Publication: Flood Forecasting Using Artificial Neural Networks
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Issued Date
2025-01-01
Resource Type
Scopus ID
2-s2.0-105035581358
Journal Title
Flood Forecasting Using Artificial Neural Networks
Start Page
1
End Page
102
Rights Holder(s)
SCOPUS
Bibliographic Citation
Flood Forecasting Using Artificial Neural Networks (2025) , 1-102
Suggested Citation
Varoonchotikul P. Flood Forecasting Using Artificial Neural Networks. Flood Forecasting Using Artificial Neural Networks (2025) , 1-102. 102. doi:10.1201/9781003760344 Retrieved from: https://hdl.handle.net/20.500.14740/55435
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Abstract
This 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.