How Well Do DeepSeek, ChatGPT, and Gemini Respond to Water Science Questions?

Thumbnail Image

Files

1-s2.0-S1364815225004566-main-1.pdf (11.05 MB)

Access rights

openAccess
CC BY
publishedVersion

URL

Journal Title

Journal ISSN

Volume Title

A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.

Authors

Date

2026-01-30

Department

Department of Built Environment

Major/Subject

Mcode

Degree programme

Language

en

Pages

17

Series

Environmental Modelling & Software, Volume 196

Abstract

This study aims to evaluate the performance of three prominent LLMs, DeepSeek R1, ChatGPT-4o, and Gemini 2, in addressing key questions within four core fields of hydrology and water science: machine learning and optimization, remote sensing, flood modeling, and sediment transport. LLMs’ responses are systematically compared to benchmark responses derived from review articles in the respective fields. To assess the LLMs’ efficiency, a novel evaluation rubric is introduced in this study, incorporating four key criteria: relevancy, accuracy, authenticity, and novelty. Findings revealed that each model can address the core aspects of the benchmark questions. DeepSeek R1 achieved the highest overall scores in machine learning and optimization, flood modeling, and sediment transport, while ChatGPT-4o demonstrated superior performance in remote sensing. Notably, DeepSeek R1 and Gemini 2 exhibited the lowest response similarity in 95% of the evaluated questions, whereas ChatGPT-4o and Gemini 2 showed the highest similarity in 70% of cases.

Description

Keywords

CharGPT-4o, DeepSeek R1, Gemini 2, hydrology, large language model (LLM), water science, Hydrology, ChatGPT-4o, Large language model (LLM), Water science

Other note

DOI

10.1016/j.envsoft.2025.106772

Citation

Hosseini, S H & Pourzangbar, A 2026, 'How Well Do DeepSeek, ChatGPT, and Gemini Respond to Water Science Questions?', Environmental Modelling & Software, vol. 196, 106772. https://doi.org/10.1016/j.envsoft.2025.106772

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-202511268872

Collections

[article-cris] Insinööritieteiden korkeakoulu / ENG