A Systematic Literature Review on Machine Learning Techniques for Predicting Household Water Consumption

Zenodo Repository Metadata and Description

Title: Supplemental Material for "A Systematic Literature Review on Machine Learning Techniques for Predicting Household Water Consumption"

Description

This repository contains the dataset and methodological artifacts developed for a Systematic Literature Review (SLR) investigating Machine Learning (ML) applications in residential water demand forecasting. The review synthesizes evidence from 80 primary studies published between 2009 and 2024, following the PRISMA-2020 guidelines.

Repository Contents

DataExtraction.csv: The master evidence matrix containing metadata, objectives, and findings for all 80 primary studies.

Taxonomy Maps: Standardized mapping files that document how raw textual terms from the literature were normalized into canonical categories.

Supplementary Material: The file provides detailed methodological documentation and extended analyses that support the results reported in the main manuscript, while preserving conciseness in the primary text.

The supplementary material includes:

A detailed methodological workflow and execution timeline, describing the implementation of the seven-step SLR protocol in accordance with PRISMA guidelines, including database search, screening, quality assessment, data consolidation, and analysis.

A complete list of the 80 primary studies analyzed in the review, including publication type and reference identifiers.

An extended characterization of machine learning model properties (RQ1), documenting the conceptual taxonomy used to classify model capabilities and methodological features across studies.

A qualitative synthesis of reported benefits associated with evaluation metrics (RQ3), complementing the quantitative results presented in the main article.

A citation-based contextual analysis, including total citations and citations per year (CPY), stratified by model families, to identify influential studies and thematic trends within the literature.

All quantitative mappings, cross-references, and classifications described in this document are aligned with the evidence matrix made publicly available alongside this repository. This supplementary file is intended to enhance transparency, reproducibility, and methodological rigor for researchers interested in machine-learning-based residential water demand forecasting.

Key Research Insights

Methodological Evolution: A capability shift around 2020 where Deep Learning models (notably LSTM) displaced traditional methods.

Predictor Patterns: Climatic and historical consumption dominate, with limited use of socio-economic variables due to privacy constraints.

Evaluation Standards: RMSE and MAE are the most widely used evaluation metrics.

Practical Tools for Researchers

Model-Predictor Crosswalk: A co-occurrence matrix of model families and predictor categories.

Prescriptive Decision Tree: A heuristic framework guiding model selection based on data and computational constraints.