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

Yashon O Ouma,Alice N Wachera,Rodrick Cheruyot

doi:10.1007/s40747-021-00365-2

Yashon O Ouma, Alice N Wachera + Show 1 more

Open Access

https://doi.org/10.1007/s40747-021-00365-2

Copy DOI

Abstract

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.

Highlights

In sustainable water resources management, the accurate modelling of hydrological processes at watershed scales is a significant contributing factor
To understand the significance of the two neural network models in rainfall and runoff trend analysis, this study explores the implementation of wavelet neural network (WNN) and Long short-term memory (LSTM) in rainfall and runoff trend characterization and predictions within a hydrologic basin with scarce meteorological and hydrological monitoring network
The training and validation results from LSTM and WNN models are summarized in Table 2, and are based on all the five input parameters in the entire basin

Summary

Introduction

In sustainable water resources management, the accurate modelling of hydrological processes at watershed scales is a significant contributing factor. Predictions of rainfall and runoff trends are important for different water resource planning such as in irrigation, flood control, Generally, the forecasting of time-series data depends on the sequence being modelled and can have different dimensional spatial dependencies. Because of the timescale dependencies, the physical and conceptual models are considered as unsuitable for accurate prediction of rainfall and runoff where there is lack of high resolution spatial [7, 9]. These models require physical parameters which limit their application in the prediction of sequence data with unknown or limited quasi-periodic dynamics [10,11,12, 50]

Methods

Results

Conclusion