地下水位预测 | Groundwater Level Prediction

This skill predicts groundwater levels several steps ahead from historical water-level time series and driving factors (precipitation recharge, abstraction), then spatially interpolates the predictions into a regional raster. It is suited to scenarios such as groundwater dynamics analysis, over-extraction early warning, and water resource planning.

The core algorithm consists of three parts: time-series decomposition (a centered moving average extracts the trend component, periodic averaging extracts the seasonal component, and the remainder is the residual); driver regression / random forest (fits the water-level response using precipitation and its lagged recharge terms, abstraction, and the seasonal and trend components as features, then extrapolates --predict-steps months ahead); spatial interpolation (interpolates the predicted well-point water levels into a raster using inverse distance weighting, IDW). Uncertainty is characterized by the RMSE estimated with a temporal hold-out method and the prediction–truth correlation coefficient.

Dependencies / 依赖

pip install 'numpy' 'rasterio' 'scipy' 'scikit-learn'

Usage / 使用方法

Basic Usage

python geoskill-groundwater-level-prediction.py --bbox 116.0 39.0 117.0 40.0 --predict-steps 6

Example 1 (Synthetic Data, Offline)

python geoskill-groundwater-level-prediction.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out

Example 2 (Random Forest Method)

python geoskill-groundwater-level-prediction.py --bbox 116 39 117 40 --synthetic --method rf --predict-steps 12 --output-dir ./out

Example 3 (Longer Prediction Horizon)

python geoskill-groundwater-level-prediction.py --bbox 114 30 115 31 --synthetic --predict-steps 24 --quiet

Example 4 (Real Multi-temporal Water-Level Raster, band = Monthly Snapshot)

python geoskill-groundwater-level-prediction.py --input gwl_monthly.tif --predict-steps 6 --output-dir ./out

Example 5 (Custom Seasonal Period + Random Seed)

python geoskill-groundwater-level-prediction.py --bbox 121 31 122 32 --synthetic --period 12 --seed 7 --output-dir ./out

Output / 输出

Data Source / 数据源 / Source

• --input: local multi-temporal water-level GeoTIFF (each band is a monthly snapshot).
• --synthetic: physically consistent well-point time series + driving factors (precipitation/abstraction), fully offline.

Privacy / 隐私声明 / Privacy

• Runs offline by default; --synthetic mode requires no network at all.
• All processing is done locally; user data is never uploaded.

License / License

MIT

name: geoskill-groundwater-level-prediction description: '基于历史水位时序与降水/开采驱动因子，用回归或随机森林预测未来地下水位并做空间插值与不确定性评估'

地下水位预测 | Groundwater Level Prediction

本 skill 从历史地下水位时序与驱动因子（降水补给、开采量）出发，预测未来若干步的水位，并把预测结果空间插值为区域栅格。适用于地下水动态分析、超采区预警、水资源规划等场景。

核心算法包括三部分：时序分解（居中滑动平均提取趋势项、按周期叠加提取季节项、剩余为残差）；驱动回归 / 随机森林（以降水及其滞后补给项、开采量、季节项、趋势项为特征拟合水位响应，外推 --predict-steps 个月）；空间插值（把井点预测水位用反距离加权 IDW 插值为栅格）。不确定性由时间留出法估计的 RMSE 与预测—真值相关系数刻画。

依赖

pip install 'numpy' 'rasterio' 'scipy' 'scikit-learn'

使用方法

基本用法

python geoskill-groundwater-level-prediction.py --bbox 116.0 39.0 117.0 40.0 --predict-steps 6

示例 1（合成数据，离线）

python geoskill-groundwater-level-prediction.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out

示例 2（随机森林方法）

python geoskill-groundwater-level-prediction.py --bbox 116 39 117 40 --synthetic --method rf --predict-steps 12 --output-dir ./out

示例 3（更长的预测步长）

python geoskill-groundwater-level-prediction.py --bbox 114 30 115 31 --synthetic --predict-steps 24 --quiet

示例 4（真实多时相水位栅格，band = 月份快照）

python geoskill-groundwater-level-prediction.py --input gwl_monthly.tif --predict-steps 6 --output-dir ./out

示例 5（自定义季节周期 + 随机种子）

python geoskill-groundwater-level-prediction.py --bbox 121 31 122 32 --synthetic --period 12 --seed 7 --output-dir ./out

输出

数据源 / Source

• --input：本地多时相水位 GeoTIFF（每个 band 为一个月快照）。
• --synthetic：物理一致的井点时序 + 驱动因子（降水/开采），完全离线。

隐私声明 / Privacy

• 默认离线运行，--synthetic 模式完全无网络。
• 所有处理在本地完成，不上传用户数据。

License

MIT