Accurate forecast of hydrological data such as precipitation is critical in order to provide useful information for water resources management, playing a key role in different sectors. Traditional forecasting methods present many limitations due to the high-stochastic property of precipitation and its strong variability in time and space: not identifying non-linear dynamics or not solving the instability of local weather situations. In this work, several alternative models based on the combination of wavelet analysis (multiscalar decomposition) with artificial neural networks have been developed and evaluated at sixteen locations in Southern Spain (semiarid region of Andalusia), representative of different climatic and geographical conditions. Based on the capability of wavelets to describe non-linear signals, ten wavelet neural network models (WNN) have been applied to predict monthly precipitation by using short-term thermo-pluviometric time series. Overall, the forecasting results show differences between the ten models, although an effective performance (i.e., correlation coefficients ranged from 0.76 to 0.90 and Root Mean Square Error values ranged from 6.79 to 29.82 mm) was obtained at each of the locations assessed. The most appropriate input variables to obtain the best forecasts are analyzed, according to the geo-climatic characteristics of the sixteen sites studied.

中文翻译：

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在半干旱环境中使用小波神经网络模型进行月降水预报

准确预测降水等水文数据对于为水资源管理提供有用信息至关重要，在不同部门发挥关键作用。由于降水的高度随机性及其在时空上的强烈可变性，传统的预报方法存在许多局限性：不能识别非线性动力学或不能解决当地天气情况的不稳定性。在这项工作中，在西班牙南部（安达卢西亚半干旱地区）的 16 个地点开发和评估了几种基于小波分析（多标量分解）与人工神经网络相结合的替代模型，这些地点代表了不同的气候和地理条件。基于小波描述非线性信号的能力，十个小波神经网络模型 (WNN) 已被应用于通过使用短期热雨量时间序列预测月降水量。总体而言，预测结果显示了十个模型之间的差异，尽管在每个评估位置都获得了有效性能（即相关系数范围为 0.76 至 0.90，均方根误差值范围为 6.79 至 29.82 毫米）。根据所研究的 16 个站点的地理气候特征，分析了获得最佳预测的最合适的输入变量。90 和均方根误差值范围从 6.79 到 29.82 毫米）是在每个评估位置获得的。根据所研究的 16 个站点的地理气候特征，分析了获得最佳预测的最合适的输入变量。90 和均方根误差值范围从 6.79 到 29.82 毫米）是在每个评估位置获得的。根据所研究的 16 个站点的地理气候特征，分析了获得最佳预测的最合适的输入变量。