Rainfall impacts local water quantity and quality. Accurate and timely prediction of rainfall is highly desirable in water management and hydrogeologic hazards mitigation, which is critical for the environmentally sustainable development. Previous rainfall prediction processes are complex and computationally costly for its intrinsic high uncertainty and variability. In this paper, a data-driven approach is applied to predict the rainfall based on historic data via time-series modeling and optimally pruned extreme learning machine (OP-ELM). The rainfall datasets collected from six counties within Three Georges Reservoir are utilized as case studies in this research, first, the rainfall data is pre-processed with outlier removal and missing value imputation, and the monthly ahead difference is computed based on the instant average of monthly rainfall. Next, the autocorrelation function and partial autocorrelation function were computed and the Ljung–Box test statistic is utilized to explore the significance of the historic lagged-series. All the statistically significant historic lags are selected as inputs for prediction algorithms. Last, an OP-ELM algorithm is developed to predict the monthly rainfall with tenfold cross validation. Four activation functions: the sigmoid, sine, hardlim, and radial basis function, are considered in the OP-ELM. The prediction performance is evaluated with metrics including mean absolute error, mean absolute percentage error, root mean square error, and max error rate. Overall, the computational results indicate the proposed framework outperforms the other benchmarking machine learning algorithms through six case studies in Three Gorges Reservoir, China.

降雨会影响当地的水量和水质。在水管理和减轻水文地质灾害方面，准确，及时地预测降雨是非常必要的，这对于环境的可持续发展至关重要。先前的降雨预测过程由于其固有的高不确定性和可变性而非常复杂且计算量大。本文采用数据驱动的方法，通过历史数据通过时间序列建模和最佳修剪的极限学习机（OP-ELM）来预测降雨。本研究以案例研究为基础，从三乔治水库内六个县收集的降雨数据集进行了预处理，首先对降雨数据进行了异常值去除和缺失值估算，每月提前差是根据每月降雨量的即时平均值计算的。接下来，计算自相关函数和部分自相关函数，并利用Ljung-Box检验统计量来探索历史滞后级数的意义。选择所有具有统计意义的历史滞后作为预测算法的输入。最后，开发了OP-ELM算法，通过十倍交叉验证来预测月降雨量。OP-ELM中考虑了四个激活函数：S型，正弦，hardlim和径向基函数。使用包括平均绝对误差，平均绝对百分比误差，均方根误差和最大误差率在内的度量来评估预测性能。全面的，