Journal article
AI-assisted voice enabled computing framework for hydrological analysis
Environmental modelling & software : with environment data news, Vol.197, 106833
02/2026
DOI: 10.1016/j.envsoft.2025.106833
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.
•We present a no-code, web-based platform for AI-assisted hydrological analysis.•Users interact through voice or text with a conversational AI assistant.•The platform integrates real-time hydrological data from federal sources.•It supports reproducible, multi-step workflows with session memory.•The tool lowers barriers for research, education, and decision-making.
Details
- Title: Subtitle
- AI-assisted voice enabled computing framework for hydrological analysis
- Creators
- Carlos Erazo Ramirez - Tulane UniversityIbrahim Demir - Tulane University
- Resource Type
- Journal article
- Publication Details
- Environmental modelling & software : with environment data news, Vol.197, 106833
- DOI
- 10.1016/j.envsoft.2025.106833
- ISSN
- 1364-8152
- eISSN
- 1873-6726
- Publisher
- Elsevier Ltd
- Grant note
- Department of the Interior (DOI) -US Geological Survey (USGS): G25AP00137
This material is based upon work supported by the Department of the Interior (DOI) -US Geological Survey (USGS) under Award No. G25AP00137.
- Language
- English
- Date published
- 02/2026
- Academic Unit
- Electrical and Computer Engineering; Civil and Environmental Engineering; Injury Prevention Research Center
- Record Identifier
- 9985096042802771
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