Electronic Resource

How reliable are retrieval-augmented and standard ChatGPT models to support flood susceptibility mapping?

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This paper evaluates the performance of baseline and domain-augmented ChatGPT models for literature-based knowledge support in flood susceptibility mapping (FSM) using machine Learning approaches. To assess this, we designed five key questions related to FSM, with benchmark responses derived from our comprehensive review article (Pourzangbar et al., Journal of Flood Risk Management 18, e70042), which analyzed 100 studies on ML applications in FSM. The same questions were posed (i) to standard ChatGPT-4 and ChatGPT-4o models without additional contextual material, and (ii) to a domain-augmented GPT-4 configuration (Chat-FSM) equipped with retrieval access to the 100 reviewed articles. The comparison highlights that GPT-based models can reasonably reproduce frequently reported machine learning models and conditioning factors from the reviewed literature, but show weaker consistency in feature selection methods, often suggesting less relevant techniques. Among the models, ChatGPT-4o demonstrated the weakest alignment with benchmark data, while Chat-FSM demonstrated the highest agreement with the benchmark dataset across most evaluated questions. In terms of application-level efficiency, GPT models required substantially less time and computational effort compared to manual literature synthesis under the defined experimental setup. While ChatGPT-based systems can support literature-informed exploration in FSM, human expertise remains essential for critical reasoning, methodological design, and application to novel or contextspecific scenarios.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

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Penerbit

Jerman : Cambridge University Press., 2026

Deskripsi Fisik

12 hlm.

Bahasa

English

ISBN/ISSN

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Klasifikasi

NONE

Tipe Isi

text

Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

MACHINE LEARNING
ChatGPT
flood susceptibility mapping
large language models

Info Detail Spesifik

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Pernyataan Tanggungjawab

Ali Pourzangbar and Mário J. Franca

Versi lain/terkait

Lampiran Berkas

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