Ground water level monitoring using GRACE data

The ground water storage (GWS) depletion is one of the major concern throughout the world and specifically developing Asian countries. In India, the ground water is depleting with an alarming rate and its monitoring is very much necessary. Remote sensing data can be a great resource for analysing the GWS dynamics. The objective of the project is to analyse the fluctuation of GWS using Machine Learning/Deep Learning technology for certain region. The various remote sensing data that can be used are Optical remote sensing data, SAR data, GRACE mission data, etc. In the proposed project, student is supposed to use GRACE data along with some other ancillary data. All the data which is going to be used are freely available and can be used by anyone. 

About the Project

The ground water storage (GWS) depletion is one of the major concern throughout the world and specifically developing Asian countries. In India, the ground water is depleting with an alarming rate and its monitoring is very much necessary. Remote sensing data can be a great resource for analysing the GWS dynamics. The objective of the project is to analyse the fluctuation of GWS using Machine Learning/Deep Learning technology for certain region. The various remote sensing data that can be used are Optical remote sensing data, SAR data, GRACE mission data, etc. In the proposed project, student is supposed to use GRACE data along with some other ancillary data. All the data which is going to be used are freely available and can be used by anyone.

Prerequisites

  • Basic Understanding of Remote sensing, linear algebra, Regression and Time Series
  • Basic Programming skill in Python/R/MATLAB
  • A PC/Laptop with Python/MATLAB

Week 1

1
Introduction to Remote sensing and its application

Week 2

1
Understanding GRACE data and estimation of mass variation

Week 3

1
Understanding programming language Python/R/MATLAB as per student choice for the project work

Week 4

1
Literature review for the proposed project

Week 5

1
Understanding the time series modelling using conventional and machine learning techniques

Week 6

1
Generating the result and its validation

Week 7

1
Analysis and discussion

Week 8

1
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