DeepAI AI Chat
Log In Sign Up

Use of 1D-CNN for input data size reduction of LSTM in Hourly Rainfall-Runoff modeling

by   Kei Ishida, et al.

An architecture consisting of a serial coupling of the one-dimensional convolutional neural network (1D-CNN) and the long short-term memory (LSTM) network, which is referred as CNNsLSTM, was proposed for hourly-scale rainfall-runoff modeling in this study. In CNNsLTSM, the CNN component receives the hourly meteorological time series data for a long duration, and then the LSTM component receives the extracted features from 1D-CNN and the hourly meteorological time series data for a short-duration. As a case study, CNNsLSTM was implemented for hourly rainfall-runoff modeling at the Ishikari River watershed, Japan. The meteorological dataset, consists of precipitation, air temperature, evapotranspiration, and long- and short-wave radiation, were utilized as input, and the river flow was used as the target data. To evaluate the performance of proposed CNNsLSTM, results of CNNsLSTM were compared with those of 1D-CNN, LSTM only with hourly inputs (LSTMwHour), parallel architecture of 1D-CNN and LSTM (CNNpLSTM), and the LSTM architecture which uses both daily and hourly input data (LSTMwDpH). CNNsLSTM showed clear improvements on the estimation accuracy compared to the three conventional architectures (1D-CNN, LSTMwHour, and CNNpLSTM), and recently proposed LSTMwDpH. In comparison to observed flows, the median of the NSE values for the test period are 0.455-0.469 for 1D-CNN (based on NCHF=8, 16, and 32, the numbers of the channels of the feature map of the first layer of CNN), 0.639-0.656 for CNNpLSTM (based on NCHF=8, 16, and 32), 0.745 for LSTMwHour, 0.831 for LSTMwDpH, and 0.865-0.873 for CNNsLSTM (based on NCHF=8, 16, and 32). Furthermore, the proposed CNNsLSTM reduces the median RMSE of 1D-CNN by 50.2 10.6


page 8

page 13


A Comparative Study of Detecting Anomalies in Time Series Data Using LSTM and TCN Models

There exist several data-driven approaches that enable us model time ser...

Prediction in ungauged regions with sparse flow duration curves and input-selection ensemble modeling

While long short-term memory (LSTM) models have demonstrated stellar per...

Capabilities of Deep Learning Models on Learning Physical Relationships: Case of Rainfall-Runoff Modeling with LSTM

This study investigates the relationships which deep learning methods ca...

A non-intrusive reduced order modeling framework for quasi-geostrophic turbulence

In this study, we present a non-intrusive reduced order modeling (ROM) f...

Time-series modeling with undecimated fully convolutional neural networks

We present a new convolutional neural network-based time-series model. T...

Estimation of Sea State Parameters from Ship Motion Responses Using Attention-based Neural Networks

On-site estimation of sea state parameters is crucial for ship navigatio...

A Data Driven Method for Multi-step Prediction of Ship Roll Motion in High Sea States

Accurate prediction of roll motion in high sea state is significant for ...