Climate Change Impact on Sub-Surface Hydrology of Kaduna River Catchment

This paper applied the use of a stochastic weather generator at the site of a hydrological model to simulate the impact of climate change on the sub-surface hydrological response of Kaduna River as a basis for a sustainable ground water development plan. Average time series of rainfall, temperature and gauge height observed readings were downscaled to the watershed flow volume and applied as forcing to simulate the ground water response as spatially lumped, ignoring the heterogeneous nature of the ground water aquifer. Future simulations indicate increase in base flow, ground storage and decrease soil storage throughout the summer and autumn months for the catchment.

FAIR Machine Learning Model Pipeline Implementation of COVID-19 Data

Research and development are gradually becoming data-driven and the implementation of the FAIR Guidelines(that data should be Findable, Accessible, Interoperable, and Reusable) for scientific data administration and stewardship has the potential to remarkably enhance the framework for the reuse of research data. In this way, FAIR is aiding digital transformation. The ‘FAIRification’ of data increases the interoperability and(re)usability of data, so that new and robust analytical tools, such as machine learning(ML) models, can access the data to deduce meaningful insights, extract actionable information, and identify hidden patterns. This article aims to build a FAIR ML model pipeline using the generic FAIRification workflow to make the whole ML analytics process FAIR. Accordingly, FAIR input data was modelled using a FAIR ML model. The output data from the FAIR ML model was also made FAIR. For this, a hybrid hierarchical k-means (HHK) clustering ML algorithm was applied to group the data into homogeneous subgroups and ascertain the underlying structure of the data using a Nigerian-based FAIR dataset that contains data on economic factors, healthcare facilities, and coronavirus occurrences in all the 36 states of Nigeria. The model showed that research data and the ML pipeline can be FAIRified, shared, and reused by following the proposed FAIRification workflow and implementing technical architecture.