EDS | Environmental Decision Support System

System Overview

Turning environmental signal into operational awareness.

EDS started as a practical problem, not an abstract one: deciding whether a night is actually worth setting up for astrophotography, and keeping optics clear of dew once a session is running. That recurring, concrete decision — cloud cover, sky transparency, seeing, and dew point, all changing hour to hour — shaped the system's core sense-forecast-alert loop well before it was generalized into a broader environmental decision-support pipeline.

EDS is built as a layered pipeline rather than a single monolithic tool. Sensing sources feed a normalization and modeling layer, which produces forecasts and evaluates them against configurable thresholds, which in turn drives alerting and awareness outputs. Each layer is designed to evolve on its own schedule — new sensing sources, refined models, and additional output channels can be added without reworking the system end to end.

The goal is continuous, structured visibility into changing environmental conditions, not a one-off report or a static map. EDS is designed for teams that need to track conditions over time and respond when they change — environmental monitoring groups, infrastructure operators, and field research teams — rather than for a single fixed use case. It is currently an active prototype: core ingestion and modeling are running against real test data, and the surrounding system is still being shaped around that foundation.

Domain: Sensing / Forecasting
Status: Active prototype
Core loop: Sense → Forecast → Alert → Act
Primary users: Astrophotographers, environmental monitoring & infrastructure teams

Sensing Inputs

A layered set of environmental signal sources.

EDS treats sensing as a plural, extensible layer rather than a dependency on any single feed. Sources are normalized into a common schema so the modeling layer can reason over them consistently, regardless of where a given signal originates.

01

Astronomical Observing Conditions

Cloud cover, sky transparency, seeing, dew point, and moon phase — the specific combination of conditions an imaging session actually depends on.

02

Meteorological Telemetry

Wind, precipitation, temperature, humidity, and pressure readings from ground stations and public weather feeds.

03

Hydrological & Water-Level Data

River, reservoir, and coastal water-level sensors used to track hydrological change over time.

04

Remote Sensing & Imagery

Satellite and aerial imagery layers used to contextualize and cross-check ground-level sensor readings.

05

Field Sensor Networks

Distributed, IoT-class sensor nodes reporting localized environmental conditions from fixed or mobile deployments.

06

Historical & Climatological Data

Long-run historical datasets used to establish baselines, seasonal patterns, and anomaly thresholds.

07

Open Environmental Data Feeds

Public and institutional environmental datasets integrated as supplementary context around primary sensing sources.

Forecasting & Alerting Model

From raw signal to evaluated risk.

Between sensing and awareness sits a modeling pipeline that turns unstructured signal into evaluated, actionable state. Each stage narrows the data down — from raw readings to a small number of alerts that actually warrant attention.

01

Ingest

Raw sensor and feed data is collected, timestamped, and queued for processing.

02

Normalize

Inputs are cleaned, aligned to a common schema, and checked for gaps or sensor faults.

03

Model

Statistical and machine-learning models estimate near-term environmental conditions.

04

Evaluate

Forecasts are compared against configurable thresholds and historical baselines.

05

Alert

Qualifying conditions generate structured alerts routed to the relevant channel.

Operational Awareness Outputs

Awareness built for operators, not just data for analysts.

The output layer is designed around how conditions actually get used day to day: quick situational read, spatial context, timely notification, and a record to look back on.

Situational Summaries

Condensed, human-readable summaries of current and forecasted environmental state.

Geographic Overlays

Map-based views that place sensor data, forecasts, and alerts in spatial context.

Alert Delivery

Configurable notification channels for qualifying conditions and threshold breaches.

Historical Trend Views

Time-series views for tracking how conditions evolve against baseline patterns.

Exportable Reporting

Structured exports for downstream analysis, recordkeeping, or integration with other systems.

Adaptive Device Control

Closed-loop outputs that act directly on hardware — such as automated dew-heater regulation — not just notifications for a person to read.

Prototype Status

An active prototype, built in layers.

EDS is under active development. The system is being built layer by layer, with each stage validated against real test data before the next is added — rather than shipped as a single fixed release.

Data Ingestion Pipeline

Implemented

Core ingestion and normalization for initial sensing sources is in place and running against live test data.

Forecasting Models

In Progress

Baseline statistical forecasting is operational; machine-learning model refinement is ongoing.

Alerting & Delivery

Prototype

Threshold-based alert generation exists; delivery channels are being expanded and hardened.

Operator Interface

Early Stage

Awareness outputs are being designed as structured views rather than a fixed dashboard product.

Field Deployment

Planned

Structured pilot integrations with real sensing sites are the next milestone.

Note Interface captures and system diagrams will be published here as the prototype matures.

Collaboration

Technical collaboration on environmental sensing and decision support.

MDNT is open to focused collaboration on EDS: sensing integrations, forecasting model design, environmental data partnerships, and structured field pilots. If your work touches environmental monitoring, infrastructure operations, or applied environmental research, get in touch.

Sensing & data integrations
Forecasting & alerting model design
Environmental data partnerships
Field pilot opportunities

labs@mdnt.pt

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Environmental Decision Support System.