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

Maddu, Rajesh; Vanga, Abhishek Reddy; Sajja, Jashwanth Kumar; Basha, Ghouse; Rehana, S.

doi:10.2166/wcc.2021.460

Cited by 15 publications

(5 citation statements)

References 68 publications

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“…Bi-directional long-short term memory (BLSTM) is the method of making any neural network have the arrangement of data in both backward and forward directions [ 16 , 17 ]. BLSTM utilizes most of the data by going the time-step in both directions.…”

Section: Methodsmentioning

confidence: 99%

Bi-directional long short term memory-gated recurrent unit model for Amharic next word prediction

2022

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The next word prediction is useful for the users and helps them to write more accurately and quickly. Next word prediction is vital for the Amharic Language since different characters can be written by pressing the same consonants along with different vowels, combinations of vowels, and special keys. As a result, we present a Bi-directional Long Short Term-Gated Recurrent Unit (BLST-GRU) network model for the prediction of the next word for the Amharic Language. We evaluate the proposed network model with 63,300 Amharic sentence and produces 78.6% accuracy. In addition, we have compared the proposed model with state-of-the-art models such as LSTM, GRU, and BLSTM. The experimental result shows, that the proposed network model produces a promising result.

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Section: Methodsmentioning

confidence: 99%

Bi-directional long short term memory-gated recurrent unit model for Amharic next word prediction

2022

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“…Different scenarios were simulated considering an asphalt pavement and two sampietrini pavements; the former is standard, the latter is permeable. The modeled modular pavements differ with respect to in-between spaces and joint filler; in the permeable solution the laying pattern has wider joints than the traditional one [ 40 , 41 ] and the joints are filled with a water-permeable polymer sealing.…”

Section: Methodsmentioning

confidence: 99%

Effect of Sampietrini Pavers on Urban Heat Islands

Moretti

Cantisani

Carpiceci

et al. 2021

IJERPH

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Cool pavements are reflective and/or permeable pavements that improve microclimate of urban areas where heat islands cause discomfort to citizens. Stone pavements lower surface temperatures and reduce the amount of heat absorbed. This study assessed, using ENVI-met 4.3 LITE software, how air temperature and predicted mean vote depend on physical properties of the road pavement. A comparative microclimatic analysis was implemented on a rectangular square in Rome (Italy) in the summer, paved in three different ways: asphalt, traditional sampietrini, and permeable sampietrini. The model considered local weather parameters, surrounding fabric, and vegetation to give reliable results in terms of numerical and graphical output using the application tool Leonardo. The tested pavement types affected air temperature during the day, but did not influence this variable in the early morning. Permeable sampietrini pavement was more effective than traditional sampietrini pavement in reducing air temperature compared to the current asphalt surface. The road pavement did not, however, affect human comfort in terms of predicted mean vote. The obtained results are useful for further investigation of parameters that could modify the microclimatic conditions of urban areas.

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“…These meteorological features are selected to forecast weather because these features explain the state of weather for a given location and time. All the eight meteorological features are used as input features to forecast temperature [24,33,35,36,37].…”

Section: A Dataset Description and Data Preprocessingmentioning

confidence: 99%

Short-Term Weather Forecasting Using Spatial Feature Attention Based LSTM Model

Suleman

Shridevi

2022

IEEE Access

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Weather prediction and meteorological analysis contribute significantly towards sustainable development to reduce the damage from extreme events which could otherwise set-back the progress in development by years. The change in surface temperature is as one of the important indicators in detecting climate change. In this research, we propose a novel deep learning model named Spatial Feature Attention Long Short Term Memory (SFA-LSTM) model to capture accurate spatial and temporal relations of multiple meteorological features to forecast temperature. Significant spatial feature and temporal interpretations of historical data aligned directly to output feature helps the model to forecast data accurately. The spatial feature attention captures mutual influence of input features on the target feature. The model is built using encoder-decoder architecture, where the temporal dependencies in data are learnt using LSTM layers in the encoder phase and spatial feature relations in the decoder phase. SFA-LSTM forecasts temperature by simultaneously learning most important time steps and weather variables. When compared with baseline models, SFA-LSTM maintains the state-of the-art prediction accuracy while offering the benefit of appropriate spatial feature interpretability. The learned spatial feature attention weights are validated from magnitude of correlation with target feature obtained from the dataset.

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

Cited by 15 publications

References 68 publications

Bi-directional long short term memory-gated recurrent unit model for Amharic next word prediction

Bi-directional long short term memory-gated recurrent unit model for Amharic next word prediction

Effect of Sampietrini Pavers on Urban Heat Islands

Short-Term Weather Forecasting Using Spatial Feature Attention Based LSTM Model

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