- author = "Oluwaseun Kunle Oyebode",
- title = "Development of a sustainable evolutionary-inspired artificial intelligent system for municipal water demand modelling",
- school = "University of KwaZulu-Natal",
- year = "2020",
- address = "Durban, South Africa",
- month = mar,
- keywords = "genetic algorithms, genetic programming, Gene expression programming, ANN, Artificial neural network, Municipal water demand, Sustainable development, Water resources management, Water demand forecasting, Water consumption",
-
URL = "
https://researchspace.ukzn.ac.za/handle/10413/18677",
-
URL = "
http://ukzn-dspace.ukzn.ac.za/bitstream/handle/10413/18677/Oyebode_Oluwaseun_Kunle_2019.pdf",
- size = "152 pages",
-
abstract = "This study presents the development of a differential
evolution (DE)-inspired artificial neural network (ANN)
that incorporates climate and socioeconomic information
for a more accurate and reliable water demand
forecasting. The study also addresses the limitations
of ANN. Multiple feature selection techniques were
employed to identify the minimal subset of features for
optimal learning. The performance of the feature
selection techniques was validated and compared to a
baseline scenario comprising a full set of data
covering potential casual variables including weather,
socio-economic and historical water consumption data.
The performance of the models was evaluated based on
accuracy. Results show that all the feature selection
techniques outperformed the baseline scenario. More
importantly, the subset of features obtained from the
Pearson correlation technique produced the most
superior model in terms of model accuracy. Findings
from the study suggests that inclusion of weather and
socioeconomic variables in water demand modeling could
enhance the accuracy of forecasts and cater for the
impacts of climate and socioeconomic variations in
water demand planning and management.
The performance of the optimal DE-inspired model was thereafter compared to those developed via conventionally-used multiple linear regression and standard time series technique: exponential smoothing as well as other prominent soft computing techniques, namely support vector machines (SVM) and conjugate-gradient (CG)-trained multilayer perceptron (MLP). Results show that the DE-inspired ANN model was superior to the four other techniques for both the baseline scenario and optimal subset of features. DE showcased robustness in fine-tuning algorithm parameter values thereby producing good performance in terms of the solution efficiency and quality. Generally, this study demonstrates that water demand models can account for the impacts of weather and socioeconomic variations by incorporating explanatory variables based on weather and socioeconomic factors. This study also suggests that the synergetic use of feature selection techniques, DE algorithm and an early stopping criterion could be used in addressing the limitations of ANN and developing an improved and more reliable water demand forecasting model.
This work goes further to propose for a novel and more comprehensive integrated water demand and management modelling framework (IWDMMF) that is capable of syncing conventional evolutionary computation techniques and social aspects of society. The methodologies, principles and techniques behind this study fosters sustainable development and thus could be adopted in planning and management of water resources.",
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notes = "Student Number: 214585625
Supervisors: Akshay Kumar Saha and Albert Thembinkosi Modi",
