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Stochastic Hydrology

Stochastic Hydrology. Instructor: Prof. P. P. Mujumdar, Department of Civil Engineering, IISc Bangalore. The objective of this course is to introduce the concepts of probability theory and stochastic processes with applications in hydrologic analysis and design. Modeling of hydrologic time series with specific techniques for data generation and hydrologic forecasting will be dealt with. Case study applications will be discussed. (from nptel.ac.in)

Lecture 15 - ARIMA Models, Part 2

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Introduction

Lecture 01 - Introduction

Lecture 02 - Bivariate Distributions

Lecture 03 - Independence; Functions of Random Variables

Lecture 04 - Moments of a Distribution

Commonly used Probability Distributions

Lecture 05 - Normal Distribution

Lecture 06 - Other Continuous Distributions

Data Generation

Lecture 07 - Parameter Estimation

Lecture 08 - Covariance and Correlation

Lecture 09 - Data Generation

Time Series Analysis

Lecture 10 - Time Series Analysis, Part 1

Lecture 11 - Time Series Analysis, Part 2

Lecture 12 - Time Series Analysis, Part 3

Lecture 13 - Frequency Domain Analysis

Lecture 14 - Frequency Domain Analysis (cont.), ARIMA Models

Lecture 15 - ARIMA Models, Part 2

Lecture 16 - ARIMA Models, Part 3

Lecture 17 - ARIMA Models, Part 4

Lecture 18 - Case Studies, Part 1

Lecture 19 - Case Studies, Part 2

Lecture 20 - Case Studies, Part 3

Lecture 21 - Case Studies, Part 4

Markov Chains

Lecture 22 - Markov Chains

Lecture 23 - Markov Chains (cont.)

Frequency Analysis

Lecture 24 - Frequency Analysis

Lecture 25 - Frequency Analysis (cont.)

Lecture 26 - Frequency Analysis (cont.), Probability Plotting

Lecture 27 - Probability Plotting (cont.)

Lecture 28 - Goodness of Fit

Lecture 29 - IDF Relationships

Multivariate Models

Lecture 30 - Multiple Linear Regression

Lecture 31 - Principal Component Analysis

Lecture 32 - Regression on Principal Components

Lecture 33 - Multivariate Stochastic Models, Part 1

Lecture 34 - Multivariate Stochastic Models, Part 2

Lecture 35 - Multivariate Stochastic Models, Part 3

Data Consistency

Lecture 36 - Data Consistency, Part 1

Lecture 37 - Data Consistency, Part 2

Lecture 38 - Data Consistency, Part 3

Applications and Summary

Lecture 39 - Recent Applications: Climate Change Impact Assessment

Lecture 40 - Summary