Ground penetrating radar use in flood prevention

Abstract

The main goal of the work is to create an automatic method of locating weak zones within flood embankments structure based on ground penetrating radar (GPR) measurements. The presented research shows the possibilities of using advanced methods of GPR signal processing and its analysis with the help of signal attributes for detecting zones threatening the stability of the structure of flood embankments. Obtained results may help in quick detection of potential weak zones of the embankments and consequently give means to ameliorate them, which may prevent damage to the embankments during rise in the level of river water. The presented analyses were carried out on GPR data obtained for the flood banks of the Rudawa River (Kraków, Poland) in the area of their visible degradation. The use of signal attributes, such as Energy, instantaneous frequency, similarity, curvature gradient, dominant frequency, allowed initial indication of anomalous zones threatening the stability of embankment. Advanced processing supported by the use of advanced filters such as GLCM, Grubbs filter threshold and Convolve Prewitt helped in the analysis of the structure of the embankments. Artificial neural networks (ANNs) in the supervised and unsupervised variants were used to perform the automatic classification of weakened zones within the embankments. The results demonstrated the usefulness of GPR geophysical method through integration of ANN in the analysis of the data.

The Potential Use of Ground Conductivity Meters to Identify the Location of Seepages—Case Study of the Maniów Levee near Krakow, Poland

“…There is a lot of possibilities to conduct a monitor of seepage through the use of geoelectrical methods [33]. The easiest and cheapest way so far would be to repeat the measurement in the same places or even profiles at certain intervals, at least one month.…”

Fusion of the Multisource Datasets for Flood Extent Mapping Based on Ensemble Convolutional Neural Network (CNN) Model

“…The ML methods are applied in two main categories: (1) supervised method by predicting some output variable associated with each input sample and (2) unsupervised method that does not need any sample data and provides a prediction by considering input feature dataset. The ML methods are widely deployed in many applications based on different sensors and datasets such as quasidistributed smart textile [37], simultaneous assessment of magnetic field intensity [38], paddy rice seed classification [39,40], anime film visualization [41], eggplant seed classification [42], regional digital construction [43], flood mapping [44], and flood prevention [45]. Although the ML methods have provided promising results in many abovementioned applications, they suffer from lower coverage and generalization [1].…”

Geophysical and Uav-Based Observations Over a Flood Defense Structure: Application to the Polder2c’s Experimental Dike

“…Monitoring flood protection systems has become crucial with the increase of extreme weather events. In this context, geophysical methods such as ERT, GPR, Multichannel Analysis of Surface Waves (MASW), seismic refraction, electromagnetic induction (EMI) or Self Potentials (SP) has gained more and more importance to characterize dikes (Fargier et al, 2014;Tomecka-Suchoń, 2019;Niederleithinger et al, 2012;Rahimi et al, 2018;Bolève et al, 2012). Amongst these methods, ERT and GPR are useful and complementary tools, easy to carried out on the field for dike assessment (Golebiowski, Tomislaw et al, 2020).…”