ArXiv Preprint
The identification and control of human factors in climate change is a
rapidly growing concern and robust, real-time air-quality monitoring and
forecasting plays a critical role in allowing effective policy formulation and
implementation. This paper presents DELFI, a novel deep learning-based mixture
model to make effective long-term predictions of Particulate Matter (PM) 2.5
concentrations. A key novelty in DELFI is its multi-scale approach to the
forecasting problem. The observation that point predictions are more suitable
in the short-term and probabilistic predictions in the long-term allows
accurate predictions to be made as much as 24 hours in advance. DELFI
incorporates meteorological data as well as pollutant-based features to ensure
a robust model that is divided into two parts: (i) a stack of three Long
Short-Term Memory (LSTM) networks that perform differential modelling of the
same window of past data, and (ii) a fully-connected layer enabling attention
to each of the components. Experimental evaluation based on deployment of 13
stations in the Delhi National Capital Region (Delhi-NCR) in India establishes
that DELFI offers far superior predictions especially in the long-term as
compared to even non-parametric baselines. The Delhi-NCR recorded the 3rd
highest PM levels amongst 39 mega-cities across the world during 2011-2015 and
DELFI's performance establishes it as a potential tool for effective long-term
forecasting of PM levels to enable public health management and environment
protection.
Naishadh Parmar, Raunak Shah, Tushar Goswamy, Vatsalya Tandon, Ravi Sahu, Ronak Sutaria, Purushottam Kar, Sachchida Nand Tripathi
2022-10-28
