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Abstract <p>The present study aims to map the flood-susceptible areas in the dynamic landscape of the Indian Himalayan Region. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) decision-making approach combined with the statistical frequency ratio analysis was employed to accomplish the objectives of the study. The maximum weight was assigned to the stream power index (0.12), followed by aspect (0.11), and topographic wetness index (0.10) through the frequency ratio analysis. The results showed that the model achieved an Area Under Curve (AUC) value of 0.889, indicating the efficiency of predicting the flood-prone zones. The susceptibility was categorized into five classes: least, low, moderate, high, and extreme susceptibility. About 9% of the total croplands and 12% of the total plantations are subject to extreme flood susceptibility. Uttarakhand, Jammu and Kashmir, and Himachal Pradesh were three states identified as the highest risk zone. About 10%, 7%, and 9% of the state agricultural areas fall under extreme susceptibility in Uttarakhand, Jammu and Kashmir, and Himachal Pradesh, respectively. In the case of the plantation, the extremely susceptible zone occupies about 12% of the available plantation area each for Uttarakhand and Jammu-Kashmir and 10% in Himachal Pradesh.</p> Research highlights <p><UnorderedList Mark="Bullet"> <ItemContent> <p>An integrated statistical and decision-making approach was used to map the flood susceptible zones.</p> </ItemContent> <ItemContent> <p>The frequency ratio technique was used to assign weights in the TOPSIS analysis.</p> </ItemContent> <ItemContent> <p>SPI, Aspect, and TWI were the highest contributing factors, with weights of 0.12, 0.11, and 0.10, respectively.</p> </ItemContent> <ItemContent> <p>About 9% of the cropland area and 12% of the plantation area are very highly susceptible to flood.</p> </ItemContent> <ItemContent> <p>Uttarakhand, Jammu and Kashmir, and Himachal Pradesh are the three states at maximum risk.</p> </ItemContent> </UnorderedList></p>

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Integrating statistical analysis and decision-making to assess flood susceptibility in Indian mountainous agroecosystems

  • 期刊论文

  • 发表日期：

    2026年3月30日

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  • Swadhina Koley,
  • Manjar Alam,
  • Soora Naresh Kumar

摘要

Abstract

The present study aims to map the flood-susceptible areas in the dynamic landscape of the Indian Himalayan Region. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) decision-making approach combined with the statistical frequency ratio analysis was employed to accomplish the objectives of the study. The maximum weight was assigned to the stream power index (0.12), followed by aspect (0.11), and topographic wetness index (0.10) through the frequency ratio analysis. The results showed that the model achieved an Area Under Curve (AUC) value of 0.889, indicating the efficiency of predicting the flood-prone zones. The susceptibility was categorized into five classes: least, low, moderate, high, and extreme susceptibility. About 9% of the total croplands and 12% of the total plantations are subject to extreme flood susceptibility. Uttarakhand, Jammu and Kashmir, and Himachal Pradesh were three states identified as the highest risk zone. About 10%, 7%, and 9% of the state agricultural areas fall under extreme susceptibility in Uttarakhand, Jammu and Kashmir, and Himachal Pradesh, respectively. In the case of the plantation, the extremely susceptible zone occupies about 12% of the available plantation area each for Uttarakhand and Jammu-Kashmir and 10% in Himachal Pradesh.

Research highlights

An integrated statistical and decision-making approach was used to map the flood susceptible zones.

The frequency ratio technique was used to assign weights in the TOPSIS analysis.

SPI, Aspect, and TWI were the highest contributing factors, with weights of 0.12, 0.11, and 0.10, respectively.

About 9% of the cropland area and 12% of the plantation area are very highly susceptible to flood.

Uttarakhand, Jammu and Kashmir, and Himachal Pradesh are the three states at maximum risk.