Prediction of land surface temperature of major coastal cities of India using bidirectional LSTM neural networks

Author:

Maddu Rajesh1ORCID,Vanga Abhishek Reddy1ORCID,Sajja Jashwanth Kumar1ORCID,Basha Ghouse2,Shaik Rehana1ORCID

Affiliation:

1. Hydroclimatic Research Group, Lab for Spatial Informatics, International Institute of Information Technology, Hyderabad 500032, India

2. National Atmospheric Research Laboratory, Department of Space, Govt. of India, Tirupati 517502, India

Abstract

Abstract Surface Temperature (ST) is important in terms of surface energy and terrestrial water balances affecting urban ecosystems. In this study, to process the nonlinear changes of climatological variables by leveraging the distinct advantages of Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM), we propose an LSTM-BiLSTM hybrid deep learning model which extracts multi-dimension features of inputs, i.e., backward (future to past) or forward (past to future) to predict ST. This study assessed the climatological variables, i.e., wind speed, wind direction, relative humidity, dew point temperature, and atmospheric pressure impact on ST using five major coastal cities of India: Chennai, Mangalore, Visakhapatnam, Cuddalore, and Cochin. The Recurrent Neural Networks (RNN) and hybrid LSTM-BiLSTM models have effectively predicted ST and outperformed the standalone Artificial Neural Networks (ANN), LSTM, and BiLSTM models. The RNN and LSTM-BiLSTM models have performed better in predicting ST for Mangalore (Nash-Sutcliffe efficiency (NSE)=0.91), followed by Cochin (NSE=0.89), Chennai (NSE=0.88), Cuddalore (NSE=0.88), and Vishakhapatnam (NSE=0.81). The hybrid data-driven modeling framework indicated that coupling the LSTM and BiLSTM models was proven effective in predicting the ST of coastal cities.

Publisher

IWA Publishing

Subject

Management, Monitoring, Policy and Law,Atmospheric Science,Water Science and Technology,Global and Planetary Change

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