Alternate Groundwater Modelling Strategies: A Multi-Faceted Data-Driven Approach

The impact of statistical methodologies on studying groundwater has been significant in the last several decades, due to cheaper computational abilities and presence of technologies that enable us to extract and measure more and more data. This paper focuses on the validation of statistical methodologies that are in practice and continue to be at the earliest disposal of the researcher, demonstrating how traditional time-series models and modern neural networks may be a viable option to analyze and make viable forecasts from data commonly available in this domain, and suggesting a copula-based strategy to obtain directional dependencies of groundwater level, spatially. This paper also proposes a sphere of model validation, seldom addressed in this domain: the model longevity or the model shelf-life. Use of such validation techniques not only ensure lower computational cost while maintaining reasonably high accuracy, but also, in some cases, ensure robust predictions or forecasts, and assist in comparing multiple models.

Real Time Monitoring and Forecasting of COVID 19 Cases using an Adjusted Holt based Hybrid Model embedded with Wavelet based ANN

Since the inception of the SARS - CoV - 2 (COVID - 19) novel coronavirus, a lot of time and effort is being allocated to estimate the trajectory and possibly, forecast with a reasonable degree of accuracy, the number of cases, recoveries, and deaths due to the same. The model proposed in this paper is a mindful step in the same direction. The primary model in question is a Hybrid Holt's Model embedded with a Wavelet-based ANN. To test its forecasting ability, we have compared three separate models, the first, being a simple ARIMA model, the second, also an ARIMA model with a wavelet-based function, and the third, being the proposed model. We have also compared the forecast accuracy of this model with that of a modern day Vanilla LSTM recurrent neural network model. We have tested the proposed model on the number of confirmed cases (daily) for the entire country as well as 6 hotspot states. We have also proposed a simple adjustment algorithm in addition to the hybrid model so that daily and/or weekly forecasts can be meted out, with respect to the entirety of the country, as well as a moving window performance metric based on out-of-sample forecasts. In order to have a more rounded approach to the analysis of COVID-19 dynamics, focus has also been given to the estimation of the Basic Reproduction Number, $R_0$ using a compartmental epidemiological model (SIR). Lastly, we have also given substantial attention to estimating the shelf-life of the proposed model. It is obvious yet noteworthy how an accurate model, in this regard, can ensure better allocation of healthcare resources, as well as, enable the government to take necessary measures ahead of time.