Saturday, 29 August 2026

IMD Gridded Data Analyzer v1.0.3: A Research-Oriented Tool for Indian Climate Data Analysis

 





Working with long-term climate data often involves several repetitive steps — downloading datasets, extracting data for research locations, preparing datasets, calculating statistics, and performing climate-trend analysis.

To simplify this workflow, we have developed IMD Gridded Data Analyzer, a research-oriented software tool that brings data downloading, extraction, statistical analysis, climate-change trend analysis, and SWAT data preparation into a single workflow.

Download → Extract → Analyze

The software is designed particularly for researchers, PhD students, students, climatologists, hydrologists, GIS professionals, and others working with Indian climate data.

What is IMD Gridded Data Analyzer?

IMD Gridded Data Analyzer is a Windows-based research software designed to reduce the time and effort involved in working with long-term IMD gridded climate datasets.

Instead of repeatedly performing data preparation and statistical calculations manually, users can follow an integrated workflow:

1. Download IMD Data → 2. Extract Data → 3. Perform Analysis

The software supports analysis of individual locations, multiple locations, regions, and complete location lists.


Key Features

1. Download IMD Climate Data

The software provides an integrated interface for obtaining IMD meteorological datasets.

Users can work with:

  • Precipitation / rainfall
  • Minimum temperature (Tmin)
  • Maximum temperature (Tmax)
  • Real-time daily IMD data
  • Complete available archives
  • Selected data where supported

The software can also retain existing valid files and avoid unnecessary repeated downloads.


2. Extract Data for a Single Location

For research involving a particular city, station, watershed point, or research site, users can extract climate data by providing:

  • Location name
  • Latitude
  • Longitude
  • Required variables
  • Extraction period

This provides a straightforward way to obtain climate data for an individual research location.


3. Extract Data for a Region

The regional extraction option allows users to define a geographic area rather than processing individual points one at a time.

This can be particularly useful for:

  • Study regions
  • Watersheds
  • Hydrological studies
  • Regional climate assessments
  • SWAT-based modeling

Regional extraction can also be used to prepare SWAT-compatible meteorological files.


4. Multiple-Location Climate Data Extraction

One of the useful features for climate-change studies is the ability to process multiple locations together.

Users can prepare a CSV/Excel location list containing coordinates and then extract the required climate data for all locations in a single workflow.

For example, a study may contain:

  • Multiple cities
  • Weather stations
  • District locations
  • Research sampling sites
  • Climate-change assessment locations
  • Large point datasets

This avoids repeatedly entering and processing every location individually.


Statistical Analysis

After extracting the climate data, the software can generate an Excel analysis workbook containing statistical summaries.

The monthly statistics include parameters such as:

  • Number of observations (N)
  • Mean
  • Median
  • Standard deviation
  • Minimum
  • Maximum
  • Sum

These statistics are organized by climate variable and calendar month, making them useful for examining the statistical characteristics of rainfall, minimum temperature, and maximum temperature.


Mann–Kendall Trend Analysis

Climate-change research frequently requires analysis of whether a climate variable shows an increasing or decreasing trend over time.

IMD Gridded Data Analyzer includes Mann–Kendall trend analysis within the same workflow.

The generated analysis includes:

  • N
  • Trend
  • Z-statistic
  • P-value
  • Sen's Slope
  • Intercept

The software can identify the resulting trend as:

  • Increasing
  • Decreasing
  • No trend

Analysis can be performed for overall monthly observations as well as individual calendar months.


Sen's Slope

The software also calculates Sen's Slope along with the Mann–Kendall results.

Sen's Slope provides an estimate of the magnitude and direction of a trend, allowing researchers to examine not only whether a trend exists but also its estimated rate of change.

This can be useful for studies investigating long-term:

  • Rainfall trends
  • Temperature trends
  • Climate variability
  • Climate-change signals

Multi-Location Climate-Change Analysis

For larger studies, manually repeating the same workflow for dozens or hundreds of locations can become time-consuming.

IMD Gridded Data Analyzer is designed to process multiple extracted datasets systematically.

