Abstract
Abstract. Bathymetry in coastal environment plays a key role in understanding erosion dynamics and evolution along coasts. In the presented investigation depth along the shore-line was estimated using different multispectral satellite data. Training and validation data derived from a traditional bathymetric survey developed along transects in Cesenatico; measured data were collected with a single-beam sonar returning centimetric precision. To limit spatial auto-correlation training and validation dataset were built choosing alternatively one transect as training and another as validation. Each set was composed by a total of ~6000 points. To estimate water depth two methods were tested, Support Vector Machine (SVM) and Random Forest (RF). The RF method provided the higher accuracy with a root mean square error value of 0.228 m and mean absolute error of 0.158 m, against values of 0.409 and 0.226 respectively for SVM. Results show that application of machine learning methods to predict depth near shore can provide interesting results that can have practical applications.
Highlights
The study of bathymetry in coastal environment is becoming increasingly important because of the strategic importance of these areas and their vulnerability to different pressure factors
Considering the promising results obtained by Machine Learning (ML), this study aims to evaluate different ML techniques performance in Satellite Derived Bathymetry (SDB) analysis
The iterative procedure to identify the best configuration of algorithm lead to the choice of the following parameters for Random Forest (RF): “ntree” was set equal to 600 and “mtry” equal to 10
Summary
The study of bathymetry in coastal environment is becoming increasingly important because of the strategic importance of these areas and their vulnerability to different pressure factors. Coastal areas fragility derives in particular from the constant pressure of intense anthropogenic activities such as urbanisation, exploitation of natural resources, and climate change-induced natural hazards (Paterson et al, 2011). Considering these aspects bathymetry is important to get a direct measure of the magnitude of these phenomena. Bathymetric surveys are conducted using different high precision tools, such as single or multi beam echo sounders. This way to collect information produces high precision measures. High survey costs and difficulties connected to large area measurement (time required, dependency on weather, sea condition etc...) are the main limitation of this approach to bathymetry
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