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Advances in Data-Based Approaches for Hydrologic Modeling and Forecasting

This book comprehensively accounts the advances in data-based approaches for hydrologic modeling and forecasting. Eight major and most;

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Distributed Hydrologic Modeling Using GIS

basis for modeling, and geospatial data forms the cornerstone of parameter and process representation. A physics-based approach involves;

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Handbook of Engineering Hydrology

modeling, hydrologic modeling: stochastic processes, and time series analysis of hydrologic data Dam risk and uncertainty, drought indices for;

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Geographic Information System Modules and Distributed Models of the Watershed

Recent advances in technology offer hydrologic engineers, watershed managers, and data collection agencies unprecedented capabilities for;

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Hydrological Data Driven Modelling

the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine;

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Modeling Hydrologic Change: Statistical Methods

Modeling hydrologic changes and predicting their impact on watersheds is a dominant concern for hydrologists and other water resource;

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Flood Forecasting

data is used, quality control, the hydrologic and hydraulic models used, and the unique problems of each country or system, such as glacial dam;

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Entropy Theory in Hydrologic Science and Engineering

any online entitlements included with the product. A THOROUGH INTRODUCTION TO ENTROPY THEORY AND ITS APPLICATIONS IN HYDROLOGIC SCIENCE AND;

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Recent Advances in the Modeling of Hydrologic Systems

fundamental understanding of hydrologic processes and to exploit technological advances in computing and remote sensing. It is against this background;

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Chaos in Hydrology

of hydrologic systems and on the existing theories for their modeling; (2) the fundamentals of nonlinear dynamic and chaos theories, methods for chaos;

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Engineering Hydrology: An Introduction to Processes, Analysis, and Modeling

modern hydrologic methods. Every hydrologic process is clearly explained along with current techniques for modeling and analyzing data. You will;

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Time Series

Focusing on Bayesian approaches and computations using analytic and simulation-based methods for inference, Time Series: Modeling;

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Advances in Streamflow Forecasting

Advances in Streamflow Forecasting: From Traditional to Modern Approaches covers the three major data-driven approaches of streamflow;

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Multiscale Hydrologic Remote Sensing

Multiscale Hydrologic Remote Sensing: Perspectives and Applications integrates advances in hydrologic science and innovative remote sensing;

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Multiscale Hydrologic Remote Sensing

Multiscale Hydrologic Remote Sensing: Perspectives and Applications integrates advances in hydrologic science and innovative remote sensing;

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Economic Forecasting

Economic forecasting involves choosing simple yet robust models to best approximate highly complex and evolving data-generating processes;

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Machine Learning for Time Series Forecasting with Python

concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models;

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Modeling and Stochastic Learning for Forecasting in High Dimensions

the latest developments in tools for high dimension and complex data analysis.;

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Advances in Geo-Spatial Information Science

-spatial information science (such as spatial data mining, mobile data modeling, and location-based services). The book contains selected and;

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Advances in Business and Management Forecasting

Volume 13, Advances in Business and Management Forecasting, is a blind refereed serial publication. It presents state-of-the-art studies;

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Road Traffic Modeling and Management

, estimation and monitoring Uses methods based on video and time series data for traffic modeling and forecasting Includes case;

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Hydrology and Hydroclimatology

interactions among different components of the water cycle. It takes a fresh look at the fundamentals and challenges in hydrologic and hydroclimatic;

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Time Series Algorithms Recipes

. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems;

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Tourism Forecasting and Marketing

time series approaches to forecasting, focusing on the concepts, model specification, data analysis, and methodologies used in day-to-day;

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Calibration of Watershed Models

hydrologic forecasting, hydrologic design, and water resources management. These models are based on general mathematical descriptions of the;

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Practical Time Series Forecasting

of Business. She is co-author of the textbook Data Mining for Business Intelligence and the book Modeling Online Auctions, among several other books;

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A Companion to Economic Forecasting

of the chapters has been specially written by an expert in the field, bringing together in a single volume a range of contrasting approaches and;

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