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Flood Forecasting Using Artificial Neural Networks
About this book
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.
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Table of contents
- Cover
- Half Title
- Title Page
- Copyright Page
- Dedication
- Table of Contents
- Abstract
- Acknowledgement
- 1 Introduction
- 2 Artificial Neural Networks
- 3 Preliminary considerations
- 4 Extrapolation management for Artificial Neural Network models of Rainfall-Runoff relationships
- 5 Recurrent Neural Networks
- 6 Choice of input
- 7 Conclusions and recommendations
- 8 Samenvatting
- 9 References
- I Data used for the study
- Curriculum vitae
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Yes, you can access Flood Forecasting Using Artificial Neural Networks by P Varoonchotikul in PDF and/or ePUB format. We have over one million books available in our catalogue for you to explore.