Towards digitalization of water supply systems for sustainable smart city development—Water 4.0

KB Adedeji, AA Ponnle, AM Abu-Mahfouz, AM Kurien - Applied Sciences, 2022 - mdpi.com
Urban water supply systems are complex and dynamic in nature, and as a result, can be
considered complex to manage owing to enhanced urbanization levels, climate change …

Industry 4.0 as a strategy to contribute to the water supply universalization in developing countries

DA Senna, VR Moreira, MCS Amaral… - Journal of …, 2023 - Elsevier
Access to safe water is still a challenge. Millions of people worldwide, mainly in rural and
remote regions, still do not have access to safe drinking water. Point-of-use and …

A comparison of ensemble and deep learning algorithms to model groundwater levels in a data-scarce aquifer of Southern Africa

Z Gaffoor, K Pietersen, N Jovanovic, A Bagula… - Hydrology, 2022 - mdpi.com
Machine learning and deep learning have demonstrated usefulness in modelling various
groundwater phenomena. However, these techniques require large amounts of data to …

An autoregressive machine learning approach to forecast high-resolution groundwater-level anomalies in the Ramotswa/North West/Gauteng dolomite aquifers of …

Z Gaffoor, A Gritzman, K Pietersen, N Jovanovic… - Hydrogeology …, 2022 - Springer
A novel approach using machine learning algorithms is used to predict high-resolution
groundwater-level changes, both current and in the future. The Southern African …

Sand dams for sustainable water management: Challenges and future opportunities

G Castelli, L Piemontese, R Quinn, J Aerts… - Science of the Total …, 2022 - Elsevier
Sand dams are impermeable water harvesting structures built to collect and store water
within the volume of sediments transported by ephemeral rivers. The artificial sandy aquifer …

Forecasting multiple groundwater time series with local and Global Deep Learning Networks

SR Clark, D Pagendam, L Ryan - International Journal of Environmental …, 2022 - mdpi.com
Time series data from environmental monitoring stations are often analysed with machine
learning methods on an individual basis, however recent advances in the machine learning …

Where should hydrology go? An early-career perspective on the next IAHS Scientific Decade: 2023–2032

TC van Hateren, HJ Jongen… - Hydrological …, 2023 - Taylor & Francis
This paper shares an early-career perspective on potential themes for the upcoming
International Association of Hydrological Sciences (IAHS) Scientific Decade (SD). This …

[HTML][HTML] Downscaling and validating GLDAS groundwater storage anomalies by integrating precipitation for recharge and actual evapotranspiration for discharge

C Viviers, M van der Laan, Z Gaffoor… - Journal of Hydrology …, 2024 - Elsevier
Abstract Study region The Steenkoppies Catchment is located approximately 75 km
southwest from Pretoria, South Africa (RSA). Study focus This study tested a framework for …

A transformative framework reshaping sustainable drought risk management through advanced early warning systems

TE Masupha, ME Moeletsi, M Tsubo - Iscience, 2024 - cell.com
In light of the increasing vulnerability to drought occurrences and the heightened impact of
drought-related disasters on numerous communities, it is imperative for drought-sensitive …

The Potential of Big Data and Machine Learning for Ground Water Quality Assessment and Prediction

A Rajeev, R Shah, P Shah, M Shah… - Archives of Computational …, 2024 - Springer
Water, a priceless gift from nature, acts as Earth's matrix, medium, and life-sustaining
substance. While the planet is predominantly covered by water, only 3% is available as …