Application of Long Short-Term Memory (LSTM) on the Prediction of Rainfall-Runoff in Karst Area - Fang, Longzhang; Shao, Dongguo (Frontiers in Physics, 2022)
In actual engineering fields, the bearing capacity of the rock is closely related to the pore water pressure in the rock. Studies have shown that the pore water in the rock has a great relationship with the change of runoff. Thus, it has crucial meaning to accurately to evaluate and quantitative the property of the rainfall-runoff, and many traditional classic model are proposed to study the characteristic of rainfall-runoff. While, considering the high uncertainty and random of the rainfall-runoff property, more and more artificial neural network(ANN) are used for the rainfall-runoff modeling as well as other fields. Among them, the Long Short-Term Memory(LSTM), which can be trained for sequence generation by processing real data sequences one step at a time and has well prediction results in other engineering fields, are adopted in this study to investigate the changes of rainfall-runoff values and made a prediction. In order to ensure the accuracy of the trained model, the cross-validation method is used in this study, which the training data set are divided into 12 parts. The monthly forecast results from 2014 to 2015 show that the model can well reflect the peaks and troughs. In recent study, the relationship between the rainfall-runoff and discharge are commonly based on the current measured data, while the prediction results are adopted to analysis the relation of these parameters. And considering the existing methods have fuzzy relationship between runoff and discharge, which leads to high risk of forecasting and dispatching. A method of modeling analysis and parameter estimation of hydrological runoff and discharge relationship based on machine learning is designed. Experimental results: the average risk of this method is 61.23%, which is 15.104% and 13.397% less than that of the other two existing methods, respectively. It proves that the method of hydrological runoff relationship modeling and parameter estimation integrated with machine learning has better practical application effect.