Urban PM2.5 Concentration Prediction via Attention-Based CNN–LSTM
نویسندگان
چکیده
منابع مشابه
An AnFIS – BASED AIR QUALITY MODEL FOR PREDICTIOn OF SO2 COnCEnTRATIOn In URBAn AREA
This paper presents the results of attempt to perform modeling of SO2 concentration in urban area in vicinity of copper smelter in Bor (Serbia), using ANFIS methodological approach. The aim of obtained model was to develop a prediction tool that will be used to calculate potential SO2 concentration, above prescribed limitation, based on input parameters. As predictors, both technogenic and mete...
متن کاملLink Prediction via Ranking Metric Dual-Level Attention Network Learning
Link prediction is a challenging problem for complex network analysis, arising in many disciplines such as social networks and telecommunication networks. Currently, many existing approaches estimate the proximity of the link endpoints from the local neighborhood around them for link prediction, which suffer from the localized view of network connections. In this paper, we consider the problem ...
متن کاملAttention in Urban Foraging
This position paper argues how there has to be much more to smart city learning than just wayshowing, and something better as augmented reality than covering the world with instructions. Attention has become something for many people to know better in an age of information superabundance. Embodied cognition explains how the work-ings of attention are not solely a foreground task, as if attentio...
متن کاملProtein complex prediction via cost-based clustering
MOTIVATION Understanding principles of cellular organization and function can be enhanced if we detect known and predict still undiscovered protein complexes within the cell's protein-protein interaction (PPI) network. Such predictions may be used as an inexpensive tool to direct biological experiments. The increasing amount of available PPI data necessitates an accurate and scalable approach t...
متن کاملFuzzy Model based Prediction of Ground-Level Ozone Concentration
Ground-level ozone is a dangerous pollutant for which the prediction of the concentration could be of great importance. In this paper, we present and compare three fuzzy models aiming the forecasting of ground-level ozone concentration. The models apply Takagi-Sugeno, respective LESFRI fuzzy inference techniques and were generated using the ANFIS method of the Matlab’s Fuzzy Logic ToolBox, resp...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Applied Sciences
سال: 2020
ISSN: 2076-3417
DOI: 10.3390/app10061953