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Spectroscopy and Machine Learning for Water Quality Analysis [Kietas viršelis]

Edited by (Ewing Christian College, Prayagraj, India)
  • Formatas: Hardback, 130 pages, aukštis x plotis x storis: 254x178x10 mm, weight: 450 g, With figures in colour and black and white; 30 Illustrations
  • Serija: IOP Series in Spectroscopic Methods and Applications
  • Išleidimo metai: 08-Apr-2021
  • Leidėjas: Institute of Physics Publishing
  • ISBN-10: 0750330457
  • ISBN-13: 9780750330459
Kitos knygos pagal šią temą:
Spectroscopy and Machine Learning for Water Quality AnalysisDidesnis paveikslėlis
  • Formatas: Hardback, 130 pages, aukštis x plotis x storis: 254x178x10 mm, weight: 450 g, With figures in colour and black and white; 30 Illustrations
  • Serija: IOP Series in Spectroscopic Methods and Applications
  • Išleidimo metai: 08-Apr-2021
  • Leidėjas: Institute of Physics Publishing
  • ISBN-10: 0750330457
  • ISBN-13: 9780750330459
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9780750330459.html
Raktažodžiai:
Preface x
Editor biography xi
List of contributors
xii
1 FTIR spectroscopy and water quality
1(1)
Daniel Cozzolino
1.1 Introduction
1(1)
1.2 The origin of the spectra
2(2)
1.3 Hardware/instrumentation
4(1)
1.4 Sampling techniques
4(2)
1.5 Examples of applications of MIR spectroscopy in water analysis
6(3)
1.6 Concluding remarks
9
References
10
2 Fluorescence spectroscopy and applications in water quality monitoring
1(1)
Cong Wang
Daoliang Li
2.1 Introduction
2(1)
2.2 Principle and method of fluorescence analysis
2(2)
2.2.1 Principle
2(1)
2.2.2 Methods
3(1)
2.3 Application of fluorescence analysis in water quality monitoring
4(12)
2.3.1 Chlorophyll and CDOM
4(2)
2.3.2 Dissolved oxygen
6(3)
2.3.3 pH
9(2)
2.3.4 Ammonia nitrogen
11(1)
2.3.5 Nitrite and nitrate
12(2)
2.3.6 Heavy metals
14(2)
2.4 Conclusion and future perspectives
16
References
16
3 Paper-based optical sensors for water analysis and monitoring
1(1)
Bambang Kuswandi
3.1 Introduction
1(1)
3.2 Paper-based sensors
2(5)
3.2.1 Paper types
2(2)
3.2.2 Printing and fabrication
4(3)
3.3 Optical sensor types
7(3)
3.3.1 Chemical sensors
7(1)
3.3.2 Biosensors
8(2)
3.4 Optical detections
10(2)
3.4.1 Colorimetric method
10(1)
3.4.2 Chemiluminescence
11(1)
3.4.3 Electrochemiluminescence
11(1)
3.4.4 Fluorescence
11(1)
3.5 Water pollution detection
12(4)
3.5.1 Heavy metals
15(1)
3.5.2 Other pollutants
16(1)
3.6 Integration with artificial intelligence and machine learning
16(1)
3.7 Conclusions
17
Acknowledgments
18(1)
References
18
4 Nanocomposite materials for water purification: synthesis, characterization, and applications
1(1)
Naumih M Noah
Peter M Ndangili
4.1 Introduction
2(2)
4.2 Nanocomposites for water filtration
4(25)
4.2.1 Non-polymeric nanocomposites
5(8)
4.2.2 Polymeric nanocomposites membranes
13(16)
4.3 Conclusions and future directions
29
References
30
5 Advancing water quality assessment via artificial neural networks (ANNs)
1
Sachchidanand Soaham Gupta
Rajeev Singh
Pratibha Chaudhary
5.1 Introduction
1(2)
5.2 Water quality parameters
3(1)
5.2.1 Chemical variables
3(1)
5.2.2 Physical variables
3(1)
5.2.3 Biological variables
4(1)
5.3 Fundaments of machine learning
4(1)
5.4 Artificial neural networks
5(5)
5.4.1 Architectures of ANN
7(3)
5.5 Real-time applications of ANN in water quality prediction
10(2)
5.6 Discussion
12
References
14