Broadening The Use Of Machine Learning In Hydrology

Broadening the Use of Machine Learning in Hydrology

Handbook of HydroInformatics

Classic Soft-Computing Techniques is the first volume of the three, in the Handbook of HydroInformatics series. Through this comprehensive, 34-chapters work, the contributors explore the difference between traditional computing, also known as hard computing, and soft computing, which is based on the importance given to issues like precision, certainty and rigor. The chapters go on to define fundamentally classic soft-computing techniques such as Artificial Neural Network, Fuzzy Logic, Genetic Algorithm, Supporting Vector Machine, Ant-Colony Based Simulation, Bat Algorithm, Decision Tree Algorithm, Firefly Algorithm, Fish Habitat Analysis, Game Theory, Hybrid Cuckoo–Harmony Search Algorithm, Honey-Bee Mating Optimization, Imperialist Competitive Algorithm, Relevance Vector Machine, etc. It is a fully comprehensive handbook providing all the information needed around classic soft-computing techniques. This volume is a true interdisciplinary work, and the audience includes postgraduates and early career researchers interested in Computer Science, Mathematical Science, Applied Science, Earth and Geoscience, Geography, Civil Engineering, Engineering, Water Science, Atmospheric Science, Social Science, Environment Science, Natural Resources, and Chemical Engineering. - Key insights from global contributors in the fields of data management research, climate change and resilience, insufficient data problem, etc. - Offers applied examples and case studies in each chapter, providing the reader with real world scenarios for comparison. - Introduces classic soft-computing techniques, necessary for a range of disciplines.

Advancements in Machine Learning and Natural Language Processing: Innovations and Applications

This book discovers groundbreaking advancements in artificial intelligence with innovative solutions for real-world challenges. This book showcases state-of-the-art methodologies like deep learning and transfer learning to tackle sentiment analysis, fake news detection, and multi-dialectal named entity recognition, with a special focus on Arabic language technologies. Bridging the gap between research and practice, it highlights topics such as knowledge extraction, AI ethics, and the societal impacts of big data. Targeted at researchers, educators, and professionals, it serves as a vital guide for beginners and a comprehensive reference for experts seeking to stay ahead in the rapidly evolving field of AI.