Applying a 1D Convolutional Neural Network in Flood Susceptibility Assessments—The Case of the Island of Euboea, Greece
The main scope of the study is to evaluate the prognostic accuracy of a one-dimensional convolutional neural network model (1D-CNN), in flood susceptibility assessment, in a selected test site on the island of Euboea, Greece. Logistic regression (LR), Naïve Bayes (NB), gradient boosting (GB), and a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/14/3471 |
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| author | Paraskevas Tsangaratos Ioanna Ilia Aikaterini-Alexandra Chrysafi Ioannis Matiatos Wei Chen Haoyuan Hong |
| author_facet | Paraskevas Tsangaratos Ioanna Ilia Aikaterini-Alexandra Chrysafi Ioannis Matiatos Wei Chen Haoyuan Hong |
| author_sort | Paraskevas Tsangaratos |
| collection | DOAJ |
| description | The main scope of the study is to evaluate the prognostic accuracy of a one-dimensional convolutional neural network model (1D-CNN), in flood susceptibility assessment, in a selected test site on the island of Euboea, Greece. Logistic regression (LR), Naïve Bayes (NB), gradient boosting (GB), and a deep learning neural network (DLNN) model are the benchmark models used to compare their performance with that of a 1D-CNN model. Remote sensing (RS) techniques are used to collect the necessary flood related data, whereas thirteen flash-flood-related variables were used as predictive variables, such as elevation, slope, plan curvature, profile curvature, topographic wetness index, lithology, silt content, sand content, clay content, distance to faults, and distance to river network. The Weight of Evidence method was applied to calculate the correlation among the flood-related variables and to assign a weight value to each variable class. Regression analysis and multi-collinearity analysis were used to assess collinearity among the flood-related variables, whereas the Shapley Additive explanations method was used to rank the features by importance. The evaluation process involved estimating the predictive ability of all models via classification accuracy, sensitivity, specificity, and area under the success and predictive rate curves (AUC). The outcomes of the analysis confirmed that the 1D-CNN provided a higher accuracy (0.924), followed by LR (0.904) and DLNN (0.899). Overall, 1D-CNNs can be useful tools for analyzing flood susceptibility using remote sensing data, with high accuracy predictions. |
| first_indexed | 2024-03-11T00:41:47Z |
| format | Article |
| id | doaj.art-2ebaa199b4c04e8c8419718f29d03cc9 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T00:41:47Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-2ebaa199b4c04e8c8419718f29d03cc92023-11-18T21:11:15ZengMDPI AGRemote Sensing2072-42922023-07-011514347110.3390/rs15143471Applying a 1D Convolutional Neural Network in Flood Susceptibility Assessments—The Case of the Island of Euboea, GreeceParaskevas Tsangaratos0Ioanna Ilia1Aikaterini-Alexandra Chrysafi2Ioannis Matiatos3Wei Chen4Haoyuan Hong5Laboratory of Engineering Geology and Hydrogeology, Department of Geological Sciences, School of Mining and Metallurgical Engineering, National Technical University of Athens, Zographou Campus, 15773 Athens, GreeceLaboratory of Engineering Geology and Hydrogeology, Department of Geological Sciences, School of Mining and Metallurgical Engineering, National Technical University of Athens, Zographou Campus, 15773 Athens, GreeceLaboratory of Engineering Geology and Hydrogeology, Department of Geological Sciences, School of Mining and Metallurgical Engineering, National Technical University of Athens, Zographou Campus, 15773 Athens, GreeceLaboratory of Engineering Geology and Hydrogeology, Department of Geological Sciences, School of Mining and Metallurgical Engineering, National Technical University of Athens, Zographou Campus, 15773 Athens, GreeceCollege of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, ChinaDepartment of Geography and Regional Research, University of Vienna, 1010 Vienna, AustriaThe main scope of the study is to evaluate the prognostic accuracy of a one-dimensional convolutional neural network model (1D-CNN), in flood susceptibility assessment, in a selected test site on the island of Euboea, Greece. Logistic regression (LR), Naïve Bayes (NB), gradient boosting (GB), and a deep learning neural network (DLNN) model are the benchmark models used to compare their performance with that of a 1D-CNN model. Remote sensing (RS) techniques are used to collect the necessary flood related data, whereas thirteen flash-flood-related variables were used as predictive variables, such as elevation, slope, plan curvature, profile curvature, topographic wetness index, lithology, silt content, sand content, clay content, distance to faults, and distance to river network. The Weight of Evidence method was applied to calculate the correlation among the flood-related variables and to assign a weight value to each variable class. Regression analysis and multi-collinearity analysis were used to assess collinearity among the flood-related variables, whereas the Shapley Additive explanations method was used to rank the features by importance. The evaluation process involved estimating the predictive ability of all models via classification accuracy, sensitivity, specificity, and area under the success and predictive rate curves (AUC). The outcomes of the analysis confirmed that the 1D-CNN provided a higher accuracy (0.924), followed by LR (0.904) and DLNN (0.899). Overall, 1D-CNNs can be useful tools for analyzing flood susceptibility using remote sensing data, with high accuracy predictions.https://www.mdpi.com/2072-4292/15/14/3471flood susceptibilityremote sensingconvolutional neural networkgeoinformaticsEuboeaGreece |
| spellingShingle | Paraskevas Tsangaratos Ioanna Ilia Aikaterini-Alexandra Chrysafi Ioannis Matiatos Wei Chen Haoyuan Hong Applying a 1D Convolutional Neural Network in Flood Susceptibility Assessments—The Case of the Island of Euboea, Greece Remote Sensing flood susceptibility remote sensing convolutional neural network geoinformatics Euboea Greece |
| title | Applying a 1D Convolutional Neural Network in Flood Susceptibility Assessments—The Case of the Island of Euboea, Greece |
| title_full | Applying a 1D Convolutional Neural Network in Flood Susceptibility Assessments—The Case of the Island of Euboea, Greece |
| title_fullStr | Applying a 1D Convolutional Neural Network in Flood Susceptibility Assessments—The Case of the Island of Euboea, Greece |
| title_full_unstemmed | Applying a 1D Convolutional Neural Network in Flood Susceptibility Assessments—The Case of the Island of Euboea, Greece |
| title_short | Applying a 1D Convolutional Neural Network in Flood Susceptibility Assessments—The Case of the Island of Euboea, Greece |
| title_sort | applying a 1d convolutional neural network in flood susceptibility assessments the case of the island of euboea greece |
| topic | flood susceptibility remote sensing convolutional neural network geoinformatics Euboea Greece |
| url | https://www.mdpi.com/2072-4292/15/14/3471 |
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