Matches in SemOpenAlex for { <https://semopenalex.org/work/W4309886642> ?p ?o ?g. }
- W4309886642 endingPage "100153" @default.
- W4309886642 startingPage "100153" @default.
- W4309886642 abstract "Water is one of the most valuable natural resources and a major element of a state's and country's socio-economic growth. The world's water resources and India are under huge pressure because of rising demand and a limited supply. Proper water management is the only solution for ensuring a close gap between demand and supply. Hydrological modeling offers an answer to this issue by establishing relationships between different hydrological processes. Several models have been developed in the past decade to simulate the rainfall and runoff relations. Some models are simple conceptual models based on spatially distributed event-based or continuous and artificial intelligence (AI) models. This study aims to compare two conceptual daily-based models and one AI model developed for the Hemavathi sub-watershed in the Cauvery Basin (India). Two daily runoff models are implemented using conceptual models, i.e., Sacramento and the Australian water balance model (AWBM) using Rainfall-Runoff Library (RRL) tool and Feed forward Backpropagation neural network (FFBPNN) model. The models were calibrated for daily streamflow values from 1990 to 2006 and then validated from 2007 to 2015. The effectiveness of model runoff predictions is evaluated using statistical parameters such as Nash-Sutcliffe efficiency (NSE) and Correlation coefficient (CC) values. The NSE values for the FFBPNN is 0.88 (calibration) and 0.74 (validation), Sacramento model is 0.66 (calibration) and 0.48 (validation), and 0.63 (calibration) and 0.44 (validation) for the AWBM model. From the obtained results, the FFBPNN model performs well in terms of NSE and CC compared to Sacramento and AWBM models." @default.
- W4309886642 created "2022-11-29" @default.
- W4309886642 creator A5042096726 @default.
- W4309886642 creator A5057263540 @default.
- W4309886642 creator A5063945118 @default.
- W4309886642 date "2023-05-01" @default.
- W4309886642 modified "2023-09-26" @default.
- W4309886642 title "Streamflow simulation using conceptual and neural network models in the Hemavathi sub-watershed, India" @default.
- W4309886642 cites W1901552909 @default.
- W4309886642 cites W1973663667 @default.
- W4309886642 cites W1983429778 @default.
- W4309886642 cites W1989916572 @default.
- W4309886642 cites W1991584796 @default.
- W4309886642 cites W1996476760 @default.
- W4309886642 cites W2006376587 @default.
- W4309886642 cites W2006794107 @default.
- W4309886642 cites W2009673657 @default.
- W4309886642 cites W2010281465 @default.
- W4309886642 cites W2010624461 @default.
- W4309886642 cites W2018351383 @default.
- W4309886642 cites W2042803967 @default.
- W4309886642 cites W2048069199 @default.
- W4309886642 cites W2048762736 @default.
- W4309886642 cites W2058998445 @default.
- W4309886642 cites W2059646894 @default.
- W4309886642 cites W2078276613 @default.
- W4309886642 cites W2078477703 @default.
- W4309886642 cites W2081346522 @default.
- W4309886642 cites W2111275569 @default.
- W4309886642 cites W2126626202 @default.
- W4309886642 cites W2156589291 @default.
- W4309886642 cites W2285045213 @default.
- W4309886642 cites W2471623547 @default.
- W4309886642 cites W2477041072 @default.
- W4309886642 cites W2793545812 @default.
- W4309886642 cites W2811165060 @default.
- W4309886642 cites W2913920173 @default.
- W4309886642 cites W305081305 @default.
- W4309886642 cites W3084961697 @default.
- W4309886642 cites W3086210877 @default.
- W4309886642 cites W3092418510 @default.
- W4309886642 cites W3121037143 @default.
- W4309886642 cites W3121604086 @default.
- W4309886642 cites W3174088825 @default.
- W4309886642 cites W3194705761 @default.
- W4309886642 cites W3212064177 @default.
- W4309886642 cites W4220986268 @default.
- W4309886642 doi "https://doi.org/10.1016/j.geogeo.2022.100153" @default.
- W4309886642 hasPublicationYear "2023" @default.
- W4309886642 type Work @default.
- W4309886642 citedByCount "2" @default.
- W4309886642 countsByYear W43098866422023 @default.
- W4309886642 crossrefType "journal-article" @default.
- W4309886642 hasAuthorship W4309886642A5042096726 @default.
- W4309886642 hasAuthorship W4309886642A5057263540 @default.
- W4309886642 hasAuthorship W4309886642A5063945118 @default.
- W4309886642 hasBestOaLocation W43098866421 @default.
- W4309886642 hasConcept C105795698 @default.
- W4309886642 hasConcept C119857082 @default.
- W4309886642 hasConcept C126197015 @default.
- W4309886642 hasConcept C126645576 @default.
- W4309886642 hasConcept C127313418 @default.
- W4309886642 hasConcept C127413603 @default.
- W4309886642 hasConcept C13606891 @default.
- W4309886642 hasConcept C150547873 @default.
- W4309886642 hasConcept C153823671 @default.
- W4309886642 hasConcept C155032097 @default.
- W4309886642 hasConcept C165838908 @default.
- W4309886642 hasConcept C187320778 @default.
- W4309886642 hasConcept C18903297 @default.
- W4309886642 hasConcept C205649164 @default.
- W4309886642 hasConcept C33923547 @default.
- W4309886642 hasConcept C39432304 @default.
- W4309886642 hasConcept C41008148 @default.
- W4309886642 hasConcept C49204034 @default.
- W4309886642 hasConcept C50477045 @default.
- W4309886642 hasConcept C50644808 @default.
- W4309886642 hasConcept C53739315 @default.
- W4309886642 hasConcept C58640448 @default.
- W4309886642 hasConcept C76886044 @default.
- W4309886642 hasConcept C77088390 @default.
- W4309886642 hasConcept C86803240 @default.
- W4309886642 hasConceptScore W4309886642C105795698 @default.
- W4309886642 hasConceptScore W4309886642C119857082 @default.
- W4309886642 hasConceptScore W4309886642C126197015 @default.
- W4309886642 hasConceptScore W4309886642C126645576 @default.
- W4309886642 hasConceptScore W4309886642C127313418 @default.
- W4309886642 hasConceptScore W4309886642C127413603 @default.
- W4309886642 hasConceptScore W4309886642C13606891 @default.
- W4309886642 hasConceptScore W4309886642C150547873 @default.
- W4309886642 hasConceptScore W4309886642C153823671 @default.
- W4309886642 hasConceptScore W4309886642C155032097 @default.
- W4309886642 hasConceptScore W4309886642C165838908 @default.
- W4309886642 hasConceptScore W4309886642C187320778 @default.
- W4309886642 hasConceptScore W4309886642C18903297 @default.
- W4309886642 hasConceptScore W4309886642C205649164 @default.
- W4309886642 hasConceptScore W4309886642C33923547 @default.
- W4309886642 hasConceptScore W4309886642C39432304 @default.