# Aethera — Technical Project Report

Document ID: MF-2026-04-TPR-01  
Status: development draft; local prototype evidence only  
Institution: MindforgeAI · Chatake Innoworks Private Limited

## Executive summary

Aethera is an AI/ML-based water-resource intelligence and distribution-planning system under development. Its intended purpose is to make water supply and demand legible together: rainfall, reservoir storage and seasonal context on the supply side; population, cities, villages, agriculture and industry on the demand side.

The current deliverable is a local development prototype. It contains a FastAPI service, a browser-based scenario interface, a deterministic synthetic-data generator, two local scikit-learn random-forest regressors and an illustrative allocation view. It is not connected to live reservoirs, government datasets or any water-distribution authority.

## 1. Problem definition

Water availability is spatial and seasonal while demand is distributed across domestic, agricultural and industrial use. A planning system therefore needs to preserve the distinction between measured inputs, estimated demand, forecast/model output and human allocation decisions.

### Objective

Design a traceable engineering pathway from water-resource inputs to a reviewable supply-demand balance and distribution scenario.

### Current boundaries

- no real reservoir measurements;
- no government data source;
- no municipal, village, industrial or agricultural deployment;
- no prediction-accuracy or optimisation-performance claim;
- no operational allocation recommendation.

## 2. Proposed system model

```text
Supply inputs                     Demand inputs
rainfall                          population
reservoir storage                 cities / villages
seasonal context                  agriculture / industry
        \                              /
         \                            /
          feature preparation and model boundary
                         |
                supply + demand estimates
                         |
                supply-demand balance
                         |
          transparent allocation scenario for review
```

## 3. Local prototype implementation

| Layer | Local implementation | Evidence state |
|---|---|---|
| Public/product layer | Static HTML, CSS and JavaScript microsite | Implemented |
| Application layer | Browser scenario form and allocation/reservoir views | Implemented |
| Service layer | FastAPI health, metadata and scenario endpoints | Implemented |
| Data layer | Deterministic generated examples in process memory | Synthetic demonstration data only |
| ML layer | Two `RandomForestRegressor` models for supply and demand estimates | Implemented locally; synthetic training only |
| Allocation view | Transparent weighted demonstration rule | Implemented; not an optimisation or authority |

## 4. Data and modelling method

The prototype uses a fixed random seed (`2026`) to generate 780 synthetic examples. Features are rainfall index, reservoir-storage index, population index, agriculture index, industry index and season-derived supply/demand factors. Separate random-forest regressors are trained for a synthetic supply target and a synthetic demand target.

The units shown in the interface are **demonstration units**, not litres, cubic metres, live storage volumes or reported statistics. The method has not been calibrated or evaluated against field data and must not be interpreted as a hydrological model.

## 5. Scenario workflow

1. A user selects a season and adjusts synthetic input indexes.
2. The service builds a feature vector.
3. The two local models estimate synthetic supply and demand.
4. The application displays a balance and a weighted sector allocation view.
5. The result remains a human-review prompt, not a distribution instruction.

## 6. Verification record

| Check | Result | Limitation |
|---|---|---|
| Deterministic scenario evaluation | Passes local assertions | Does not establish scientific validity |
| Higher storage index yields higher synthetic supply in tested cases | Passes local assertion | Relationship exists only in generated demonstration data |
| FastAPI health and scenario endpoints | Reachable locally | No production runtime or authentication |
| Browser application | Connects to local scenario endpoint | Not tested as a field/mobile deployment |

## 7. Future engineering gates

1. recover and reconcile the canonical student implementation, if available;
2. establish approved real-data sources, licences and data cards;
3. define hydrological, demand and allocation baselines;
4. hold out validation data and pre-register evaluation metrics;
5. add uncertainty, fairness, priority-policy and human-oversight design;
6. complete security, privacy, reliability and deployment review before any operational use.

## 8. Conclusion

Aethera now has a reproducible local development baseline and a public technical story, without converting its intended capability into an unsupported operational claim. Results, real-data conclusions and deployment assertions remain pending the evidence gates above.

## References and appendices

Add reviewed hydrology, demand-modelling, optimisation, public-data and policy sources here before research submission. Retain dataset cards, experiment configurations, model cards, API contract, test record and change log with the future evidence package.