Deep sequential (DS) models are extensively employed for forecasting time series data
since the dawn of the deep learning era, and they provide forecasts for the values required …

In multivariate time series forecasting tasks, expanding forecast length and improving
forecast efficiency is an urgent need for practical applications. Accurate long-term …

Statistical analyses often require unbiased and reliable data completion. In this work, we
imputed missing fine particulate matter (PM2. 5) observations from eight years (2012–2019) …

Data-driven models are efficient decision support tools for monitoring harmful algal blooms
(HABs), particularly with the advent of the Internet of Things (IoT) and continuous data …

The air quality in Jakarta, particularly concerning PM 10 particulate matter, has become a
serious concern due to its significant impact on health and the environment. The increase in …

Air pollution is a global issue that poses significant threats to human health and the
environment due to industrial development. Forecasting the concentrations of major …

Waste disposal is one of the most important industries in the world. If not maintained
properly it would lead to the destruction of the environment. Improper waste disposal is …

Air Quality Index is an essential factor for evaluating the air pollution level concerning major
air pollutants. In India, the concentrated Particulate Matter 2.5 (PM2. 5) level is 1.3 times …

The electric power grid is subject to several vulnerabilities, which have an adverse impact
on the reliability of the system. Some of the vulnerabilities that the grid faces are anomalous …

Air pollution is a concern worldwide, especially in densely populated cities in developing
nations like Dhaka, Bangladesh. Accurately predicting air quality is crucial for health and …