1,373 research outputs found
Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review
The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data
For recurrent neural networks trained on time series with target and
exogenous variables, in addition to accurate prediction, it is also desired to
provide interpretable insights into the data. In this paper, we explore the
structure of LSTM recurrent neural networks to learn variable-wise hidden
states, with the aim to capture different dynamics in multi-variable time
series and distinguish the contribution of variables to the prediction. With
these variable-wise hidden states, a mixture attention mechanism is proposed to
model the generative process of the target. Then we develop associated training
methods to jointly learn network parameters, variable and temporal importance
w.r.t the prediction of the target variable. Extensive experiments on real
datasets demonstrate enhanced prediction performance by capturing the dynamics
of different variables. Meanwhile, we evaluate the interpretation results both
qualitatively and quantitatively. It exhibits the prospect as an end-to-end
framework for both forecasting and knowledge extraction over multi-variable
data.Comment: Accepted to International Conference on Machine Learning (ICML), 201
Predicting air pollution in Almaty city using Deep Learning Techniques
Nowadays, in the era of urbanization and the growth of the social welfare of the population, megacities such as Almaty suffers from environmental problems such as air pollution. Air pollution adversely affects people's health, which leads to various harmful diseases. By predicting Particle Matter 2.5 (PM2.5) according to data of pollution particles and physical parameters we will reveal the effectiveness of measures taken by local authorities to meet the standards of the safety threshold for living beings. The paper’s main goal is to create a predictive model for particle matter 2.5 using a 3-layered sequential neural network model and gain the highest accuracy to simulate the continuation of the ecological situation in the city. The proposed model consists of four stages: data collection (from 6 stations), data pre-processing by treating missing values we deleted them and data normalization with function MinMaxScaller, building 3-layered sequential neural network and model evaluation using Mean squared error (MSE) metric, supported with a platform - Colab notebook and implemented using Python language. Based on experimental results, the forecast was defined as reliable - the strength of the model was proved using the MSE evaluation metric and equals 1e-5
Application and Evaluation of LSTM Architectures for Energy Time-Series Forecasting
Täpsete prognooside koostamine on energiavaldkonnas väga aktiivneuurimisvaldkond, kuna usaldusväärne teave tulevase elektritootmise kohta on oluline elektrivõrgu ohutuse tagamisel ning aitab minimeerida liigset elektrienergia tootmist. Kuna rekurrentsed tehisnärvivõrgud ületavad aegridade prognoosimise täpsuses enamikke muid masinõppe meetodeid, siis on need võetud ka energia prognoosimisel laialdaselt kasutusele. Käesolevas töös on energiaprognooside tegemiseks rakendatud algoritme Persistence ja ARIMA baasmeetoditena ning pika lühiajalise mäluga (LSTM) tehisnärvivõrke erinevates konfiguratsioonides. Töö uurib kolme LSTM-põhist arhitektuuri:i) standardne LSTM, ii) kahekihiline (stacked) LSTM ja iii) jadast-jadasse (sequence to sequence) LSTM. Kõigi nende LSTM-arhitektuuridega uuritakse nii ühemõõtmelisi kui ka mitmemõõtmelisi õpiülesandeid. LSTM-mudeleid treenitakse kuue erineva avalikult kättesaadava aegrea ennustamiseks, kusjuures iga aegrea jaoks treenitakse kuus erinevat LSTM mudelit. LSTM-mudelite poolt tehtud ennustusi mõõdetakse viie erineva hindamismõõdikuga. Lähtuvalt hindamise tulemustest neil kuuel aegreal hinnatakse LSTM-mudelite arhitektuuride robustsust.Accurate energy forecasting is a very active research field as reliable information about future electricity generation allows for the safe operation of the power grid and helps to minimize excessive electricity production. As Recurrent Neural Networks outperform most machine learning approaches in time series forecasting, they became widely used models for energy forecasting problems. In this work, the Persistence forecast and ARIMA model as baseline methods and the long short-term memory (LSTM)-based neural networks with various configurations are constructed to implement multi-step energy forecasting. The presented work investigates three LSTM based architectures:i) Standard LSTM, ii) Stack LSTM and iii) Sequence to Sequence LSTM architecture. Univariate and multivariate learning problems are investigated with each of these LSTM architectures. The LSTM models are implemented on six different time series which are taken from publicly available data. Overall, six LSTM models are trained for each time series. The performance of the LSTM models is measured by five different evaluation metrics. Considering the results of all the evaluation metrics, the robustness of the LSTM models is estimated over six time series
Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China
With the explosive growth of atmospheric data, machine learning models have achieved great success in air pollution forecasting because of their higher computational efficiency than the traditional chemical transport models. However, in previous studies, new prediction algorithms have only been tested at stations or in a small region; a large-scale air quality forecasting model remains lacking to date. Huge dimensionality also means that redundant input data may lead to increased complexity and therefore the over-fitting of machine learning models. Feature selection is a key topic in machine learning development, but it has not yet been explored in atmosphere-related applications. In this work, a regional feature selection-based machine learning (RFSML) system was developed, which is capable of predicting air quality in the short term with high accuracy at the national scale. Ensemble-Shapley additive global importance analysis is combined with the RFSML system to extract significant regional features and eliminate redundant variables at an affordable computational expense. The significance of the regional features is also explained physically. Compared with a standard machine learning system fed with relative features, the RFSML system driven by the selected key features results in superior interpretability, less training time, and more accurate predictions. This study also provides insights into the difference in interpretability among machine learning models (i.e., random forest, gradient boosting, and multi-layer perceptron models).</p
Explainable Tensorized Neural Ordinary Differential Equations forArbitrary-step Time Series Prediction
We propose a continuous neural network architecture, termed Explainable
Tensorized Neural Ordinary Differential Equations (ETN-ODE), for multi-step
time series prediction at arbitrary time points. Unlike the existing
approaches, which mainly handle univariate time series for multi-step
prediction or multivariate time series for single-step prediction, ETN-ODE
could model multivariate time series for arbitrary-step prediction. In
addition, it enjoys a tandem attention, w.r.t. temporal attention and variable
attention, being able to provide explainable insights into the data.
Specifically, ETN-ODE combines an explainable Tensorized Gated Recurrent Unit
(Tensorized GRU or TGRU) with Ordinary Differential Equations (ODE). The
derivative of the latent states is parameterized with a neural network. This
continuous-time ODE network enables a multi-step prediction at arbitrary time
points. We quantitatively and qualitatively demonstrate the effectiveness and
the interpretability of ETN-ODE on five different multi-step prediction tasks
and one arbitrary-step prediction task. Extensive experiments show that ETN-ODE
can lead to accurate predictions at arbitrary time points while attaining best
performance against the baseline methods in standard multi-step time series
prediction
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