Accurate groundwater depth forecasting is particularly important for human life and sustainable groundwater management in arid and semi‐arid areas. To improve the groundwater forecasting accuracy, in this paper, a hybrid groundwater depth forecasting model using configurational entropy spectral analyses (CESA) with the optimal input is constructed. An original groundwater depth series is decomposed into subseries of different frequencies using the variational mode decomposition (VMD) method. Cross‐correlation analysis and Shannon entropy methods are applied to select the optimal input series for the model. The ultimate forecasted values of the groundwater depth can be obtained from the various forecasted values of the selected series with the CESA model. The applicability of the hybrid model is verified using the groundwater depth data from four monitoring wells in the Xi'an of Northwest China. The forecasting accuracy of the models was evaluated based on the average relative error (RE), root mean square error (RMSE), correlation coefficient (R) and Nash‐Sutcliffe coefficient (NSE). The results indicated that comparing with the CESA and autoregressive model, the hybrid model has higher prediction performance.

中文翻译：

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最优输入的形态熵谱分析在地下水深度预测中的应用。

准确的地下水深度预测对于干旱和半干旱地区的人类生活和可持续的地下水管理尤其重要。为了提高地下水的预测精度，本文建立了一种基于最优熵的配置熵谱分析（CESA）混合地下水深度预测模型。使用变分模式分解（VMD）方法将原始地下水深度系列分解为不同频率的子系列。应用互相关分析和香农熵方法为模型选择最佳输入序列。可以使用CESA模型从所选序列的各种预测值中获得地下水深度的最终预测值。利用西北地区西安市4口监测井的地下水深度数据验证了该混合模型的适用性。根据平均相对误差（RE），均方根误差（RMSE），相关系数（R）和Nash-Sutcliffe系数（NSE）。结果表明，与CESA模型和自回归模型相比，混合模型具有更高的预测性能。