Geology

Operating system

Watershed

Biology

Computer science

Climatology

Flood myth

Process (computing)

Geography

Hydrology (agriculture)

Machine learning

Set (abstract data type)

Flood forecasting

Surface runoff

Environmental science

Runoff model

Hydrological modelling

Archaeology

Artificial intelligence

Artificial neural network

Runoff curve number

Geotechnical engineering

Ecology

Programming language

Water

Considering the high random and non-static property of the rainfall-runoff process, lots of models are being developed in order to learn about such a complex phenomenon. Recently, Machine learning techniques such as the Artificial Neural Network (ANN) and other networks have been extensively used by hydrologists for rainfall-runoff modelling as well as for other fields of hydrology. However, deep learning methods such as the state-of-the-art for LSTM networks are little studied in hydrological sequence time-series predictions. We deployed ANN and LSTM network models for simulating the rainfall-runoff process based on flood events from 1971 to 2013 in Fen River basin monitored through 14 rainfall stations and one hydrologic station in the catchment. The experimental data were from 98 rainfall-runoff events in this period. In between 86 rainfall-runoff events were used as training set, and the rest were used as test set. The results show that the two networks are all suitable for rainfall-runoff models and bett...

Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation