Studies on climate change have largely overlooked the delayed response of Ground Water Levels (GWL) to atmospheric conditions. This gap is critical because fluctuations in GWL can lead to hazards like land subsidence. This study addresses the issue by identifying optimal delay times for key variables, which improves GWL projection accuracy. The input data process consists of introducing meteorological and hydrological variables in the form of 42 combinations. Meteorological data under climate change scenarios were obtained by downscaling outputs from the General Circulation Models (GCMs) within the Shared Socio-economic Pathways (SSP) scenarios. So far, no similar study has attempted to rank such a wide array of delay time combinations. This study improves hybrid Random Forest and Genetic Algorithm (RF-GA) projections by introducing the best combination of input variables. The investigation assessed the performance of both the conventional Random Forest (RF) and the RF-GA in simulating groundwater fluctuations. The variable sensitivity analysis results indicated that watershed discharge holds a higher Variable Importance (VI) compared to meteorological variables. The findings in the validation section also demonstrated that the RF-GA outperformed an RF that runs on default hyperparameters. Temperature and evaporation show a 3 and 2-month delay time, respectively. It was discovered that precipitation was the only variable with two possible delay times of 2 and 4-month. Also, combinations with many and few variables performed poorly. The projection results indicate an increase of 6.8 and 7.1 cm in the average GWL in the Silakhor plain under the low-emission SSP1-2.6 and high-emission SSP5-8.5 scenarios, respectively.

中文翻译：

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基于混合机器学习算法的气候变化下地下水位预测的可变敏感性分析


关于气候变化的研究在很大程度上忽视了地下水位 （GWL） 对大气条件的延迟响应。这个差距很关键，因为 GWL 的波动会导致地面沉降等危险。本研究通过确定关键变量的最佳延迟时间来解决这个问题，从而提高 GWL 投影的准确性。输入数据过程包括以 42 种组合的形式引入气象和水文变量。气候变化情景下的气象数据是通过在共享社会经济路径 （SSP） 情景中缩小一般环流模型 （GCM） 的输出来获得的。到目前为止，还没有类似的研究试图对如此广泛的延迟时间组合进行排序。本研究通过引入输入变量的最佳组合来改进混合随机森林和遗传算法 （RF-GA） 投影。该调查评估了传统的随机森林 （RF） 和 RF-GA 在模拟地下水波动方面的性能。变量敏感性分析结果表明，与气象变量相比，流域流量具有更高的变量重要性 （VI）。验证部分的发现还表明，RF-GA 的性能优于在默认超参数上运行的 RF。温度和蒸发分别显示 3 个月和 2 个月的延迟时间。结果发现，降水是唯一具有 2 个月和 4 个月两种可能延迟时间的变量。此外，具有许多变量和少量变量的组合表现不佳。预测结果表明，在低排放 SSP1-2.6 和高排放 SSP5-8.5 情景下，Silakhor 平原的平均 GWL 分别增加了 6.8 和 7.1 cm。