The DOI matches its Crossref record.
Rainfall-Runoff Modeling Using Artificial Neural Networks
Abstract
An Artificial Neural Network (ANN) methodology was employed to forecast daily runoff as a function of daily precipitation, temperature, and snowmelt for the Little Patuxent River watershed in Maryland. The sensitivity of the prediction accuracy to the content and length of training data was investigated. The ANN rainfall-runoff model compared favorably with results obtained using existing techniques including statistical regression and a simple conceptual model. The ANN model provides a more systematic approach, reduces the length of calibration data, and shortens the time spent in calibration of the models. At the same time, it represents an improvement upon the prediction accuracy and flexibility of current methods.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Tien Bui, D., Pradhan, B., Nampak, H., Bui, Q.-T., Tran, Q.-A., & Nguyen, Q.-P. (2016) · Journal of Hydrology · doi:10.1016/j.jhydrol.2016.06.027 · Cited by 355
Mekanik, F., Imteaz, M. A., Gato-Trinidad, S., & Elmahdi, A. (2013) · Journal of Hydrology · doi:10.1016/j.jhydrol.2013.08.035 · Cited by 310
Application of ANN and ANFIS models for reconstructing missing flow data
Dastorani, M. T., Moghadamnia, A., Piri, J., & Rico-Ramirez, M. (2010) · Environmental Monitoring and Assessment · doi:10.1007/s10661-009-1012-8 · Cited by 163
Application of support vector regression for modeling low flow time series
Sahoo, B. B., Jha, R., Singh, A., & Kumar, D. (2019) · KSCE Journal of Civil Engineering · doi:10.1007/s12205-018-0128-1 · Cited by 56
Metadata
- Authors
- Tokar, A. S., & Johnson, P. A.
- Venue
- Journal of Hydrologic Engineering
- Published
- 1999-07
- Volume
- 4
- Issue
- 3
- Pages
- 232-239
- ISSN
- 1084-0699 (ISSN-L)
- 1943-5584
- DOI
- 10.1061/(asce)1084-0699(1999)4:3(232
- OpenAlex
- W2063756720
- Cited by
- 660
- References
- 0
- FWCI
- 15.63
- Type
- journal article
- Language
- en
- Open access
- closed
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Rainfall-Runoff Modeling Using Artificial Neural Networks. https://app.cochat.ai/papers/10.1061/(asce)1084-0699(1999)4:3(232
- The paper
- Tokar, A. S., & Johnson, P. A. (1999). Rainfall-Runoff Modeling Using Artificial Neural Networks. Journal of Hydrologic Engineering. https://doi.org/10.1061/(asce)1084-0699(1999)4%3A3(232
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.
