Software

Projects

Open-source, agent-driven tools that turn my hydrology research into software anyone can run. I design the architecture, write the engines, and ship the interfaces, from a live map you draw on to natural-language runtimes that drive industrial modelling software end-to-end.

The ecosystem

The Agentic Hydrology Platform

One natural-language interface over hydrological modelling, across river basins, cities, and the countryside. You describe the goal in plain language; an agentic runtime selects the data, picks the right engine, runs the simulation, and explains the result. Every step is deterministic, audited, and reproducible. The platform composes the projects below into a single workflow.

Architecture: a plain-language interface feeds the aiswmm runtime, which orchestrates four interchangeable engines over one shared open-data foundation. Diagram by the author.

Python PyTorch MCP servers Agent Skills EPA SWMM 5.2 DHI MIKE+ Open data

View on GitHub Related publications

Inside the platform

The pieces I built

Flagship · Live demo

Agentic SWMM

My signature open-source project: a verification-first workflow that drives EPA SWMM end-to-end with agent skills and the Model Context Protocol. Every run is auditable and reproducible, with provenance tracking and modelling memory. Try it live on the Greenwich Peninsula, end to end in the browser: SWMManywhere synthesis → swmm5 run → audit → render. Published in AI for Engineering (2026).

Agent Skills MCP EPA SWMM 5.2 Provenance Reproducible

End-to-end in the browser Peer-reviewed · AI for Engineering MIT licensed

Open live demo View on GitHub Read paper

Open data · open-source service

SWMMCanada

Draw a boundary anywhere in Canada and build a ready-to-run EPA SWMM model straight from Canadian open data: real municipal storm networks for 7 cities, synthesized networks elsewhere. React + MapLibre front end, Python + FastAPI back end.

Python · FastAPI React · MapLibre geopandas

7 cities MIT licensed

Open the app GitHub

The MIKE+ Sirius_RTC example network with 568 nodes and 576 links, rendered as a dense mesh of pipes

Commercial engine · headless

Agentic MIKE+

A headless, natural-language automation layer for DHI MIKE+. Agents inspect, edit, run, and visualize hydraulic models without ever opening the GUI. Worker-isolated, deterministic, and license-flexible (read & plot run license-free).

MCP server mikeio · mikeio1d Python 3.11

568 nodes verified 10+ tools

GitHub

Observed versus LSTM-predicted streamflow hydrograph on held-out test years, annotated NSE 0.871 and KGE 0.880

Basin forecasting · deep learning

LSTM streamflow engine

A data-driven rainfall-runoff model that learns long-range dependencies (snowpack, soil moisture, groundwater) to forecast catchment streamflow and floods. Reaches NSE ≈ 0.87 on held-out test years. Ships inside the platform.

PyTorch LSTM Rainfall-runoff

NSE 0.87 test KGE 0.88

GitHub

Also

Earlier tools

Fuzzy-HydroGPT-RTC v2.0

Desktop app · PyQt6

Hydrological modelling and uncertainty quantification in one PyQt6 interface, with Real-Time Control (RTC) for near-instant (<10 s) GPT-based peak-flow predictions. Generalizable beyond hydrology.

View on GitHub

HydroGPT-Fuzzy v1.0

Data-processing platform

A rapid hydrological & climate data processor: Canadian weather scraping, GPT-based JSONL conversion, triangular/trapezoidal fuzzy membership functions, and NSE model evaluation.

Download v1.0