Hybrid algorithm for short-term forecasting of PM2. 5 in China
In recent years, the forecasting of particles with a diameter of 2.5 μm or less (PM 2.5) has
been a popular research topic, and involves multiple sources of pollution, making it difficult …
been a popular research topic, and involves multiple sources of pollution, making it difficult …
An auto-encoder based LSTM model for prediction of ambient noise levels
Traffic noise is one of the most prevalent cause of environmental pollution in Indian cities. A
reliable method is required for assessment, and prediction of ambient noise levels. This …
reliable method is required for assessment, and prediction of ambient noise levels. This …
Prediction of chloride diffusion coefficient in concrete modified with supplementary cementitious materials using machine learning algorithms
The chloride diffusion coefficient (Dcl) is one of the most important characteristics of concrete
durability. This study aimed to develop a prediction model for the Dcl of concrete …
durability. This study aimed to develop a prediction model for the Dcl of concrete …
Short-term electricity consumption forecasting with NARX, LSTM, and SVR for a single building: small data set approach
I Zapirain, G Etxegarai, J Hernández… - Energy Sources, Part …, 2022 - Taylor & Francis
Nowadays, there is an undoubted change of trend toward a decentralized and decarbonized
electric grid, where the electric generation based on local resources will take on special …
electric grid, where the electric generation based on local resources will take on special …
The state-of-the-art in the application of artificial intelligence-based models for traffic noise prediction: a bibliographic overview
This paper reviews the application of artificial intelligence (AI)-based models in modeling
vehicular road traffic noise. A computerized search method was used to conduct the …
vehicular road traffic noise. A computerized search method was used to conduct the …
Machine learning for predicting the half cell potential of cathodically protected reinforced cement concrete slabs subjected to chloride ingress
This research work predicts the Half Cell Potential (HCP) values of cathodically protected
concrete slabs subjected to chloride ingress using machine learning techniques. Six classes …
concrete slabs subjected to chloride ingress using machine learning techniques. Six classes …
Trend and time series analysis by ARIMA model to predict the emissions and performance characteristics of biogas fueled compression ignition engine
Biomass-derived biogas is a very promising alternative energy source because of its
renewable and clean combustion characteristics compared to fossil petroleum diesel fuel …
renewable and clean combustion characteristics compared to fossil petroleum diesel fuel …
Comparison of svm and arima model in time-series forecasting of ambient noise levels
Nowadays, time-series modelling techniques are widely used for prediction and forecasting
of non-stationary data's. The study analyses the continuous one-year ambient noise data …
of non-stationary data's. The study analyses the continuous one-year ambient noise data …
Stock Market Price Forecasting using ARIMA vs ANN; A Case study from CSE
G Wijesinghe, R Rathnayaka - 2020 2nd International …, 2020 - ieeexplore.ieee.org
Stock market prediction or forecasting is a challenging task to predict the upcoming stock
values. Stock prices are nonstationary and highly noisy because stock markets are affected …
values. Stock prices are nonstationary and highly noisy because stock markets are affected …
Wavelet-exponential smoothing: a new hybrid method for suspended sediment load modeling
In this study, four conventional and a newly proposed method of wavelet-exponential
smoothing (WES)-with two presented scenarios (WES 1 and WES 2)–are employed to …
smoothing (WES)-with two presented scenarios (WES 1 and WES 2)–are employed to …