This makes it useful for:

  • Climate-change assessments
  • Regional climate variability
  • Long-term rainfall trend studies
  • Temperature trend studies
  • Multi-city comparisons
  • Spatial climate studies

A typical multi-location workflow is:

Prepare location list → Extract locations → Perform analysis → Compare results


SWAT-Compatible Data Preparation

For researchers working in hydrology and SWAT modeling, the software also provides regional SWAT data preparation.

Regional extraction can generate SWAT-compatible meteorological files and associated elevation information using the configured NASA MERRA-based elevation source.

This makes the software useful not only for climate analysis but also for hydrological modeling workflows.


Automated Excel Analysis Workbooks

The analysis module generates structured Excel workbooks that can be used for further interpretation, visualization, and reporting.

A typical workbook can contain:

Monthly Statistics

Monthly descriptive statistics for the extracted climate variables.

Mann-Kendall Trend

Trend results including:

  • Trend direction
  • Z-statistic
  • P-value
  • Sen's Slope
  • Intercept

Monthly Source

The monthly source time series used for analysis, including:

  • Date
  • Location
  • Latitude
  • Longitude
  • Rainfall
  • Tmin
  • Tmax

This provides a connection between the original extracted observations and the resulting statistical analysis.


Designed for Research Workflows

The main objective of the software is not simply to provide another way of viewing climate data.

It is designed to reduce repetitive work involved in:

Downloading → Extracting → Preparing → Calculating → Analyzing

By automating these repetitive steps, researchers can spend more time on interpretation and scientific analysis rather than repeatedly preparing datasets.


Who Can Use IMD Gridded Data Analyzer?

The software can be useful for researchers and students working in areas such as:

  • Climate science
  • Climate-change studies
  • Hydrology
  • Meteorology
  • Environmental science
  • GIS
  • Agricultural research
  • Regional climate studies
  • Rainfall analysis
  • Temperature analysis
  • Hydrological modeling
  • SWAT modeling
  • Academic research

Download IMD Gridded Data Analyzer v1.0.3

The latest version is available from the official GitHub Releases page:

👉 Download:
IMD Gridded Data Analyzer — GitHub Releases


User Manual

A detailed user manual is available for installation, data downloading, extraction, statistical analysis, trend analysis, SWAT preparation, updates, and troubleshooting.

👉 Read the User Manual v1.0.3:
IMD Gridded Data Analyzer User Manual — PDF


Video Tutorial

A step-by-step video tutorial is also available to help users understand the software workflow.

👉 Watch the tutorial on YouTube:
IMD Gridded Data Analyzer — Video Tutorial


Final Thoughts

Long-term climate research often requires substantial time for data preparation before the actual scientific analysis can begin.

IMD Gridded Data Analyzer aims to make this process more efficient by bringing IMD data downloading, extraction, statistical analysis, trend analysis, multi-location processing, and SWAT data preparation into one research-oriented workflow.

From data preparation to climate insights — in one workflow.

If you are working with IMD gridded climate data for a research project, thesis, dissertation, climate-change study, hydrological study, or SWAT model, you can explore the software and share your feedback for future development.

IMD Gridded Data Analyzer v1.0.3 — Download, Extract, Analyze.

#IMD #IMDData #ClimateData #ClimateResearch #ClimateChange #RainfallAnalysis #TemperatureAnalysis #MannKendall #SenSlope #Hydrology #SWAT #GIS #Meteorology #EnvironmentalScience #ResearchSoftware #IndianClimateData

Friday, 12 September 2025

AI for Results and Analysis writing UDEMY Coupon

 We all are experts and can do any GIS analysis easily. But the problem starts when publishing a paper or writing a thesis, and the work of review of literature starts. We download hundreds of papers, but only a few of them are related to our research. To write a single review, we need to read the entire paper, sum up the major findings and methods, and then write a review. This wastes a lot of our time. Even when we have written the entire paper, sometimes it is rejected due to poor English or badly formatted writing. All of this is solved in this course using AI. #GIS #Research

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I know how frustrating paper writing can be. Many researchers face the same problems:

  •  Papers rejected because of weak writing or structure.
  •  The tedious task of literature review that consumes weeks.
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  •  Hours wasted on formatting references and citations.

