**1. Introduction**

Around the world, natural catastrophes pose a major threat to property and human lives. Although it is impossible to prevent natural hazards, their negative impact can be reduced by creating effective planning strategies and mitigation techniques. Significant morphological changes in landforms brought on by active tectonics or climatic changes may affect human activity and management. Events such as gully erosion, landslides, and floods are physical phenomena that are active in geological times but uneven in time and space [1–4].

According to (NDMA, 2008), a flood is extra water due to a river being incapable of transferring a large amount of water from the upstream area within its banks after significant rainfall. Floods occur more frequently and are more damaging to local social, economic, and environmental aspects than all other natural catastrophes that occur on a global scale. High intensity precipitation in the watershed, changes in river cross sections caused by sedimentation, sudden dam failure, release of high flow from dams, etc., are just a few causes of floods.

Depending on a variety of criteria that includes velocity, geography, and source, floods can be broadly classified into four categories: fluvial (river) floods, ground water floods, pluvial floods, and surge (coastal) floods. Assam, which is in the monsoon climatic region,

**Citation:** Harshasimha, A.C.; Bhatt, C.M. Flood Vulnerability Mapping Using MaxEnt Machine Learning and Analytical Hierarchy Process (AHP) of Kamrup Metropolitan District, Assam. *Environ. Sci. Proc.* **2023**, *25*, 73. https://doi.org/10.3390/ ECWS-7-14301

Academic Editor: Athanasios Loukas

Published: 3 April 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Indian Institute of Remote Sensing, Dehradun 248001, India; akshaysimhachhp@gmail.com

**<sup>\*</sup>** Correspondence: cmbhatt@iirs.gov.in

has been having an average yearly rainfall from1600 mm to 4300 mm, causing flooding throughout the region (Assam State Disaster Management, n.d.). Overflowing tributaries of the Brahmaputra River also contribute to the volume of flood water in the valley.

Furthermore, this state has unique hydrological, climatic, and unstable geological conditions that intensify the source of numerous geomorphic and geological dangers in the region. Considering all these conditions, the use of remote sensing techniques proves to be a viable solution.

### **2. Study Area**

The Kamrup Metropolitan district, which is located in the state of Assam in the northeastern part of India, covers an area of 1528 km2. The study area stretches from 26.07◦ N latitude to 91.63◦ E longitude in the lower basin of Brahmaputra, which is prone to rapid flooding nearly every year (Figure 1).

**Figure 1.** Research study area of the Kamrup Metropolitan district, Assam, India.

In 2021, the districts of Assam had an average annual temperature of 24 ◦C and an annual rainfall of over 2200 mm. The Kamrup Metropolitan district has major cities and is Assam's administrative center.

### **3. Materials and Methods**

Figure 2 illustrates the methodology that was approached with AHP modeling and MaxEnt modeling.

### *3.1. Flood Inventory Mapping*

A key step for susceptibility mapping is the preparation of an inventory of hazard landforms. The flood inventory for the Kamrup Metropolitan district (Assam, India) is compiled from national and regional documents from various organizations such as Assam State Disaster Management Authority and the North-Eastern Space Applications Centre. About 53 flood areas are listed on the inventory map for floods. For training samples, a random partition approach is used. In the present study, 70% of each hazard is considered for model construction (training) and the remaining 30% of each hazard is used for validation.

(**b**) 

**Figure 2.** (**a**) AHP flowchart; (**b**) MaxEnt flowchart.

### *3.2. Flood Conditioning Factors*

It is essential to determine the effective factors of different natural hazards and humanmade fatalities to perform flood maps [5]. A good understanding of the main hazard-related factors is needed to recognize the susceptible areas.

For this aim, the conditioning factors for the hazard were selected [6–10]. In this study, ArcGIS 10.3 (ESRI, USA) is used to perform the analysis of AHP and to produce and display the data layers. All the factors were processed into a raster grid of 30 × 30 m grid cells. Entire conditioning factors were primarily continuous, and some of them were classified within different categories based on expert knowledge and a literature review [11–14].
