This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1016/j.envsoft.2025.106833. This is version 1 of this Preprint.
AI-Assisted Voice Enabled Computing Framework for Hydrological Analysis
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Authors
Carlos Erazo Ramirez 
,
Ibrahim Demir
Abstract
This work presents a web-based, voice-enabled, no-code platform for AI-assisted hydrological analysis. The system allows users to interact through natural language—via both text and speech—to retrieve data, utilize hydrological functions, and visualize spatial and analytical outputs. Core components include a conversational AI assistant utilizing Large Language Models, a modular analysis engine based on HydroSuite, and direct integration with hydrological data from federal agencies using HydroShare and other data and web services. Structured intent parsing, persistent session state, and dynamic map-layer control support multi-turn interactions and reproducible workflows. A case study over the Mississippi River Delta demonstrates how the platform enables guided exploration, layered data integration, and low-latency execution with minimal technical overhead. The platform lowers barriers for research, education, and decision-making in hydrology by combining AI reasoning with a transparent, accessible user interface. By enabling natural language interaction, data integration, and reproducible, multi-turn task processing, this system lays the foundation for automated hydrological research and operational workflows.
DOI
https://doi.org/10.31223/X5C44Q
Subjects
Civil Engineering, Computer and Systems Architecture, Environmental Engineering, Higher Education
Keywords
large language models, Map Dashboard, Voice Recognition, hydroinformatics, No-Code
Dates
Published: 2025-07-01 16:01
Last Updated: 2025-07-01 16:01
License
CC BY Attribution 4.0 International
Additional Metadata
Conflict of interest statement:
None
Data Availability:
Dashboard access may be provided upon request.
Metrics
Views: 983
Downloads: 525
Comment #220 David De León Pérez @ 2025-07-03 20:11
Please cite as:
MDPI and ACS Style
De León Pérez, D.; Acosta Vega, R.; Salazar Galán, S.; Aranda, J.Á.; Francés García, F. Toward Systematic Literature Reviews in Hydrological Sciences. Water 2024, 16, 436. https://doi.org/10.3390/w16030436
AMA Style
De León Pérez D, Acosta Vega R, Salazar Galán S, Aranda JÁ, Francés García F. Toward Systematic Literature Reviews in Hydrological Sciences. Water. 2024; 16(3):436. https://doi.org/10.3390/w16030436
Chicago/Turabian Style
De León Pérez, David, Rick Acosta Vega, Sergio Salazar Galán, José Ángel Aranda, and Félix Francés García. 2024. "Toward Systematic Literature Reviews in Hydrological Sciences" Water 16, no. 3: 436. https://doi.org/10.3390/w16030436
APA Style
De León Pérez, D., Acosta Vega, R., Salazar Galán, S., Aranda, J. Á., & Francés García, F. (2024). Toward Systematic Literature Reviews in Hydrological Sciences. Water, 16(3), 436. https://doi.org/10.3390/w16030436
Comment #219 David De León Pérez @ 2025-07-03 20:10
Please cite as:
MDPI and ACS Style
De León Pérez, D.; Acosta Vega, R.; Salazar Galán, S.; Aranda, J.Á.; Francés García, F. Toward Systematic Literature Reviews in Hydrological Sciences. Water 2024, 16, 436. https://doi.org/10.3390/w16030436
AMA Style
De León Pérez D, Acosta Vega R, Salazar Galán S, Aranda JÁ, Francés García F. Toward Systematic Literature Reviews in Hydrological Sciences. Water. 2024; 16(3):436. https://doi.org/10.3390/w16030436
Chicago/Turabian Style
De León Pérez, David, Rick Acosta Vega, Sergio Salazar Galán, José Ángel Aranda, and Félix Francés García. 2024. "Toward Systematic Literature Reviews in Hydrological Sciences" Water 16, no. 3: 436. https://doi.org/10.3390/w16030436
APA Style
De León Pérez, D., Acosta Vega, R., Salazar Galán, S., Aranda, J. Á., & Francés García, F. (2024). Toward Systematic Literature Reviews in Hydrological Sciences. Water, 16(3), 436. https://doi.org/10.3390/w16030436