International Journal on Science and Technology

E-ISSN: 2229-7677   •   Impact Factor: 9.88

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 17 Issue 3 July-September 2026 Submit your research before last 3 days of September to publish your research paper in the issue of July-September.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 17 Issue 3
July-September 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Rainfall – Runoff Modeling in the Hemavathi river catchment of Western Ghats of Karnataka using Regression and Artificial Neural Networks

Author(s) Mr. Suresh Ramakrishna Yelandur
Country India
Abstract Rainfall–Runoff process is a complex phenomenon which depends on various factors like catchment features, intensity and duration of rainfall, soil characteristics, etc. Many researchers have developed models relating rainfall-runoff in various catchments of the world. In the present study, attempt has been made to develop daily monsoon rainfall-runoff models using Multi Linear Regression (MLR) and Artificial Neural Network Feed Forward Multi Layer Perceptron (MLP) Technique for the Hemavathi river catchment in the Western Ghats of Karnataka. Daily rainfall and 10 day Reciprocal Antecedent Precipitation Index (API) are used as the input parameters, daily runoff being the output. Different models have been tried for both calibration and validation data using the above said techniques. The best model is chosen based on the statistical parameters. The models for the calibration period perform relatively better than those for the validation period data sets. An error analysis also has been carried out to compare the results obtained from the models. Hydrographs are drawn to compare the observed and estimated runoff values and the best year has been identified.
Keywords Antecedent Precipitation Index, Multi Linear Regression, Multi Layer Perceptron, Root Mean Squared Error, Coefficient of Efficiency, Error Analysis
Field Engineering
Published In Volume 17, Issue 2, April-June 2026
Published On 2026-06-19
DOI https://doi.org/10.71097/IJSAT.v17.i2.11299
View / Download PDF File

Share this


CrossRef

Crossref DOI prefix of IJSAT is 10.71097/IJSAT

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 17 Isu 3Cover Page Vol 17 Isu 2Cover Page Vol 17 Isu 1

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJSAT
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics 		Join as a Reviewer
Editors & Reviewers
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us Message on WhatsApp +91-9687-182-185 editor@ijsat.org