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Advanced Machine Learning Techniques includes the theoretical foundations of modern machine learning, as well as advanced methods and frameworks used in modern machine learning. Handbook of HydroInformatics, Volume II: Advanced Machine Learning Techniques presents both the art of designing good learning algorithms, as well as the science of analyzing an algorithm's computational and statistical properties and performance guarantees. The global contributors cover theoretical foundational topics such as computational and statistical convergence rates, minimax estimation, and concentration of measure as well as advanced machine learning methods, such as nonparametric density estimation, nonparametric regression, and Bayesian estimation; additionally, advanced frameworks such as privacy, causality, and stochastic learning algorithms are also included. Lastly, the volume presents Cloud and Cluster Computing, Data Fusion Techniques, Empirical Orthogonal Functions and Teleconnection, Internet of Things, Kernel-Based Modeling, Large Eddy Simulation, Patter Recognition, Uncertainty-Based Resiliency Evaluation, and Volume-Based Inverse Mode. This is an interdisciplinary book, 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, Chemical Engineering.

Hydrology --- Data processing. --- Machine learning.

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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.

Soft computing. --- Hydrology --- Data processing. --- Cognitive computing --- Electronic data processing --- Computational intelligence

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Water harvesting --- Water conservation --- Water --- Storage

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"This book analyzes and compares different types of irrigation systems. Details are provided for the analysis of field data to compare the suitability of the land for surface, sprinkle, and drip irrigation systems. The analyzed parameters include soil and land characteristics and establishes which irrigation method is best suited to the characteristics of the land"--

Irrigation --- Arid regions agriculture --- Social aspects. --- Dryland agriculture --- Dryland farming --- Agriculture --- Crop zones --- Desert reclamation --- Society and irrigation --- Irrigation. --- Arid regions agriculture. --- Irrigation engineering.