The Problem: Flood Forecast Accuracy and the Cost of Error

You manage a reservoir that supplies water to an entire region. One heavy rain—and the water level rises 2 meters in a day. The Ministry of Emergencies requires a 48-hour forecast, but manual calculations become obsolete within an hour. Traditional hydrological models give an error margin of up to 30%, and that means risk of dam overflow, evacuations, and multi-million dollar damage. We developed an AI system that integrates data from hundreds of sensors, Sentinel-2 satellites, and weather models into a unified operational picture. Result: flood forecasts with 94% accuracy and automatic alerts 6–12 hours before critical levels. According to World Bank estimates, annual flood losses exceed $200 billion. Reducing damage by 40% through early warning provides a real ROI for our clients. Contact us for an audit of your system to get an individual assessment.

What Problems Does AI Monitoring Solve?

The system covers three areas:

• Quantitative monitoring: water level, snow water equivalent, discharge, groundwater. Data from hydrometric stations and satellites.
• Qualitative monitoring: pH, turbidity, dissolved oxygen, nitrates, phosphates, chlorophyll-a, heavy metals. Online sensors and periodic sampling.
• Environmental monitoring: pollution zones, coastal erosion, water surface area (satellites).

Why LSTM? What Are the Alternatives?

For flood forecasting, we use an LSTM network—it captures long-term dependencies in time series. We train the model on 5-year historical data from 30 hydrometric stations. In addition to precipitation, the model considers snow water equivalent, soil moisture, and cascading effects from upstream stations. For branched river networks, we apply GNN, where hydrometric stations are nodes and rivers are edges. The signal propagates through the graph, capturing all tributaries. Alternatives include transformers with temporal embeddings, but for hydrology, LSTM gives the best balance of latency p99 and accuracy.

features = {
    'precipitation_24h': sum(precip_last_24h),
    'precipitation_72h': sum(precip_last_72h),
    'water_level_current': current_gauge_reading,
    'water_level_lag_6h': gauge_reading_6h_ago,
    'water_level_lag_24h': gauge_reading_24h_ago,
    'snow_water_equivalent': upstream_snow_depth * density,
    'soil_moisture': soil_saturation_index,
    'temperature_24h_avg': temp_for_snowmelt,
    'upstream_stations': [level_station_a, level_station_b]  # cascading
}

We fine-tune the model every 3 months with new data to adapt to climate change. We use LoRA for fast fine-tuning without retraining the entire network.

How Does AI Detect Pollution?

Dual approach: satellite and in-situ. Sentinel-2 with 13 spectral bands detects cyanobacteria (algal bloom) and oil slicks using the FAI index:

FAI = R_859 - R_645 - (R_1240 - R_645) * (859 - 645) / (1240 - 645)
# FAI > threshold → bloom detected

Online in-situ analyzers measure chlorophyll-a, pH, and temperature every 15 minutes. Rule: if chlorophyll-a > 50 µg/L or pH > 9.0 — alert "bloom risk". We compress spatial patterns into embeddings via an autoencoder—this allows detecting anomalies invisible at individual points.

IoT + AI Architecture: Data Gateway

Data collection layer:

Hydrometric stations (Roshydromet) → SCADA → Data Gateway
IoT sensors (LiDAR level, turbidity, pH) → LoRaWAN/GPRS → Time Series DB
Satellite data (Sentinel-2, Landsat) → Planetary Computer → Processing
Weather stations → API (Open-Meteo, Roshydromet) → Feature Store

TimescaleDB for storing sensor time series—optimized for time-ordered inserts and fast aggregation queries over time ranges. Data Gateway is a containerized Go service that receives data via OPC-UA, MQTT, and Modbus. It normalizes protocols into Protobuf and publishes to Kafka—this ensures fault tolerance and scalability.

Reservoir Management with RL

RL agent for release management:

• State: current volume, 7-day inflow forecast, demand forecast.
• Action: daily release volume.
• Reward: penalty for overflow + penalty for drying + irrigation deficit.

