Water resource shortage and groundwater over-exploitation have been the primary limiting factors on sustainable agriculture in North China. It is often required for the balance between "Grain Yield Stabilization and Groundwater Extraction Reduction". This study aims to allocate groundwater and surface water resources for the groundwater exploitation reduction and stable grain yield. A case study was taken as the Shijin Irrigation District, Hebei Province, China. A process model was developed to strategically couple three models: SWAP, MODFLOW, and WEAP. A series of simulations were conducted to integrate with the water cycle. The framework was used to effectively capture the dynamic interactions between surface water, soil water, and groundwater. Four scenarios were designed for comparative analysis: the Current Scenario (S0), the Water-saving Irrigation Scenario (S1), the Planting Structure Adjustment Scenario (S2), and the Comprehensive Regulation Scenario (S3). The long-term simulations were then performed on the groundwater levels and crop yields from 2025 to 2036. An optimal allocation of multiple source water was formulated under these scenarios. The results indicated that the reliable performance was achieved to simulate the groundwater level, soil moisture content, and crop yield, with the determination coefficients (R²) of over 0.58. Compared with the Current Scenario (S0), the Water-saving Irrigation Scenario (S1) demonstrated the significant benefits: The annual water saving of the irrigation district reached 72 million m³, the amount of groundwater extraction reduction reached 46 million m³, while the groundwater level over the irrigation district raised cumulatively by approximately 3.5 m over the simulation period, and the total grain yield fluctuated within the range of 1.056 billion~1.126 billion kg. In the Planting Structure Adjustment Scenario (S2), the proportion of grain crops decreased from 70% to 60%, while the groundwater level exhibited a slow upward trend, and the grain yield remained stable at over 1 billion kg. Water-saving irrigation and adjustments were systematically integrated into various cropping patterns. An optimal mode was obtained suitable for the multiple source water allocation in the Shijin Irrigation District. Surface water was prioritized to supply from the Gangnan and Huangbizhuang reservoirs, whereas the groundwater served as a supplementary source. Compared with the current water allocation, the optimal mode raised the overall groundwater level of the irrigation district by about 4.68 m, and the fluctuation range of total grain yield was only 34 million kg, indicating both groundwater extraction reduction and grain yield stabilization. The research findings can provide the effective modeling on the water resources for the water-saving transformation and control of groundwater over-exploitation in similar multi-water-source irrigation districts in North China.

The channel sluice can quickly remove the flood into the canal in the irrigation area. In order to provide a simple and efficient method for flood control scheduling decision of drainage sluice in irrigation area, this study took Pishihang Irrigation District as an example to establish a prediction model with dispatched flow as the target variable and 10 characteristic variables as independent variables. The 10 variables were the water level and rainfall of drainage sluice at irrigation mouth: rainfall in the past 1 hour, 2 hour, 3 hour, 6 hour, and 9 hour and rainfall in the future 1 hour, 3 hour and 6 hour, water level on the gates of the Guan Kouji drainage gate, difference in water level at the gate in the past half hour.The prediction accuracy of 8 machine learning algorithms was compared to pick the best algorithm. The Shapley Additive exPlanations（SHAP）method was used to analyze the importance of 10 groups of variables, and the influence weights of different variables on the prediction results were obtained. By comparing the prediction error indicators of the optimal algorithm under different variable combinations, the optimal variable combinations were selected, and the accuracy of the algorithm was further optimized to determine the final scheduling flow decision model. The results showed that: 1) The integrated learning algorithm was better than the traditional regression algorithm in predicting the evaluation index. The order of prediction accuracy of ensemble learning algorithms was as follows: random forest regression （RFR）>extrme gradient boosting regression (XGR) >adapative bossting regression (ABR)>spoort vector regression (SVR), and Bagging had the highest accuracy in the three categories of ensemble learning algorithms. RFR had the highest prediction accuracy among the 8 machine learning algorithms (the root mean square error, mean absolute error, mean square error and determination coefficient of the training set were 0.146, 0.094, 0.021 m³/s and 0.976, respectively. The root-mean-square error, mean absolute error and mean square error of the test set were 0.306, 0.197, 0.093 m³/s and 0.931, respectively); 2) The importance values of characteristic variables determined by SHAP method were as follows in descending order: water level on the gates of the Guan Kouji drainage gate, rainfall in the past 9 hour, rainfall in the future 6 hour, rainfall in the past 6 hour, rainfall in the past 3 hour, rainfall in the past 2 hour, rainfall in the future 1 hour, rainfall in the past 1 hour, difference in water level at the gate in the past half hour and rainfall in the future 3 hour. The water level on the gates of the Guan Kouji drainage gate had the greatest influence on the prediction results of the drainage flow, accounting for 34.6% of the total importance values of the features. The total importance values of the rainfall features in the past period were 0.473, and the total importance values of the rainfall features in the future period were 0.287. The influence degree of the rainfall in the past period was greater than that of the rainfall in the future. 3) The RFR algorithm with the input variables of past 6 hour rainfall, past 9 hour rainfall, future 6 hour rainfall and the water level on the gates of the Guan Kouji drainage gate was the best to predict the dispatching flow of the sluice gate (The model error indexes were as follows: root mean square error, mean absolute error, mean square error and determination coefficient of training set are 0.126, 0.080, 0.016 m³/s and 0.982, respectively; The root mean square error, mean absolute error, mean square error and determination coefficient of the test set were 0.263, 0.164, 0.069 m³/s and 0.950, respectively. The determination coefficients of the training set and the test set were increased by 0.6% and 2.0%, respectively, compared with all the characteristic variables. The root-mean-square error, mean absolute error and mean square error were reduced by 13.7%, 14.9%, 23.8%, 14.1%, 16.3% and 25.8%, respectively, compared with all the characteristic variables. It can be seen that variable selection has a significant impact on the prediction accuracy. This study avoids the multi-source data collection and complex operation required by the coupling mechanism model, and provides technical support for the irrigation district management agency to scientifically dispatch the sluice of each channel. The research results were of great significance for realizing the modernization of irrigation district.