Data-efficient deep learning for predictive modelling of conventional single slope solar stills : leveraging transfer learning and tailored data augmentation strategies
department or school: Applied Science, Faculty of
department or school: Chemical and Biological Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Migaybil, Hashim
issued: 2025-11-27T21:58:55Z
issued: 2025
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Conventional single-slope solar stills are essential for decentralized freshwater production, yet their performance optimization is limited by small datasets and the nonlinear dynamics of desalination. This doctoral thesis addresses these constraints by developing and evaluating data-efficient supervised machine learning frameworks to predict freshwater productivity (Pstd, L/m²·day). The study integrates a novel high-performance solar still design with two complementary learning paradigms: Transfer Learning (TL) and tailored Data Augmentation (DA). The research begins with the design and MATLAB/SIMULINK simulation of a photovoltaic-assisted single-slope solar still engineered for improved thermal performance. The hybrid system achieved a peak efficiency of 45%, and its 730-sample dataset served as the “source” domain for TL. The first paradigm introduces a cross-design TL framework. A source Artificial Neural Network (ANN) was pre-trained on the hybrid system simulation data, and its learned weights were transferred and fine-tuned to model a conventional solar still using only 365 experimental samples. The optimized TL-based ANN (5-64-64-1) outperformed both randomly initialized ANNs and Multiple Linear Regression (MLR), achieving an Overall Index of Model Performance (OIMP) of 0.872 and demonstrating superior predictive accuracy and generalization. The second paradigm develops a tailored DA strategy to directly expand the conventional still’s limited dataset. Gaussian noise–based jittering was applied to sequential inputs within a 7-day look-back window to generate synthetic training data for a one-dimensional Convolutional Neural Network (CNN-1D). The optimized CNN-1D model—comprising three 128-filter convolutional layers—substantially outperformed baseline CNN and Support Vector Regression (SVR) models, achieving an RMSE of 0.045 and an OIMP of approximately 0.97. A threshold-based classification method was also introduced to translate raw predictions into interp…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/93043
note: Data-Efficient Deep Learning for Predictive Modelling of Conventional Single Slope Solar Stills: Leveraging Transfer Learning and Tailored Data Augmentation Strategies by Hashim Migaybil B.Sc., King Abdul-Aziz University, 2011 M.A.Sc, The University of British Columbia, 2021 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Chemical and Biological Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) November 2025 ©Hashim Migaybil 2025 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Data-Efficient Deep Learning for Predictive Modelling of Conventional Single Slope Solar Stills: Leveraging Transfer Learning and Tailored Data Augmentation Strategies submitted by Hashim Migaybil in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Chemical and Biological Engineering Examining Committee: Dr. Bhushan Gopaluni, Professor, Chemical and Biological Engineering, UBC Supervisor Dr. Ryozo Nagamune, Professor, Mechanical Engineering, UBC Supervisory Committee Member Dr. Yankai Cao, Associate Professor Chemical and Biological Engineering, UBC Supervisory Committee Member Dr. Dana Grecov, Professor, Mechanical Engineering, UBC University Examiner Dr. Steven Weijs, Associate Professor, Civil Engineering, UBC University Examiner iii Abstract Conventional single-slope solar stills are essential for decentralized freshwater production, yet their performance optimization is limited by small datasets and the nonlinear dynamics of desalination. This doctoral thesis addresses these constraints by developing and evaluating data-efficient supervised machine learning frameworks to predict freshwater productivity (Pstd, L/m²·day). The study integrates a novel high-performance solar still design with two complementary learning paradigms: Trans…
has type: Thesis/Dissertation
date issued: 2026-05
is shown at: 10.14288/1.0450881
language: eng
degree discipline: Chemical and Biological Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Gopaluni, Bhushan
type: Text
identifier URI: http://hdl.handle.net/2429/93043