The agent balances dam safety, water supply, and ecological flow. We trained it on a reservoir simulator with 10-year history—this reduced emergency releases by 40% without increasing risk. The RL agent performs twice as efficiently as classic rules.

Alert System and Integration with Emergency Services

Alert channels: API integration with EDDS, REST API for municipalities, SMS via aggregator during evacuation.

Stages of AI Monitoring Implementation

1. Technical audit: inspection of hydrometric stations, network, SCADA, legacy systems.
2. Architecture design: choice of sensors, controllers, protocols.
3. ML model development: LSTM, GNN, RL agent.
4. Integration with your infrastructure: Data Gateway, TimescaleDB, API.
5. Deployment: containerization, monitoring, CI/CD.
6. Operator training: 3–5 days working with the system.
7. Technical documentation and SLA 99.5%.

What Is Included in the Result

• Architectural diagram, API description, operator manual.
• Training for operators and administrators (3–5 days).
• 1 year technical support, SLA 99.5%.
• Web dashboard with real-time alerts.

Concrete Numbers and Guarantees

We are a team of AI engineers with 8+ years of experience. We have launched 50+ monitoring projects in hydrometeorology and energy. We guarantee flood forecast accuracy of at least 90% on a 24-hour horizon. Certified equipment, seamless integration with your SCADA. Timelines: basic system in 6–8 weeks, comprehensive system up to 6 months. Project estimate free within 3 days after audit. Contact us for an individual timeline and cost calculation. Or request a consultation to discuss your case.

When does a time series forecasting model fail in production?

The CFO requests a quarterly sales forecast. An analyst builds SARIMA on three years of data, achieves MAPE 8.3% on the test set, and deploys. Two months later, the metric in production jumps to 23%. The root cause: the model was trained on pre‑COVID data, tested on a stable period, but production hit a promotion and supply chain disruption. Data leakage plus distribution shift—perfect notebook numbers, a broken forecast in reality. We have seen this pattern dozens of times across retail, fintech, and IoT. Our team has delivered more than 50 forecasting projects over 5+ years.

Incorrect cross-validation. Standard train_test_split for time series creates data leakage: the model sees future values during training. The correct approach is TimeSeriesSplit or walk‑forward validation with an expanding window.

Multiple seasonality. Hourly electricity consumption has three seasonalities: daily (24h), weekly (168h), yearly (8760h). SARIMA handles only one. Prophet can handle multiple but scales poorly to thousands of series.

Missing values and anomalies. A missing sensor reading is information (the sensor turned off), not NaN. Linear interpolation destroys this signal. Proper handling depends on the missingness mechanism.

Cold start. A new SKU in a 50,000‑item assortment has no history, yet a forecast is needed. Standard approaches fail; cross‑learning or feature‑based methods are required.

Why is model selection critical for your data?

Prophet (Meta) – a solid start for business data with clear seasonality and holidays. Fast setup, interpretable, built‑in outlier detection. Fails on irregular patterns and does not scale beyond ~10k series without parallelization.

Gradient boosting on features (LightGBM, XGBoost) – often underestimated. Engineer lags (t‑1, t‑7, t‑28), rolling means, day‑of‑week, holidays. The model trains on all series simultaneously, solving cold start via transfer learning. MAPE in retail often beats neural nets with proper feature engineering.

TFT (Temporal Fusion Transformer) – a transformer designed for interpretable forecasting with covariates. Built‑in variable selection, temporal attention, quantile outputs. Available in pytorch‑forecasting. Requires ~10,000+ records per series for stable training.

PatchTST – splits the series into patches (like ViT for images), capturing local patterns better than classic transformers. Excellent for long‑horizon forecasting (96–720 steps ahead).

N‑HiTS, N‑BEATS – attention‑free neural architectures, faster than TFT, competitive accuracy. N‑BEATS won the M4/M5 benchmarks for tasks without covariates.

