Publication:
Flood Forecasting Using Artificial Neural Networks

dc.contributor.authorVaroonchotikul P.
dc.contributor.correspondenceVaroonchotikul P.
dc.contributor.otherSrinakharinwirot University
dc.date.accessioned2026-04-19T19:00:02Z
dc.date.issued2025-01-01
dc.date.issuedBE2568-01-01
dc.description.abstractThis dissertation considers various questions with respect to the effects of salinity on nutrification: what are the main inhibiting factors causing the effects, do all salts have similar effects, what is the maximum acceptable salt level, are ammonia oxidisers or nitrite oxidizers most sensitive to salt stress, can nitrifiers adapt to long term salt stress and are some specific nitrifiers more resistant to salt stress than others? Research was carried out at laboratory scale and in full-scale plants and modelling was employed in both phases to provide a mathematical description for salt inhibition on nitrification and to facilitate the comparison. The result has led to an improved understanding of the effect of salinity on nitrification. The results can be used to improve the sustainability of the exisisting wastewater treatment plants operated under salt stress.
dc.identifier.citationFlood Forecasting Using Artificial Neural Networks (2025) , 1-102
dc.identifier.doi10.1201/9781003760344
dc.identifier.scopus2-s2.0-105035581358
dc.identifier.urihttps://hdl.handle.net/20.500.14740/55435
dc.rights.holderSCOPUS
dc.subjectEngineering
dc.subjectBusiness, Management and Accounting
dc.titleFlood Forecasting Using Artificial Neural Networks
dc.typeBook
dspace.entity.typePublication
oaire.citation.endPage102
oaire.citation.startPage1
oaire.citation.titleFlood Forecasting Using Artificial Neural Networks
oairecerif.author.affiliationSrinakharinwirot University
swu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105035581358&origin=inward

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