Hoese Michel Tornyeviadzi

Mathematics

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Research Summary

(inferred from publications by AI)

The researcher's work integrates diverse areas of physical sciences through AI, focusing on optimizing water systems for leakage detection and vulnerability assessment, employing advanced models like deep learning for hydrological forecasting and simulation. Additionally, they explore anaerobic digestion and biogas production for environmental impacts, while also ensuring robust quality monitoring techniques in water treatment plants.

Research Themes

All (15 Papers)

Physical Sciences (15 Papers)

All Papers

Leakage detection in water distribution networks via 1D CNN deep autoencoder for multivariate SCADA data(2023)

Nodal vulnerability assessment of water distribution networks: An integrated Fuzzy AHP-TOPSIS approach(2021)

A systematic framework for dynamic nodal vulnerability assessment of water distribution networks based on multilayer networks(2021)

Modelling the impact of weather parameters on the microbial quality of water in distribution systems(2021)

Robust night flow analysis in water distribution networks: A BiLSTM deep autoencoder approach(2023)

Modelling the impact of water temperature, pipe, and hydraulic conditions on water quality in water distribution networks(2021)

Semi-supervised anomaly detection methods for leakage identification in water distribution networks: A comparative study(2023)

Dynamic segment criticality analysis: A precursor to scheduling of maintenance routines in water distribution networks(2022)

Node search space reduction for optimal placement of pressure sensors in water distribution networks for leakage detection(2024)

Emulating process-based water quality modelling in water source reservoirs using machine learning(2022)

Predicting Discharges in Sewer Pipes Using an Integrated Long Short-Term Memory and Entropy A-TOPSIS Modeling Framework(2022)

Self-optimizing attainable regions of the anaerobic treatment process: Modeling performance targets under kinetic uncertainty(2019)

Geometry, kinetics and reactor network synthesis: Attainable limits for minimizing residence time in biomethane digesters(2020)

Modelling the performance potential of forced and natural-draft biomass cookstoves using a hybrid Entropy-TOPSIS approach(2021)

A Machine Learning Approach to Predicting the Turbidity from Filters in a Water Treatment Plant(2025)

Collaboration Network

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About This Profile

This profile is generated from publicly available publication metadata and is intended for research discovery purposes. Themes, summaries, and trajectories are inferred computationally and may not capture the full scope of the lecturer's work. For authoritative information, please refer to the official KNUST profile.