORD

  • Publication year

    2023

  • Venue

    2023 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

  • Publication date

    2023-02-18

  • Fields of study

    Not labeled

  • Identifiers
    DOI 10.1109/SCEECS57921.2023.10062985
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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