This course is designed to solve exactly these problems. Using your own research data, you’ll learn how to:
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The best part? This course emphasizes ethical use of AI — you stay in control, ensuring your work is original and credible, while saving weeks of effort.

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Friday, 27 January 2023

Crop yield estimation using remote sensing and GIS video tutorial

Crop yield estimation is a crucial aspect of agricultural management and planning. Accurate and timely yield estimates can help farmers make informed decisions about planting, fertilization, irrigation, and harvest timing. Remote sensing is a powerful tool that can be used to estimate crop yields with a high degree of accuracy. One of the most commonly used indices in remote sensing for crop yield estimation is the normalized difference vegetation index (NDVI). In this article, we will explore the use of NDVI in conjunction with regression equations to estimate crop yields using remote sensing.
NDVI is a commonly used index in remote sensing that measures the amount of vegetation cover in an area. NDVI is calculated by taking the difference between the near-infrared and red bands of a multispectral image and dividing that difference by the sum of the near-infrared and red bands. NDVI values range from -1 to 1, with higher values indicating more vegetation cover. NDVI is highly correlated with crop growth and yield, and it can be used to estimate crop yields with a high degree of accuracy.
Regression equations can be used to create a relationship between NDVI and crop yield. These equations can be used to estimate crop yields based on NDVI values, making it possible to estimate crop yields using remote sensing data. The regression equation can be developed by collecting data on NDVI and crop yield from a specific crop and area, and then using that data to create a mathematical equation that describes the relationship between NDVI and crop yield.
Remote sensing data can be used to estimate crop yields by collecting NDVI images of the crop and then applying the regression equation to the NDVI values. The resulting estimates of crop yield can be used to make informed decisions about planting, fertilization, irrigation, and harvest timing. Additionally, remote sensing can be used to estimate crop yields across a large area, making it possible to identify areas with the highest crop yields and target resources and attention accordingly.
In conclusion, NDVI is a commonly used index in remote sensing for crop yield estimation. Regression equations can be used to create a relationship between NDVI and crop yield, which can be used to estimate crop yields using remote sensing data. Remote sensing can be used to estimate crop yields across a large area, making it possible to identify areas with the highest crop yields and target resources and attention accordingly. Crop yield estimation, NDVI, remote sensing, regression equations, crop growth, agricultural management, planting, fertilization, irrigation, harvest timing, precision agriculture.


Highlights :

  1. Use Machine learning method for crop classification in ArcGIS, separate crops from natural vegetation

  2. The model was developed using the minimum observed data available online

  3. Crop NDVI separation

  4. Crop Yield model development

  5. Crop production calculation from GIS model data

  6. Identify the low and high-yield zones and area calculation

  7. Calculate the total production of the region

  8. Validation of developed model on another study area

  9. Validate production and yield of other areas using a developed model of another area

  10. Convert the model to the ArcGIS toolbox





Saturday, 31 December 2022

Udemy GIS Coupon code for 2023

 ArcSWAT: Covers hydrological simulations and weather data preparation using R; use it for any research area, and scripts are available for download.

Link: https://www.udemy.com/course/running-arcswat-model-with-arcgis-for-any-study-area-gis/?couponCode=HAPPYNEWYEAR2023

SWAT CUP: The simulated flow may not always completely match the observed flow. In this scenario, we must establish hydrological parameters. SWATCUP handles it automatically and recommends the best match settings.

Link: https://www.udemy.com/course/swat-cup-calibration-validation-and-write-values-to-arcswat/?couponCode=HAPPYNEWYEAR2023

 

GROUNDWATER: This course covers the use of GIS for groundwater investigation using theoretical parameters. Create a live project from scratch.

Link: https://www.udemy.com/course/groundwater-potential-zones-using-gis-full-project-arcgis-tutorial/?couponCode=HAPPYNEWYEAR2023

 

LANDUSE LANDCOVER: Covers landuse classification of high-resolution data. You will learn how to correct error pixels such as urban and barren land, or agriculture and natural vegetation. It covers most classification methods, such as machine learning, supervised, unsupervised, and pixel-level recoding of challenging images.

