Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier

Abstract

Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas.

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

“…This problem has been addressed through the development and application of machine learning algorithms, which are able to handle large volumes of non-linear and complex data derived from different sources and reported at a variety of scales. These algorithms have been extensively used in natural hazard studies, for example: flooding [23][24][25][26][27][28][29][30][31][32][33], wildfire [34,35], dust storm [36], sinkhole formation [37], drought [38,39], earthquakes [40,41], gully erosion [42][43][44], land/ground subsidence [45,46], groundwater contamination [26,[47][48][49][50][51], and landslides [17,. They can extract informative patterns in historical data to predict future events [79].…”

GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models

“…Scientists have used a variety of computational data mining methods and models in natural hazard research, including studies of floods [18][19][20][21][22][23][24][25][26][27][28], wildfire [29], sinkholes [30], droughtiness [31,32], earthquakes [33,34], land/ground subsidence [35,36], groundwater [21,[37][38][39][40][41][42][43][44], and landslides [22,. These methods extract related patterns in historical data to predict future events [73].…”

Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran

“…For example, Van Dao et al [3] reported that deep learning ANN requires much more computational time than conventional methods. Despite this disadvantage, machine learning methods have the ability to handle large volumes of non-linear and complex data derived from different sources and reported at a variety of scales in many fields especially in studies of natural hazards such as floods [18][19][20][21][22][23][24][25][26][27][28], wildfires [29,30], sinkholes [31], drought [32,33], earthquakes [34,35], gully erosion [36,37], and land/ground subsidence [38,39].…”