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

By Himan Shahabi, Ataollah Shirzadi, K. Ghaderi, Ebrahim Omidvar, N. Al‐Ansari, John J. Clague, Marten Geertsema, Khabat Khosravi, A. Amini, Sepideh Bahrami and 7 more. Remote Sensing, 2020

334 citations recorded by the source corpus.

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.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/6c074420c8d456c0866c4f5e37e5fcf3998732e9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Jie Dou
2. Himan Shahabi

In Global and Planetary Change

1. Ataollah Shirzadi
2. Himan Shahabi
3. Kamran Chapi
4. N. Al‐Ansari
5. Jie Dou

In Water Science and Technology

1. Himan Shahabi

Citations in the corpus

• A Survey on ensemble learning under the era of deep learning (2021, methodology): “…imbalance learning (Bi and Zhang 2018) 97 2018 TA7 Soil moisture forecasting (Prasad et al. 2018) 71 2018 TA8 Flood susceptibility mapping (Shahabi et al. 2020) 45 2020 TA9 Detection of lung cancer (Shakeel et al. 2020) 43 2020 TA10 Hyperspectral image classification (Su et al. 2020) 26…”
• Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran (2020, background): “A wide variety of machine learning models are now in use in natural hazard research, including: artificial neural network (ANN) [39,40], adaptive neuro-fuzzy inference system (ANFIS) [41–44], support vector machine (SVM) [15,45,46], K-nearest neighbor (KNN) [47], logistic model tree (LMT) [48,49], a”