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

The Excel file "A Systematic Literature Review on Machine Learning Techniques for Predicting Household Water Consumption" is structured to document a systematic review process. It contains six sheets, each representing a stage in the literature review. Here's a breakdown of each sheet:

1. Initial Set

Contains the raw collection of research articles from databases like Scopus.

Includes fields like: title, author, journal, year, source, and abstract.

2. Duplicates Removed

Similar to the Initial Set but with duplicate entries removed.

Retains the same structure and content fields.

3. Abstract Screened

Articles that passed the abstract screening stage.

Same format as above, suggesting relevance was judged based on abstract content.

4. Corpus Screened

Articles that passed a deeper screening phase.

Likely assessed on fuller content, with some articles having missing abstracts (None values present).

5. Quality Assessment

Evaluates articles based on 10 quality criteria (e.g., clarity of objectives, use of visuals, replicability).

Scores are numerical (0–1) and include calculated metrics like Quality of the report, Credibility, Rigor, and Relevance.

6. Final Set

The most relevant and high-quality studies selected for the review.

Detailed columns include:

ML Technique (MLT)

MLT Characteristic

Type of Evaluation

Selection Factors

Benefits and Challenges

Type of Publication and DOI

Excel文件《面向家庭用水量预测的机器学习技术系统综述》（A Systematic Literature Review on Machine Learning Techniques for Predicting Household Water Consumption）用于完整记录系统综述的执行流程，该文件共包含6个工作表，分别对应文献综述流程中的不同阶段。各工作表详情如下：
1. 初始数据集（Initial Set）
收录从Scopus等学术数据库获取的研究论文原始合集，字段涵盖论文标题、作者、刊载期刊、发表年份、文献来源及摘要。
2. 去重数据集（Duplicates Removed）
与初始数据集结构完全一致，但已完成重复条目移除操作，保留相同的字段与内容格式。
3. 摘要筛选数据集（Abstract Screened）
收录通过摘要筛选阶段的研究论文，格式与前述数据集保持统一，即依据摘要内容完成文献相关性判定。
4. 全文筛选数据集（Corpus Screened）
收录通过深度筛选阶段的研究论文，通常基于完整文献内容进行评估，部分论文存在摘要缺失的情况（即None值）。
5. 质量评估数据集（Quality Assessment）
基于10项质量标准对收录论文进行量化评估，评估维度包括研究目标清晰度、可视化手段使用情况、研究可重复性等。评分采用0至1的数值区间，同时包含报告质量、可信度、严谨性及相关性等计算衍生指标。
6. 最终数据集（Final Set）
筛选得到的最具相关性与高质量的综述研究论文合集，其详细字段包括：机器学习技术（ML Technique, MLT）、机器学习技术特征、评估类型、遴选因素、优势与挑战、出版物类型及数字对象标识符（Digital Object Identifier, DOI）。