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Article detail · 2019 · article

Artificial Neural Networks Method for Prediction of Rainfall-Runoff Relation: Regional Practice

ISSN2458-8989
YÖKSİS OpenAlex Open access · diamond
Year2019
Citations3OpenAlex
Percentile%53.0
FWCI0.281.00 = world average
Scopus (SJR)Q3

Data source split

  • YÖKSİSYÖKSİS article record
  • YÖKSİS venueNatural and Engineering Sciences
  • Catalog match (ISSN)Natural and Engineering Sciences
  • OpenAlexOpenAlex enrichment (abstract, citations, topics)
  • Semantic Scholarcitation count (not merged with OpenAlex)

Abstract

OpenAlex English

Rainfall and runoff relation is very important on efficient use of water resources and prevention of disasters. Nowadays, different methods of artificial intelligent techniques are applied to determine the rainfall-runoff relations. Artificial Neural Networks (ANN) is used for the present study. Also, Classical methods such as Multiple Linear Regression (MLR) are used. In this study, the data obtained from USA Waltham Massachusetts Stony Brook Reservoir basin was taken. 731 daily data of rainfall, runoff, and temperature were used to generate input data in the Multi Linear Regression and Feed-Forward Back-Propagation Artificial Neural Network models. The results obtained were compared with the actual results.

Topics

Citations

OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.

3citationsOpenAlex · cited_by_count (cache / database)

2 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).

  1. 2022 ESTIMATING DAM RESERVOİR LEVEL CHANGE OF ISTANBUL ALIBEY DAM WITH THE FUZZY SMRGT METHODCitations 3 · OpenAlex
  2. 2022 ESTIMATING DAM RESERVOİR LEVEL CHANGE OF ISTANBUL ALIBEY DAM WITH THE FUZZY SMRGT METHODCitations 3 · OpenAlex

Authors

3
  1. FATİH ÜNEŞ İSKENDERUN TEKNİK ÜNİVERSİTESİ 1
  2. Levent Keskin 2
  3. MUSTAFA DEMİRCİ İSKENDERUN TEKNİK ÜNİVERSİTESİ 3