Water Engineering Modeling and Mathematic Tools provides an informative resource for practitioners who want to learn more about different techniques and models in water engineering and their practical applications and case studies. The book provides modelling theories in an easy-to-read format verified with on-site models for specific regions and scenarios. Users will find this to be a significant contribution to the development of mathematical tools, experimental techniques, and data-driven models that support modern-day water engineering applications. Civil engineers, industrialists, and water management experts should be familiar with advanced techniques that can be used to improve existing systems in water engineering.
This book provides key ideas on recently developed machine learning methods and AI modelling. It will serve as a common platform for practitioners who need to become familiar with the latest developments of computational techniques in water engineering.
1. 16. An introduction to hydraulic structures - p. 297 2. 17. Mixing of inclined dense jets: a numerical modeling - p. 343 3. 18. Real-time flood forecasting with weather radar and distributed hydrological model - p. 369 4. 19. Flood susceptibility mapping in ungauged watersheds using a statistical model - p. 381 5. 20. Groundwater potential mapping using hybridization of simulated annealing and random forest - p. 391 6. 21. Synergy of combining megahertz ultrasound frequency and heat-activated persulfate for wastewater decontamination: micromodeling of acoustic cavitation and its role in the sono-hybrid process - p. 405 7. 22. On the sonochemical production of nitrite and nitrate in water: A computational study - p. 429 8. 23. Numerical insight into the liquid compressibility effect on the sonochemical activity of acoustic bubbles - p. 453 9. 24. Extremely randomized tree: a new machines learning method for predicting coagulant dosage in drinking water treatment plant - p. 475 10. 25. Pareto design of multiobjective evolutionary neuro-fuzzy system for predicting scour depth around bridge piers - p. 491 11. 26. River flow forecasting using stochastic and neuro-fuzzy-embedded technique: a comprehensive preprocessing-based assessment - p. 519 12. Index - p. 551
Water Engineering Modeling and Mathematic Tools provides an informative resource for practitioners who want to learn more about different techniques and models in water engineering and their practical applications and case studies. The book provides modelling theories in an easy-to-read format verified with on-site models for specific regions and scenarios. Users will find this to be a significant contribution to the development of mathematical tools, experimental techniques, and data-driven models that support modern-day water engineering applications. Civil engineers, industrialists, and water management experts should be familiar with advanced techniques that can be used to improve existing systems in water engineering.
This book provides key ideas on recently developed machine learning methods and AI modelling. It will serve as a common platform for practitioners who need to become familiar with the latest developments of computational techniques in water engineering.
1. 16. An introduction to hydraulic structures - p. 297 2. 17. Mixing of inclined dense jets: a numerical modeling - p. 343 3. 18. Real-time flood forecasting with weather radar and distributed hydrological model - p. 369 4. 19. Flood susceptibility mapping in ungauged watersheds using a statistical model - p. 381 5. 20. Groundwater potential mapping using hybridization of simulated annealing and random forest - p. 391 6. 21. Synergy of combining megahertz ultrasound frequency and heat-activated persulfate for wastewater decontamination: micromodeling of acoustic cavitation and its role in the sono-hybrid process - p. 405 7. 22. On the sonochemical production of nitrite and nitrate in water: A computational study - p. 429 8. 23. Numerical insight into the liquid compressibility effect on the sonochemical activity of acoustic bubbles - p. 453 9. 24. Extremely randomized tree: a new machines learning method for predicting coagulant dosage in drinking water treatment plant - p. 475 10. 25. Pareto design of multiobjective evolutionary neuro-fuzzy system for predicting scour depth around bridge piers - p. 491 11. 26. River flow forecasting using stochastic and neuro-fuzzy-embedded technique: a comprehensive preprocessing-based assessment - p. 519 12. Index - p. 551