Link: https://www.udemy.com/course/land-use-land-cover-classification-gis-erdas-arcgis-envi/?couponCode=HAPPYNEWYEAR2023

 

FUTURE LANDUSE: Remote sensing has impressive capabilities to generate future landuse classified image of  2090, even if it is not captured in the present; see machine learning in action. No coding is required.

Link: https://www.udemy.com/course/prediction-of-future-land-use-remote-sensing-and-gis-terrset/?couponCode=HAPPYNEWYEAR2023

 

BASIC GIS: This course covers more than a university lab class if you are new to GIS. This course will teach you about 3D, data analysis, and satellite image processing. 11hrs of hands-on videos of practicals.

Link: https://www.udemy.com/course/complete-basic-gis-tasks-arcgis-erdas-remote-sensing/?couponCode=HAPPYNEWYEAR2023

Tuesday, 11 October 2022

Online GIS courses

Just click on links Discount applied.

Dear Research Scholars,

GIS courses updated with machine learning and R. Discount is applied on all links valid for 5 Days.

The ArcSWAT course is updated with more than 2 hr 26 minutes of video. Now it covers weather data management using R programming. It was experienced that weather data for swat was not available. So new section is added for data management.

Link: https://www.udemy.com/course/running-arcswat-model-with-arcgis-for-any-study-area-gis/?couponCode=SWATOFF90

 

Sometimes we found simulated flow does not perfectly match with observed flow. In this case, we need to set hydrological parameters. SWATCUP do it automatically and suggests the best fit values. That values are again written to SWAT to perfectly run and calibrated the swat model for a specific watershed. That also called sensitivity analysis.

Link: https://www.udemy.com/course/swat-cup-calibration-validation-and-write-values-to-arcswat/?couponCode=SWATCUP90OFF

 

If new to GIS, then this course covers more then a university lab class. Covers everything about remote sensing and GIS, including different ways of georeferencing, Mosiac, excel to GIS, hydrology, 3D views, adding data from servers,  and much more GIS  more than 11 hrs tutorial. Only 99% practical. Covering ArcGIS, Erdas, and some QGIS.

Link: https://www.udemy.com/course/complete-basic-gis-tasks-arcgis-erdas-remote-sensing/?couponCode=GISOFF90

 

This covers the application of GIS for groundwater exploration based on theoretical parameters linked with GIS. The same method will be used for drought or flood.

Link: https://www.udemy.com/course/groundwater-potential-zones-using-gis-full-project-arcgis-tutorial/?couponCode=GWDOFF90

 

Covers landuse classification, Landsat 8, managing of high-resolution data of Sentinal-2, Pixel editing of wrong classified pixels. Post correction of classified image, Number of methods covered, like machine learning-based classification and all other supervised and unsupervised methods.

Link: https://www.udemy.com/course/land-use-land-cover-classification-gis-erdas-arcgis-envi/?couponCode=LANDUSEOFF90

 

Use the output of landuse classification as input for terrset based on CA Markov model. Predict and generate a map of the future landuse of 2050 or 2090 by Machine learning.

Link: https://www.udemy.com/course/prediction-of-future-land-use-remote-sensing-and-gis-terrset/?couponCode=CAMARKOVOFF90

 

Thursday, 15 July 2021

Udemy GIS course coupons

 

How to learn GIS at home, then these courses are for you. 

---

Course Name: ArcSWAT Model with ArcGIS - Run for any Study Area - GIS

Duration: 4hr 26min

Highlights: Watershed simulation, amount of sediment reach at the outlet, ET, PET, Water Yield, Groundwater Recharge estimates, Streamflow in m3/sec, Hending weather data, Cover zero to simulation graphs. Applicable for ArcGIS 10.1 to ArcGIS 10.7

Link:https://www.udemy.com/course/running-arcswat-model-with-arcgis-for-any-study-area-gis/?couponCode=LABATHOME


Course Name: SWAT CUP Calibration Validation and write values to ArcSWAT

Duration: 2hr 37min

Highlights: Multiple concepts of calibration and validation, Write calibrated parameter values to ArcSWAT.

