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

TLDR

Mapping flood‑prone areas is essential for effective flood disaster management. The study proposes a novel flood susceptibility mapping technique. The authors developed a bagging ensemble of K‑Nearest Neighbor classifiers, trained on Sentinel‑1 data and ten conditioning factors, and evaluated its performance with Relief Attribute Evaluation and AUC metrics in the Haraz watershed. The Bagging–Cubic–KNN ensemble achieved the highest accuracy (AUC = 0.660), reduced overfitting, and is recommended for sustainable flood‑prone area management.

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.