Log In Sign Up

HydroDeep – A Knowledge Guided Deep Neural Network for Geo-Spatiotemporal Data Analysis

by   Aishwarya Sarkar, et al.

Floods are one of the major climate-related disasters, leading to substantial economic loss and social safety issue. However, the confidence in predicting changes in fluvial floods remains low due to limited evidence and complex causes of regional climate change. The recent development in machine learning techniques has the potential to improve traditional hydrological models by using monitoring data. Although Recurrent Neural Networks (RNN) perform remarkably with multivariate time series data, these models are blinded to the underlying mechanisms represented in a process-based model for flood prediction. While both process-based models and deep learning networks have their strength, understanding the fundamental mechanisms intrinsic to geo-spatiotemporal information is crucial to improve the prediction accuracy of flood occurrence. This paper demonstrates a neural network architecture (HydroDeep) that couples a process-based hydro-ecological model with a combination of Deep Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Network to build a hybrid baseline model. HydroDeep outperforms the performance of both the independent networks by 4.8 in Nash-Sutcliffe efficiency. A trained HydroDeep can transfer its knowledge and can learn the Geo-spatiotemporal features of any new region in minimal training iterations.


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...

Transfer Learning Approaches for Knowledge Discovery in Grid-based Geo-Spatiotemporal Data

Extracting and meticulously analyzing geo-spatiotemporal features is cru...

Water Preservation in Soan River Basin using Deep Learning Techniques

Water supplies are crucial for the development of living beings. However...

Exploring Randomly Wired Neural Networks for Climate Model Emulation

Exploring the climate impacts of various anthropogenic emissions scenari...

VARENN: Graphical representation of spatiotemporal data and application to climate studies

Analyzing and utilizing spatiotemporal big data are essential for studie...

Can deep neural networks learn process model structure? An assessment framework and analysis

Predictive process monitoring concerns itself with the prediction of ong...