Machine Learning Method for Predicting the Depth of Shallow Lakes Using Multi-Band Remote Sensing Images

Document Type: Regular Article

Authors

1 M.Sc. Student in Geographic Information Systems, Department of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

2 Assistant Professor in Surveying Engineering, Department of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

Abstract

Knowing the lake’s characteristics information such as depth is an essential requirement for the water managers; however, conducting a comprehensive bathymetric survey is considered as a difficult task. After the advent of remote sensing, and satellite imagery, it has been recognized that water depth can be estimated in some way over shallow water. There are many models that can evaluate relationships between multi-band images, and depth measurements; however, artificial computation methods can be used as an approximation tool for this issue. They are also considered as fairly simple and practical models to estimate depth in shallow waters. In this paper, different methods of artificial computation are used to calculate the depth of shallow lake, then these methods are compared. The results show that Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS), and regression learner are best methods for this issue with RMSE 0.8, 1.47, and 0.96 respectively.

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Main Subjects

References

1.           Cazenave, A., Satellite Altimetry☆, in Reference Module in Earth Systems and Environmental Sciences. 2018, Elsevier.

2.           Ceyhun, Ö. and A. Yalçın, Remote sensing of water depths in shallow waters via artificial neural networks. Estuarine, Coastal and Shelf Science, 2010. 89(1): p. 89-96.

3.           Elsahabi, M., O. Makboul, and A. Negm, Lake Nubia sediment capacity estimation based on satellite remotely sensed detected bathymetry. Procedia Manufacturing, 2018. 22: p. 567-574.

4.           Lyzenga, D.R., Passive remote sensing techniques for mapping water depth and bottom features. Applied Optics, 1978. 17(3): p. 379-383.

5.           Dörnhöfer, K. and N. Oppelt, Remote sensing for lake research and monitoring – Recent advances. Ecological Indicators, 2016. 64: p. 105-122.

6.           https://oceanservice.noaa.gov/facts/bathyuses.html.

7.           https://sealevel.jpl.nasa.gov/overview/oceansurfacetopo/.

8.           Duan, Z. and W.G.M. Bastiaanssen, Estimating water volume variations in lakes and reservoirs from four operational satellite altimetry databases and satellite imagery data. Remote Sensing of Environment, 2013. 134: p. 403-416.

9.           http://www2.csr.utexas.edu/grace/.

10.         https://swot.jpl.nasa.gov/home.htm.

11.         Calmant, S., F. Seyler, and J.F.J.S.i.g. Cretaux, Monitoring continental surface waters by satellite altimetry. 2008. 29(4-5): p. 247-269.

12.         Li, C., et al., Multi-band remote sensing based retrieval model and 3D analysis of water depth in Hulun Lake, China. Mathematical and Computer Modelling, 2013. 58(3): p. 771-781.

13.         Jay, S. and M. Guillaume, A novel maximum likelihood based method for mapping depth and water quality from hyperspectral remote-sensing data. Remote Sensing of Environment, 2014. 147: p. 121-132.

14.         Bramante, J.F., D.K. Raju, and S.J.I.J.o.R.S. Tsai Min, Derivation of bathymetry from multispectral imagery in the highly turbid waters of Singapore’s south islands: A comparative study. 2013. 34(6): p. 2070-2088.

15.         Stumpf, R.P., et al., Determination of water depth with high‐resolution satellite imagery over variable bottom types. 2003. 48(1part2): p. 547-556.

16.         Clark, R.K., T.H. Fay, and C.L.J.A.o. Walker, Bathymetry calculations with Landsat 4 TM imagery under a generalized ratio assumption. 1987. 26(19): p. 4036_1-4038.

17.         Karimi, N., et al., Deriving and Evaluating Bathymetry Maps and Stage Curves for Shallow Lakes Using Remote Sensing Data. 2016. 30(14): p. 5003-5020.

18.         Werdell, P.J., et al., An overview of approaches and challenges for retrieving marine inherent optical properties from ocean color remote sensing. Progress in Oceanography, 2018. 160: p. 186-212.

19.         Traganos, D. and P. Reinartz, Mapping Mediterranean seagrasses with Sentinel-2 imagery. Marine Pollution Bulletin, 2018. 134: p. 197-209.

20.         Zadeh, L.A., Fuzzy logic= computing with words. IEEE transactions on fuzzy systems, 1996. 4(2): p. 103-111.

21.         Fang-fang, Z., et al., Comparative Analysis of Automatic Water Identification Method Based on Multispectral Remote Sensing. Procedia Environmental Sciences, 2011. 11: p. 1482-1487.

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History

  • Receive Date: 31 July 2019
  • Revise Date: 21 October 2019
  • Accept Date: 25 October 2019

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