Link: https://www.udemy.com/course/swat-cup-calibration-validation-and-write-values-to-arcswat/?couponCode=LABATHOME



Course Name: Land use Land cover classification GIS, ERDAS, ArcGIS, ENVI + Machine Learning in GIS

Duration: 6hr 22min

Highlights: A number of classification methods are covered including Machine learning. From data downloading to final results. Also covers pixel editing to improve LULC accuracy, Covers Accuracy assessment, Post modification and a number of basic and advanced tasks.  Applicable for Erdas 14 to Erdas 18. All versions of ArcGIS.

Link: https://www.udemy.com/course/land-use-land-cover-classification-gis-erdas-arcgis-envi/?couponCode=LABATHOME


Course Name: Future Land Use with GIS - TerrSet - CA Markov - ArcGIS

Duration: 4hr 4min

Highlights: How an urban are expand in future at which extended till the year 2030, 2060 or 2090. Based on past classification and factors affecting landuse incorporated in the software. Convert the theoretical concept of urban expension into relaity.  Generate Video simulation of Urban growthAlso, generate a map of the future landuse with the machine learning approach. No coding is required. TerrSet and ArcGIS software required.

Link: https://www.udemy.com/course/prediction-of-future-land-use-remote-sensing-and-gis-terrset/?couponCode=LABATHOME

Course Name: Complete Basic GIS Tasks - ArcGIS - Erdas - Remote Sensing

Duration: 11hr 7min

Highlights: If you are new to GIS then this 11 hrs course cover everything in GIS from data management to publication-ready maps. Rea;l life tasks are covered. Covers GIS, GPS, Excel,  Weather data handling, Advance scripts, Image mosaicking in the best way, image resolution increase, Processing of high-resolution free images of 10m, 30m resolution. 3D and much more. Applies to ArcGIS 10.1 to 10.8, Erdas 2014, 2015 and 2018.

Link: https://www.udemy.com/course/complete-basic-gis-tasks-arcgis-erdas-remote-sensing/?couponCode=LABATHOME

Friday, 8 May 2020

Online GIS course 28 hrs



The combined duration of all GIS courses 28hr 25min. If you spend 1hr daily, you will be expert in GIS within 28 days.
Even you will be able to do a complete GIS Project within one Day if works continuously. All courses are designed in such a way that you will feel GIS is too comfortable. Even Larger simulation of Hydrology will be just like the word for you.
Basic of GIS and Remote Sensing: This course is scientifically designed you no need to put a load on your brain to understand typically task, Just by watching the video, you will understand an advance concept. Many of you demanded to make a course on Basic of GIS data. As we face problems in handling shapefiles and data handling, Mosaic of the satellite image, NDVI, Handling advance data, cutting study area. Increasing image resolution and even making research-ready layouts. So here is the course.
Duration 11hr 7min

Land use land cover classification in Depth Learn from scratch from data download to landuse change detection, How to improve images, How to classify bad pixels manually. Post landuse classification process, Accuracy assessment, you will also learn many of Basic GIS tools also. Supervised, Unsupervised and Combined method are covered. You will receive more accuracy in the combined method for even bad quality terrain.
Duration 5hr 13min

ArcSWAT Tutorial - Learn Watershed Simulation and working with weather data in GIS, In this course you will learn ungauged catchment water flow simulation in  Cubic Meter Per Second [m³/s] , groundwater recharge in watershed,  Sediment at outlet, Water yield, water quality, Curve number, Soils, Hydrological cycle parameters of watershed and many more advance output as you think. Duration 4hr 12min

Groundwater Potential Zones : In this course you will learn how to use landuse, DEM and Weather data. By generating stream density. Mosaic of DEM. How to run overlay analysis on multiple data set and many basic task of GIS like download  of weather data, convert to GIS. You will also learn ow to calculate water level fluctuation and generate map. You ca use same tutorial for Drought, site suitability or any other theme also, even this method applicable to Flood risk zones.
Duration 3hr 49min

Prediction of Future Landuse: In this course you will learn how to generate future landuse maps with GIS. How to use present data and simulate urban growth to future. How to validate predicted results. How to Modify image Matrix to get desire results. This method use, MLP and Machine learning based method. No single line code required. Only requirement is you must have well classified two satellite image with accuracy more than 80%, Rest of things covered in this course. Its so much easy then you think you just need Terrset and ArcGIS.
Duration 4hr 4min



