## 1 Introduction
### Related Work  
Much past research has accompanied traditional statistical and/or machine learning techniques in missing value imputation of meteorological data. Some studies only captured the spatial variations (e.g., [10; 11; 12; 13]) using spatial interpolation methods such as the inverse distance method (ID), normal ratio method (NR), geographical co-ordinates method (GC), and spatial kriging, also known as Gaussian process regression, while others only focused on their variation over time (e.g., [14; 15; 16]). The most common temporal method used is the autoregressive integrated moving average (ARIMA) models. Some studies (e.g., [17]) pointed out that the predictions obtained from spatiotemporal methods are more accurate than those of purely spatial predictions. This is mainly because spatiotemporal interpolation can be applied to geo-referencing positions over a space-time grid. Some researchers have used the traditional spatial kriging model after some modifications to model the spatiotemporal behavior of the data [4; 5]. There have been several spatiotemporal kriging-based studies conducted with weather variables. The study in [18] developed a spatiotemporal kriging model to predict wind speed and compared the results with the autoregressive moving average (ARMA) and Monte Carlo methods. Spatiotemporal kriging modeling gave a better fit for the data. Moreover, the authors stated a few advantages of kriging models in comparison with other regression methods. Providing estimates with the mean squared error of the estimation (kriging variance), non-requirement of any distributional assumption related to the data, the ability to use a complete set of spatial and temporal information, and the ability to use a limited number of sampled data points to estimate the value of a variable over a continuous spatial field are some of them. Due to these prominent advantages of kriging, recent studies have integrated kriging with conventional methods (e.g., regression and time series models) to gain fair and more accurate predictions for the missing observations. Nevertheless, a few research studies that use such hybrid approaches were found. The study [17] applied a spatiotemporal kriging method to model total monthly rainfall data among 269 rain gauge stations, of which nearly 80% of stations had missing data. The deterministic trend component was estimated with multiple linear regression, taking several covariates, including latitude, longitude, and quadratic effect of the corresponding longitude and latitude pairs. Since it only captured 29% of the temporal variability in rainfall, the residuals produced by the model were analyzed using a generalized product-sum spatiotemporal variogram and the interpolation was carried out with spatiotemporal kriging. The final predictor was obtained by combining trend estimate and interpolated value with kriging. In another study [5], spatial association of atmospheric temperature data was modeled with universal kriging and temporal variability was captured with autoregressive (AR) techniques. Then, they were spatiotemporally combined to predict \(k\)-steps (days) ahead temperature in a given spatial domain. A comparison of forecasted values with those obtained from ARIMA model indicated that the novel hybrid model performed better than the ARIMA model. The recent advancements in machine learning (ML) and deep learning (DL) can be utilized effectively to predict missing cases in meteorological data. Both approacheshave a great ability to handle large-scale problems and provide a flexible architecture in capturing spatial and temporal features [1; 2]. In addition, they do not rely on hand-crafted feature engineering and prior assumptions on input data. Moreover, the aptness of deep neural networks on large volumes of spatiotemporal data compared to statistical and other classical machine learning techniques has been recognized. A convolution bidirectional long short-term memory (LSTM) model was applied to capture spatial and temporal patterns in traffic flow data in the study [2].