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首页> 外文期刊>Journal of Hydroinformatics >Semi-seasonal groundwater forecast using multiple data-driven models in an irrigated cropland
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Semi-seasonal groundwater forecast using multiple data-driven models in an irrigated cropland

机译：在灌溉农田中使用多种数据驱动模型进行半季节地下水预测

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摘要

In agricultural areas where groundwater is the main water supply for irrigation, forecasts of the water table are key to optimal water management. However, water management can be constrained by semi-seasonal to seasonal forecast. The objective is to create an ensemble of water table one- to five-month lead-time forecasts based on multiple data-driven models (DDMs). We hypothesize that data-driven modeling capabilities (e.g., random forests, support vector machines, artificial neural networks (ANNs), extreme learning machines, and genetic programming) will improve naive and autoregressive simulations of groundwater tables. An input variable selection method was used to carry the maximum information in the input (precipitation, crop water demand, changes in groundwater table, snowmelt, and evapotranspiration) and output relationship for the DDMs. Five DDMs were implemented to forecast groundwater tables in an unconfined aquifer in the Northern High Plains (Nebraska, USA). Root mean squared error (RMSE) and Nash-Sutcliffe efficiency index were used to evaluate the skill of the model and three hydrologic regimes were determined statistically as high, mid, and low groundwater table levels. Additionally, varying storage regimes were used to construct rising and falling limbs in the tested well. Results show that all models outperform the baseline for all the lead times, ANNs being the best of all.
机译：在地下水是灌溉主要水源的农业地区，对地下水位的预测是优化水管理的关键。但是，水资源管理可能会受到半季节到季节预报的限制。目的是创建基于多个数据驱动模型（DDM）的地下水位一到五个月提前期预测的整体。我们假设数据驱动的建模功能（例如，随机森林，支持向量机，人工神经网络（ANN），极限学习机和遗传编程）将改善地下水位的幼稚和自回归模拟。输入变量选择方法用于在DDM的输入（降水量，作物需水量，地下水位变化，融雪和蒸散量）和输出关系中携带最大信息。实施了五种DDM，以预测北部高平原（美国内布拉斯加州）的无限制含水层中的地下水位。均方根误差（RMSE）和Nash-Sutcliffe效率指数用于评估模型的技能，并统计确定了三种地下水状况，分别为地下水位高，中和低。另外，使用不同的存储方式来构造测试井中的上升和下降分支。结果表明，所有模型的交货时间均超过基线，而人工神经网络是所有模型中最好的。

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