  In the absence of Gaussianity assumptions without disturbing spatial
continuity interpolating along the whole spatial surface for different time
lags is challenging. The past researchers pay enough attention to
Spatio-temporal interpolation ignoring the dynamic behavior of a spatial mean
function, threshold distance, and direction of maintaining spatial continuity.
Therefore, we employ hierarchical spatial clustering (HSC) to preserve local
spatial stationarity. This research work introduces a hybrid extreme valued
copula-based Spatio-temporal interpolation algorithm. Spatial dependence is
captured by a blended extreme valued probability distribution (BEVD). Temporal
dependency is modeled by the Bi-directional long short-time memory (BLSTM) at
different temporal granularities, 1 month, 2 months, and 3 months.
Spatio-temporal dependence is modeled by the Gumbel-Hougaard copula (GH). We
apply the proposed Spatio-temporal interpolation approach to the air pollution
data (Outdoor Particulate Matter (PM) concentration) of Delhi, collected from
the website of the Central Pollution Control Board, India as a crucial
circumstantial study. This article describes a probabilistic-recurrent neural
networking algorithm for Spatio-temporal interpolation. This Spatio-temporal
hybrid copula interpolation algorithm outperforms and is efficient enough to
detect spatial trends and temporal influence. From the entire research, we
notice that PM concentration in a year reaches a maximum, generally in November
and December. The northern and central part of Del-hi is the most sensitive
regarding air pollution.
