The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Predictive modeling framework accelerated by GPU computing for smart water grid data-driven analysis in near real-time
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In a broader sense, fast deduction is also essential for near-real-time prediction and repeated evaluation [26].”
From GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment (2026) · cites this paper as [26]
In the citing paper
“In general, the literature suggests that deep learning-based heat prediction [27, 28], urban heat risk assessment [29], and GPU-accelerated computation [26] have mostly been examined separately.”
From GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment (2026) · cites this paper as [26]
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
Statsmodels: Econometric and Statistical Modeling with Python
Seabold, S., & Perktold, J. (2010) · Proceedings of the Python in Science Conference · doi:10.25080/majora-92bf1922-011 · Cited by 6,465
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
Breiman, L. (2001) · Statistical Science · doi:10.1214/ss/1009213726 · Cited by 4,340
Taylor, S. J., & Letham, B. (2018) · The American Statistician · doi:10.1080/00031305.2017.1380080 · Cited by 2,737
Forecasting Sales by Exponentially Weighted Moving Averages
Winters, P. R. (1960) · Management Science · doi:10.1287/mnsc.6.3.324 · Cited by 2,108
The M4 Competition: 100,000 time series and 61 forecasting methods
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2020) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2019.04.014 · Cited by 969
Research works printed but not linked: 1
- Hyndman RJ, Athanasopoulos G. Forecasting: Principles and Practice (2nd ed). OTexts Melbourne, Aust 2018. https://otexts.com/fpp2/(accessed November 17, 2021). · DOI · Paper page
Cited by
GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment
Alihodžić, A. (2026) · arXiv · doi:10.48550/arxiv.2605.16435
“In general, the literature suggests that deep learning-based heat prediction [27, 28], urban heat risk assessment [29], and GPU-accelerated computation [26] have mostly been examined separately.”
Metadata
- Authors
- Kalfarisi, R., Chew, A., Cai, J., Xue, M., Pok, J., & Wu, Z. Y.
- Affiliations
- Bentley Systems (United States) (US)
- Corresponding author
- Wu, Zheng Yi
- Venue
- Advances in Engineering Software
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 173
- Pages
- 103287
- ISSN
- 0965-9978 (ISSN-L)
- OpenAlex
- W4295740965
- Cited by
- 10
- References
- 49
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 4 · 2023: 4
- FWCI
- 1.01
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Advances in Engineering Software (publishedVersion)
- Funders
- National Research Foundation Singapore (PUB-1804-0087)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Water Systems and Optimization
- Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
- Physical Sciences › Environmental Science › Management, Monitoring, Policy and Law › Environmental Monitoring and Data Management
- Keywords
- smart water grid, predictive modeling, GPU acceleration, near real-time prediction, data preprocessing, training data size
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
