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Rainfall and runoff time-series trend analysis using LSTM recurrent neural network and wavelet neural network with satellite-based meteorological data: case study of Nzoia hydrologic basin
Complex & Intelligent Systems ( IF 5.8 ) Pub Date : 2021-04-15 , DOI: 10.1007/s40747-021-00365-2
Yashon O. Ouma , Rodrick Cheruyot , Alice N. Wachera

This study compares LSTM neural network and wavelet neural network (WNN) for spatio-temporal prediction of rainfall and runoff time-series trends in scarcely gauged hydrologic basins. Using long-term in situ observed data for 30 years (1980–2009) from ten rain gauge stations and three discharge measurement stations, the rainfall and runoff trends in the Nzoia River basin are predicted through satellite-based meteorological data comprising of: precipitation, mean temperature, relative humidity, wind speed and solar radiation. The prediction modelling was carried out in three sub-basins corresponding to the three discharge stations. LSTM and WNN were implemented with the same deep learning topological structure consisting of 4 hidden layers, each with 30 neurons. In the prediction of the basin runoff with the five meteorological parameters using LSTM and WNN, both models performed well with respective R2 values of 0.8967 and 0.8820. The MAE and RMSE measures for LSTM and WNN predictions ranged between 11–13 m3/s for the mean monthly runoff prediction. With the satellite-based meteorological data, LSTM predicted the mean monthly rainfall within the basin with R2 = 0.8610 as compared to R2 = 0.7825 using WNN. The MAE for mean monthly rainfall trend prediction was between 9 and 11 mm, while the RMSE varied between 15 and 21 mm. The performance of the models improved with increase in the number of input parameters, which corresponded to the size of the sub-basin. In terms of the computational time, both models converged at the lowest RMSE at nearly the same number of epochs, with WNN taking slightly longer to attain the minimum RMSE. The study shows that in hydrologic basins with scarce meteorological and hydrological monitoring networks, the use satellite-based meteorological data in deep learning neural network models are suitable for spatial and temporal analysis of rainfall and runoff trends.



中文翻译:

基于卫星气象数据的LSTM递归神经网络和小波神经网络的降雨和径流时间序列趋势分析:以恩佐亚水文盆地为例

这项研究将LSTM神经网络和小波神经网络(WNN)进行了比较,以在时空稀少的水文盆地中对降雨和径流时间序列趋势进行时空预测。利用来自10个雨量计站和3个流量测量站的30年(1980-2009年)长期实地观测数据,通过卫星气象数据预测了恩佐亚河流域的降雨和径流趋势,这些数据包括:降水,平均温度,相对湿度,风速和太阳辐射。预测模型是在与三个排放站相对应的三个子流域中进行的。LSTM和WNN具有相同的深度学习拓扑结构,该结构由4个隐藏层组成,每个隐藏层包含30个神经元。R 2值为0.8967和0.8820。对LSTM和WNN预测的MAE和RMSE度量值在月平均径流量预测中介于11–13 m 3 / s之间。利用基于卫星的气象数据,LSTM预测流域内的平均月降雨量为R 2  = 0.8610,而R 2为 =使用WNN的0.7825。平均月降雨量趋势预测的MAE在9至11毫米之间,而RMSE在15至21毫米之间变化。随着输入参数数量的增加(与子流域的大小相对应),模型的性能得以提高。在计算时间方面,两个模型都以几乎相同的纪元数收敛在最低RMSE上,而WNN花费稍长的时间才能达到最低RMSE。研究表明,在气象和水文监测网络稀缺的水文盆地中,在深度学习神经网络模型中使用基于卫星的气象数据适合于降雨和径流趋势的时空分析。

更新日期:2021-04-15
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