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Python for Water and Environment 1st ed. 2024 [Kõva köide]

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  • Formaat: Hardback, 288 pages, kõrgus x laius: 235x155 mm, 89 Illustrations, color; 3 Illustrations, black and white; XIX, 288 p. 92 illus., 89 illus. in color., 1 Hardback
  • Sari: Innovations in Sustainable Technologies and Computing
  • Ilmumisaeg: 09-Mar-2024
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9819994071
  • ISBN-13: 9789819994076
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Python for Water and Environment 1st ed. 2024Suurem pilt
  • Formaat: Hardback, 288 pages, kõrgus x laius: 235x155 mm, 89 Illustrations, color; 3 Illustrations, black and white; XIX, 288 p. 92 illus., 89 illus. in color., 1 Hardback
  • Sari: Innovations in Sustainable Technologies and Computing
  • Ilmumisaeg: 09-Mar-2024
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9819994071
  • ISBN-13: 9789819994076
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9789819994076.html
Märksõnad:

This textbook delves into the practical applications of surface and groundwater hydrology, as well as the environment. The Part I, "Practical Python for a Water and Environment Professional," guides readers through setting up a scientific computing environment and conducting exploratory data analysis and visualization using reproducible workflows. The Part II, "Statistical Modeling in Hydrology," covers regression models, time series analysis, and common hypothesis testing. The Part III, "Surface and Subsurface Water," illustrates the use of Python in understanding key concepts related to seepage, groundwater, and surface water flows. Lastly, the Part IV, "Environmental Applications," demonstrates the application of Python in the study of various contaminant transport phenomena.

Part I: Practical Python for a Water and Environment Professional

1. Data Analysis in the Water and Environment

2. Python Environment and Basics

3. Python Essentials

4. Exploratory Analysis of Hydrological Data

5. Graphical Hydrological Data Analysis

Part II: Statistical Modelling in Hydrology

6. Curve Fitting and Regression Analysis

7. Hydrological Time Series Analysis

8. Common Hypothesis Testing

9. Uncertainty Estimation

Part III: Surface and Subsurface Water

10. Introduction

11. Surface Flow Models

12. Subsurface Flow Models

Part IV: Environmental applications

13. Transport Phenomena

14. Contaminant Transport Models

15. Conclusion

Dr. Manabendra Saharia is an assistant professor in the Department of Civil Engineering and an associate faculty member of the Yardi School of Artificial Intelligence at the Indian Institute of Technology Delhi. Prior to joining IIT Delhi, he held positions in the hydrology labs of the NASA Goddard Space Flight Center and the National Center for Atmospheric Research (NCAR). He received his Ph.D. in Water Resources Engineering from the University of Oklahoma. At IIT Delhi, his HydroSense research lab focuses on utilizing physics and data-driven techniques to monitor and mitigate natural hazards such as floods and landslides. He has been recognized for his scientific contributions, having received Young Scientist awards from both the National Academy of Sciences, India (NASI), and the International Society for Energy, Environment and Sustainability (ISEES). Dr. Anil Kumar is a senior project scientist in the Department of Civil Engineering at the Indian Institute of Technology Delhi. He received his Ph.D. in Computational Geosciences jointly from Monash University (Australia) and the Indian Institute of Technology Bombay (India). He received a B.Tech. in Geophysical Technology from the Indian Institute of Technology Roorkee. He has been working as a researcher in the field of machine learning and numerical modeling and has helped develop innovative solutions for the oil, gas, and mining industry.