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.

Highlights

Google Scholar

Keywords

Main Subjects

References

[1]     Cazenave A, Altimetry S. Reference module in earth systems and environmental sciences 2018.
[2]     Lyzenga DR. Passive remote sensing techniques for mapping water depth and bottom features. Appl Opt 1978;17:379. doi:10.1364/AO.17.000379.
[3]     Ceyhun Ö, Yalçın A. Remote sensing of water depths in shallow waters via artificial neural networks. Estuar Coast Shelf Sci 2010;89:89–96.
[4]     Elsahabi M, Makboul O, Negm A. Lake Nubia sediment capacity estimation based on satellite remotely sensed detected bathymetry. Procedia Manuf 2018;22:567–74.
[5]     Dörnhöfer K, Oppelt N. Remote sensing for lake research and monitoring–Recent advances. Ecol Indic 2016;64:105–22.
[6]     https://oceanservice.noaa.gov/facts/bathyuses.html n.d.
[7]     https://sealevel.jpl.nasa.gov/overview/oceansurfacetopo/ n.d.
[8]     Duan Z, Bastiaanssen WGM. Estimating water volume variations in lakes and reservoirs from four operational satellite altimetry databases and satellite imagery data. Remote Sens Environ 2013;134:403–16.
[9]     http://www2.csr.utexas.edu/grace/. n.d.
[10]    https://swot.jpl.nasa.gov/home.htm. n.d.
[11]    Calmant S, Seyler F, Cretaux JF. Monitoring Continental Surface Waters by Satellite Altimetry. Surv Geophys 2008;29:247–69. doi:10.1007/s10712-008-9051-1.
[12]    Li C, Sun B, Jia K, Zhang S, Li W, Shi X, et al. Multi-band remote sensing based retrieval model and 3D analysis of water depth in Hulun Lake, China. Math Comput Model 2013;58:771–81.
[13]    Jay S, Guillaume M. A novel maximum likelihood based method for mapping depth and water quality from hyperspectral remote-sensing data. Remote Sens Environ 2014;147:121–32.
[14]    Bramante JF, Raju DK, Tsai Min S. Derivation of bathymetry from multispectral imagery in the highly turbid waters of Singapore’s south islands: A comparative study. Digit 8-Band Res Chall 2010 2013:701–10.
[15]    Stumpf RP, Holderied K, Sinclair M. Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnol Oceanogr 2003;48:547–56. doi:10.4319/lo.2003.48.1_part_2.0547.
[16]    Clark RK, Fay TH, Walker CL. Bathymetry calculations with Landsat 4 TM imagery under a generalized ratio assumption. Appl Opt 1987;26:4036_1. doi:10.1364/AO.26.4036_1.
[17]    Karimi N, Bagheri MH, Hooshyaripor F, Farokhnia A, Sheshangosht S. Deriving and Evaluating Bathymetry Maps and Stage Curves for Shallow Lakes Using Remote Sensing Data. Water Resour Manag 2016;30:5003–20. doi:10.1007/s11269-016-1465-9.
[18]    Werdell PJ, McKinna LIW, Boss E, Ackleson SG, Craig SE, Gregg WW, et al. An overview of approaches and challenges for retrieving marine inherent optical properties from ocean color remote sensing. Prog Oceanogr 2018;160:186–212.
[19]    Traganos D, Reinartz P. Mapping Mediterranean seagrasses with Sentinel-2 imagery. Mar Pollut Bull 2018;134:197–209.
[20]    Zadeh LA. Fuzzy logic= computing with words.-IEEE Transactions on Fuzzy Systems, vol. 4,№ 2, pp. 103-111 n.d.
[21]    Zhang FF, Zhang B, Li JS, Shen Q, Wu Y, Song Y. Comparative analysis of automatic water identification method based on multispectral remote sensing. Procedia Environ Sci 2011;11:1482–7.

Files

History

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

Share

How to cite

Statistics