Efficient prediction method for spatio-temporal distribution of groundwater level at grid scale in large-scale agricultural irrigation area

By constructing a SWAT-MODFLOW model and combining it with a parallel computation LSTM model, and employing spatial compression and reconstruction techniques, the problem of efficiency and accuracy in predicting groundwater levels in large-scale agricultural irrigation areas was solved, achieving efficient and accurate groundwater level prediction.

CN120781025BActive Publication Date: 2025-11-18DALIAN UNIV OF TECH +1
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Patent Information

Application Number
CN202511294449.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-09-11
Publication Date
2025-11-18
Estimated Expiration
2045-09-11

AI Technical Summary

Technical Problem

Existing technologies cannot improve the prediction efficiency of models while ensuring the accuracy of groundwater level prediction in large-scale agricultural irrigation areas. The MODFLOW model has high computational cost, and machine learning models have poor prediction accuracy under the influence of heterogeneous factors.

Method used

By constructing a SWAT-MODFLOW model, combining it with parallel computation of an LSTM model, and employing spatial compression and reconstruction techniques, a one-to-one modeling of the control grid is determined, enabling efficient prediction of groundwater levels at the grid scale in irrigation areas.

Benefits of technology

While maintaining accuracy and resolution, it significantly improves computational efficiency, substantially enhances training and prediction accuracy, and reduces computational costs.

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Abstract

The application provides a large-scale agricultural irrigation area grid scale groundwater level space-time distribution efficient prediction method, belongs to the technical field of groundwater prediction, and steps include: firstly, a SWAT model of a basin where an irrigation area is located and a MODFLOW model covering the irrigation area are respectively constructed, the two are coupled to obtain a SWAT-MODFLOW model, and parameter correction is carried out; secondly, an irrigation area groundwater level data set is constructed through scenario combination and model simulation, is used for training and screening the highest-precision LSTM model, and the optimal control range of each LSTM model is determined through space compression and reconstruction, and multiple LSTM models are obtained; finally, the multiple LSTM models are parallelly calculated to predict the space-time distribution of the grid scale groundwater level of the irrigation area. The optimal control range of each LSTM model is determined through space compression-reconstruction, finally, the prediction of the space-time distribution of the grid scale groundwater level of the large-scale agricultural irrigation area is realized through the parallel calculation of the multiple LSTM models, and the prediction efficiency is improved on the premise of ensuring the prediction accuracy of the irrigation area groundwater level.
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