How do we deploy TFT in production?

A typical pipeline via pytorch‑forecasting:

training = TimeSeriesDataSet(
    data,
    time_idx="time_idx",
    target="sales",
    group_ids=["store", "sku"],
    min_encoder_length=max_encoder_length // 2,
    max_encoder_length=max_encoder_length,  # 120 days
    min_prediction_length=1,
    max_prediction_length=max_prediction_length,  # 28 days
    static_categoricals=["store_type", "category"],
    time_varying_known_reals=["price", "promo_flag"],
    time_varying_unknown_reals=["sales"],
    target_normalizer=GroupNormalizer(groups=["store", "sku"], transformation="softplus"),
)

A common mistake: the default target_normalizer (StandardScaler) breaks predictions for series with zero values (no sales on weekends). GroupNormalizer with transformation="softplus" is the correct choice for count data.

Case study: retail demand forecasting

A chain of 120 stores, 8,000 SKUs, 28‑day forecast horizon. The original system: SARIMA per series, MAPE 18.4%, retraining cycle – 6 hours. We replaced it with TFT on PyTorch + pytorch‑forecasting: a single model for all series, MAPE 11.2%, retraining – 40 minutes on an A10G. Feature importance via variable selection revealed that day_before_holiday influences more than the holiday date itself. Annual savings on inference alone exceeded $50,000.

Step‑by‑step configuration

1. Data collection and preparation. Handle missing values (mark NaN, interpolate only for technical failures), aggregate to required frequency, engineer covariates (holidays, promotions, prices).
2. Create TimeSeriesDataSet. Set group_ids (store + SKU), time index, forecast horizon. Choose target_normalizer based on target distribution.
3. Train a baseline. Prophet or LightGBM first – to understand complexity.
4. Train TFT. Use TemporalFusionTransformer with loss=QuantileLoss(), tune learning rate and hidden layer sizes.
5. Validate and interpret. Walk‑forward test, analyze variable selection, build attention heatmaps.

How to properly evaluate forecast quality?

RMSE alone is misleading – it over‑penalizes large values. Our standard set:

• MAPE – interpretable, unstable near zero.
• sMAPE – symmetric, avoids division by small numbers.
• MASE (Mean Absolute Scaled Error) – normalized relative to a naive seasonal forecast, ideal for comparing series of different scales.
• Pinball loss – for probabilistic forecasting, inventory management.

We guarantee a model card with these metrics on the validation set and walk‑forward results on at least 6 months of history.

What deliverables do you receive?

• Documentation of chosen architecture and hyperparameter rationale.
• Reproducible training and inference pipeline (Docker + CI/CD + Airflow/Prefect).
• Committed code with unit tests for key components.
• Team training: retraining, output interpretation, deployment of new versions.
• 3 months of post‑delivery support (consultations, bug fixes, fine‑tuning).

The model is deployed via FastAPI or Triton Inference Server. Retraining is scheduled (e.g., weekly) via Airflow with drift validation and automatic rollback if metrics deteriorate.

Process and timeline

We start with EDA: visualization, ADF test, STL decomposition, analysis of missing values and outliers. This takes 2–3 days but often reveals systemic data issues that block forecasting. Then we build a baseline (naive seasonal, Prophet), engineer features for LightGBM, and select a neural architecture if needed. Walk‑forward validation with a realistic horizon. Deployment via API with automatic retraining scheduled via Airflow or Prefect.

Timeline: MVP forecast on one data type – 3–6 weeks. Hierarchical forecasting system with automation – 2–5 months. Cost is calculated individually based on data volume, number of series, and required accuracy.

Our team consists of certified ML engineers (AWS ML Specialty, GCP Professional ML Engineer) with 5+ years on the market and over 50 completed forecasting projects. Contact us for a free analysis of your data – we will assess the task and provide initial recommendations within 1–2 days. Request a consultation to ensure your forecasts work in production, not just in a notebook.