**4. Results**

### *4.1. Maximum Entropy (MaxEnt)*

The MaxEnt software uses the Maximum Entropy model to calculate hazard estimates (version 3.4.4). The MaxEnt model is usually used to estimate species distribution based on the most significant environmental conditions. From a decision-theoretic perspective, we also interpret the maximum entropy estimation as a reliable Bayes estimation. The model relies on a machine learning reaction that generates hypotheses based on skewed data. The result from the model is obtained in ASCII format. The conditioning factors are translated from raster into ASCII format, as required by the software. The most crucial phase in the modeling process is validation. The AUC has been used to assess the built-in hazard model's prediction accuracy, as shown in Figure 4.

**Figure 4.** MaxEnt flood mapping.

### *4.2. Analytical Hierarchy Process (AHP)*

Table 1 shows the weights assigned to the nine geo-environmental parameters used to generate the flood hazard map. To obtain the spatial distribution of flood hazards, the parameters evaluated were mapped and normalized into five classes based on a rating scale of 1 to 5, with 1 being the least vulnerable area and 5 being the most vulnerable area, as shown in Figure 5.

### **Table 1.** AHP weights.


**Figure 5.** AHP flood Mapping.

#### *4.3. Comparative Analysis of sensitivity and Response Curves*

The relative influence of each predictor variable on the outcomes of the predicted maps using the jackknife test was examined using a sensitivity analysis from the AUC. Concerning validating with respect to flood inventory points, we observe that the 0.83 AUC of the MaxEnt model slightly outperformed the 0.763 AUC of the AHP model, as shown in Figures 6 and 7, respectively.

**Figure 6.** AUC for MaxEnt.

**Figure 7.** AUC for AHP.

### *4.4. Spatial Extent of Vulnerability*

Flood vulnerability maps generated from the outputs of MaxEnt and AHP show that the areas encircling the river, the surfaces with slopes from 0 degrees to 11 degrees, and the land in the elevation range from 41 m to 52 m showed vulnerability to floods. Subsequently, we observe that the flood map generated by MaxEnt and AHP showed a reasonable resemblance with the historical flood maps of ISRO's Bhuvan.

From the results, we also observe that out of the 1528 km<sup>2</sup> total area of the district, about 650 km<sup>2</sup> was found to be highly vulnerable to floods; moreover, major locations such as Guwahati, Dispur, and Sonapur Gaon in the Kamrup Metropolitan district showed a higher vulnerability to flooding.

### **5. Conclusions**

In this study, flood vulnerability maps are generated for a major district of Assam by utilizing the AHP approach and MaxEnt machine learning. Given its ability to handle huge datasets, a multi-criteria analysis using AHP and MaxEntis identified and proven to be beneficial for flood risk assessment.

Slope, drainage density, TWI, and elevation were the primary flood-causing geoenvironmental parameters in the studied area. The AHP method and MaxEnt technique employed in this study are effective and enable the possibility of further research into flood vulnerabilities in various sections of the state or country. The AUC graphs are employed as a validation method in this work, which demonstrates an additional possibility of research validation and applicability in geospatial vulnerability assessment owing to extreme events.

**Author Contributions:** The initial idea for the work came from A.C.H. with assistance from mentor C.M.B., who is also a corresponding author on this publication. The records, compilation, and choice of the final design of the work were done by A.C.H. All authors contributed to this paper and shared ideas. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
