scholarly journals A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data

2020 ◽  
Vol 10 (13) ◽  
pp. 4587 ◽  
Author(s):  
Edoardo Arnaudo ◽  
Alessandro Farasin ◽  
Claudio Rossi
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Linear Models ◽  
Short Term Memory ◽  
Meteorological Data ◽  
Learning Approaches ◽  
Short Term ◽  
Quality Estimation ◽  
Memory Network ◽  
Long Short Term Memory

Air pollution in urban regions remains a crucial subject of study, given its implications on health and environment, where much effort is often put into monitoring pollutants and producing accurate trend estimates over time, employing expensive tools and sensors. In this work, we study the problem of air quality estimation in the urban area of Milan (IT), proposing different machine learning approaches that combine meteorological and transit-related features to produce affordable estimates without introducing sensor measurements into the computation. We investigated different configurations employing machine and deep learning models, namely a linear regressor, an Artificial Neural Network using Bayesian regularization, a Random Forest regressor and a Long Short Term Memory network. Our experiments show that affordable estimation results over the pollutants can be achieved even with simpler linear models, therefore suggesting that reasonably accurate Air Quality Index (AQI) measurements can be obtained without the need for expensive equipment.

PREDIKSI RATA-RATA ZAT BERBAHAYA DI DKI JAKARTA BERDASARKAN INDEKS STANDAR PENCEMAR UDARA MENGGUNAKAN METODE LONG SHORT-TERM MEMORY

2021 ◽  
Vol 26 (1) ◽  
pp. 41-55
Author(s):  
Anisa Oktaviani ◽  
Hustinawati
Keyword(s):  
Artificial Intelligence ◽  
Air Quality ◽  
World Health Organization ◽  
Quality Index ◽  
Short Term Memory ◽  
World Health ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Health Organization

Indonesia menempati peringkat ke-6 dari 98 negara paling berpolusi di dunia pada tahun 2019. Di tahun tersebut, rata-rata AQI (Air Quality Index) sebesar 141 dan rata-rata konsentrasi PM2.5 sebesar 51.71 μg/m3 yang lima kali lipat diatas rekomendasi World Health Organization (WHO). Salah satu kota penyumbang polusi udara yaitu Jakarta. Berdasarkan data ISPU (Indeks Standar Pencemar Udara) yang diambil dari SPKU (Stasiun Pemantau Kualitas Udara) Dinas Lingkungan Hidup DKI Jakarta melampirkan pada tahun 2019, Jakarta memiliki kualitas udara sangat tidak sehat. Oleh karena itu perlu adanya model Artificial Intelligence dalam memperdiksi rata-rata tingkat zat berbahaya pada udara di DKI Jakarta. Salah satu algoritma yang dapat diterapkan dalam membuat model prediksi dengan menggunakan data timeseries adalah Long Short-Term Memory (LSTM). Tujuan dari penelitian ini membangun model prediksi rata-rata ISPU di DKI Jakarta menggunakan metode LSTM yang berguna bagi para pemangku kepentingan dibidang lingkungan hidup khususnya mengenai polusi udara. Penelitian mengenai prediksi rata-rata ISPU di DKI Jakarta menggunakan metode LSTM, menghasilkan nilai evaluasi MAPE 12.28%. Berdasarkan hasil evaluasi MAPE yang diperoleh, model LSTM yang digunakan untuk prediksi rata-rata ISPU di DKI Jakarta masuk kedalam kategori akurat.

scholarly journals Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

2021 ◽  
Vol 9 (6) ◽  
pp. 651
Author(s):  
Yan Yan ◽  
Hongyan Xing
Keyword(s):  
High Frequency ◽  
Short Term Memory ◽  
Detection Method ◽  
Low Frequency ◽  
Sea Clutter ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Small Targets

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

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