pyreclaim 0.1.0

Reservoir Estimation of Capacity Loss using AI based Methods

Reservoir Estimation of Capacity Loss using AI-based Methods - RECLAIM

First-of-its-kind globally scalable tool to predict reservoir sedimentation, screen vulnerable reservoirs, and pinpoint those struggling the most.

RECLAIM (Reservoir Estimation of Capacity Loss using AI-based Methods) is a globally scalable machine learning framework to predict absolute sedimentation rates in reservoirs using observed records and multi-decadal satellite-based Earth observations.

It is the first-of-its-kind tool to:

• Rapidly assess sedimentation risk
• Provide scalable predictions across diverse climates and geographies
• Enable cost-effective screening to prioritize reservoirs for detailed surveys
• Support decision-making for mitigation interventions where they are most needed

Note: RECLAIM is designed as a screening tool, not a replacement for field surveys. It helps guide where and when to act first.

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Installation

Install the package via pip:

pip install reclaim

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Documentation

The documentation is available here.

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Quick Start / Example Workflow

This example shows generating features, loading the pretrained model, predicting sedimentation rates, and evaluating results.

from reclaim.generate_features import create_features_per_row
from reclaim.reclaim import Reclaim

# Step 1: Generate features for a reservoir
reservoir_static = {
    "obc": 150.0,
    "hgt": 45.0,
    "mrb": "Ganges",
    "lat": 25.6,
    "lon": 81.9,
    "reservoir_polygon": reservoir_polygon,
    "aec_df": aec_df
}

catchment_static = {
    "ca": 1200,
    "dca": 50,
    "catchment_geometry": catchment_geom,
    "glc_share_path": "data/glc.nc",
    "hwsd2_path": "data/soil.nc",
    "hilda_veg_freq_path": "data/veg.nc",
    "terrain_path": "data/terrain.nc"
}

features = create_features_per_row(
    reservoir_static_params=reservoir_static,
    catchment_static_params=catchment_static,
    observation_period=[2000, 2020]
)

# Step 2: Load pretrained RECLAIM model
model = Reclaim()
model.load_model()  # loads XGBoost, LightGBM, CatBoost, and metadata

# Step 3: Predict sedimentation rates using ensemble
pred_sr, weights = model.predict(features, return_weights=True)

# Step 4: Inspect predictions
print(pred_sr)
print(weights)

# Step 5: Evaluate (if ground truth available)
y_true = [...]  # replace with true sedimentation rates
metrics = model.evaluate(features, y_true)
print(metrics)  # {'RMSE': ..., 'MAE': ..., 'R2': ...}

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Citation

If you use RECLAIM in your work, please cite:

Minocha, S., Hossain, F., Zhao, J., & Istanbulluoglu, E.
RECLAIM: A Globally Scalable Machine Learning Framework to Predict Reservoir Sedimentation and Capacity Loss from Satellite-based Earth Observations (Submitted to Environmental Modelling and Software (EMS)).

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License

RECLAIM 3.0 is distributed under the GPL v3 license. You may copy, distribute, and modify the software as long as you track changes/dates in source files. Any modifications or software including GPL-licensed code must also be made available under GPL along with build & install instructions.
For more information, see LICENSE.