Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model
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The public sector can use AI to address language barriers, delays in service delivery, long waiting periods, massive caseloads, and high turnover rates.
“The public sector might use AI to address several issues, including but not limited to the language barrier, delays in service delivery, long waiting periods, massive unmanageable caseloads, and high turnover rates (Mohtar et al. [53]).”
From Opportunities, challenges, and benefits of AI innovation in government services: a review (2024) · cites this paper as Mohtar et al. [53]
Effective AI implementation necessitates a strategic understanding of deployment locations to suggest methods for incorporating AI infusions and streamlining application processes.
“Effective AI implementation necessitates a strategic understanding of deployment locations to suggest methods for incorporating AI infusions and streamlining application processes (Mohtar et al. [53]).”
From Opportunities, challenges, and benefits of AI innovation in government services: a review (2024) · cites this paper as Mohtar et al. [53]
Cites
Chai, T., & Draxler, R. R. (2014) · Geoscientific Model Development · doi:10.5194/gmd-7-1247-2014 · Cited by 7,178 · see citing works
Willmott, C., & Matsuura, K. (2005) · Climate Research · doi:10.3354/cr030079 · Cited by 6,512 · see citing works
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Opportunities, challenges, and benefits of AI innovation in government services: a review
Alhosani, K., & Alhashmi, S. M. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00111-w · Cited by 132 · see citing works
“The public sector might use AI to address several issues, including but not limited to the language barrier, delays in service delivery, long waiting periods, massive unman-ageable caseloads, and high turnover rates (Mohtar et al. [53]).”
Influential
- Authors
- Yaseen, Z. M., Ebtehaj, I., Bonakdari, H., Deo, R. C., Danandeh Mehr, A., Mohtar, W. H. M. W., Diop, L., El-shafie, A., & Singh, V. P.
- Affiliations
- Razi University (IR)
- University of Southern Queensland (AU)
- National University of Malaysia (MY)
- Université Gaston Berger (SN)
- Texas A&M University (US)
- ORCID
- Venue
- Journal of Hydrology
- Publisher
- Elsevier BV
- Published
- 2017-11
- Volume
- 554
- Pages
- 263-276
- ISSN
- 0022-1694 (ISSN-L)
- OpenAlex
- W2753179649
- Cited by
- 267
- References
- 2
- Citations by year
- 2026: 10 · 2025: 23 · 2024: 26 · 2023: 31 · 2022: 51 · 2021: 34 · 2020: 36 · 2019: 38 · 2018: 18
- FWCI
- 14.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Hydrology (publishedVersion)
- Topics
- Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
- Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Fuzzy Logic and Control Systems
- Keywords
- adaptive neuro-fuzzy inference system, firefly algorithm, monthly streamflow forecasting, fuzzy c-means clustering, Pahang River, seasonal variation, low flow forecasting, hydrological modeling, metaheuristic optimization, Nash-Sutcliffe efficiency
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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.
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