Applying a 1D Convolutional Neural Network in Flood Susceptibility Assessments—The Case of the Island of Euboea, Greece

Abstract

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.

Spatial Prediction of Fluvial Flood in High-Frequency Tropical Cyclone Area Using TensorFlow 1D-Convolution Neural Networks and Geospatial Data

“…This underscores the potential of 1D-CNN as a promising tool for spatial predictions of fluvial floods. These findings align with existing literature [24][25][26]81], emphasizing the efficacy of novel deep learning approaches in achieving superior prediction accuracy in flood studies, surpassing traditional machine learning models. Herein, the 1D-CNN model performs better than the DeepNN because the fluvial flood patterns in the study area are local, meaning they depend on nearby data points.…”

“…It has achieved groundbreaking success in a wide range of real-world applications [61]. Within the domain of deep learning, the 1D Convolutional Neural Network (1D-CNN) has garnered attention for its ability to capture meaningful features, making it suitable for both classification and regression tasks in environmental modeling and predictions, i.e., fires [62], landslides [63], and floods [24].…”

A Country Wide Evaluation of Sweden's Spatial Flood Modeling With Optimized Convolutional Neural Network Algorithms

“…Tsangaratos et al. (2023) compared the predictive ability of Naïve Bayes Trees, Logistic Regression, Gradient Boosting, DL Neural Network, and CNN to detect flood susceptible area in the island of Euboea, Greece. The results confirmed the superior predictive performance of CNN (AUC = 0.924), followed by Logistic Regression (AUC = 0.904) and DL Neural Network (AUC = 0.899).…”