Tuesday, 14 April 2020

GIS course udemy Coupon - Complete Basic GIS Tasks - ArcGIS - Erdas - Remote Sensing

Complete GIS Course from scratch to Advance

This all course covers data processing in advance way and solving general GIS problems.
Discounted already applied to links.
All are discounted links.
In this course you will also learn
· Groundwater Potential Zones using.
· Site suitability
· This also covers Flood Zones
· Drought Zones
· Groundwater Fluctuation Maps.
· Like Using Excel Data in ArcGIS,
· Plotting Maps,
· Classify various type of Data.
· Handling CDF files etc.
· Working with excel data in ArcGIS
· Digital elevation model analysis
· Watershed



In this course you will learn
· How to generate future landuse maps using past landuse
· Power impacting landuse and use in Terrset
· Getting data ready for Future Prediction
· Running of CA Markov Model
· See power of Machine learning in Action


https://www.udemy.com/prediction-of-future-land-use-remote-sensing-and-gis-terrset/?couponCode=FUTURE_GIS_UDE

In this course you will learn
· Simulation of water flow in stream
· Daily Weather data including,
· Working with Temperature, rainfall, humidity, solar radiation
· Groundwater recharge.
· Making of Soil map of your study Area
· Extracting soil Properties
· DEM Mosiac
· Use of Landuse data in Model
· Soil Erosion at outlet


In this course you will learn
This course starts from Google Search to Final Results
· Covers basic of Image processing and data download
· Landuse land cover classification
· Supervised
· Unsupervised
· Combined classification
· Pixel Re-coding
· Correction Hill shade land feature
· Understanding satellite image and identifying features
· Accuracy Assessment
· Change Detection


In this course you will learn
This course starts from Georeferencing to Advance GIS - 11hrs
· Covers all basics of GIS 
· Understanding of Projection system
· Handling Raster and Vector data
· Basics of GIS digitization in depth
· Basics of all analysis
· All task of Image processing including mosaic 
· Working with 10 meter data
· Working with Survey data
· Weather data
· Using queries in GIS

· DEM - volume and 3D
· 3D animation in GIS
· Making publication ready maps.

Saturday, 11 April 2020

GIS Basics Video Tutorial


The theory behind tools, how it works. Hands-on That tool. Practical. Mosaicking, satellite data, Digital Elevation Model, Most of the things covered that required for Mast of GIS students, PhD students and Civil Engineers, Irrigation and Hydrology Engineers. If you have missed your practical classes of GIS, then this course is for you. It covers the whole practical syllabus of GIS, even more than it. It also covers Error resolving is software, issue with Satellite data. Compared same task output on different software. This will enable you to do any real life project. Content are decide based on real life problems which my Engineer student faced in field. Sometimes satellite image not provide sharp resolution data then we take Help of Google Earth, How use that Data in GIS. Even how we can use our Good Android Phone to Survey up to 3 meter GPS accuracy by calibration its GPS. Later how we can use in GIS. Other than working with Data we need to represent in Best way. A best presentation of Data is considered to be Good work, so I have also covered how to make research ready GIS layouts. How we can improve satellite image improve resolution up to 15 meter and Processing of 10-meter satellite data. Getting Earthwork of Reservoir volume, converting to 3D. Mosaicking of Digital Elevation Model. Getting Drainage and watershed, stream order. Changing the projection of data. Advance labelling using scripts. Handling NetCDF data. Generation annual rainfall map and interpolation of Data. On Other side NDVI is covered. Cutting Study area for project and deleting bad data from satellite image and vector files using smart tricks covered. How to reference data without latitude longitude and make it usable. Even how to get street level data from online sources and convert to GIS format. Cutting shapes with shapes. Handling attributes and calculation on that. Data conversion between raster and vector of multiple type. Small mini project also shown how to use combination of tools to do one task. Even to find the right UTM Zone for your area. Also covers Excel data to GIS. Getting lat long, Making Grid, If you missed your GIS practical classes, then it is for you.
It does not matter which version of the software you have. Tool covered applicable to all version of ArcGIS 10.1 or 10.7 or above. Similarly Applicable to all versions of Erdas 2015 or 2018 and above.

Sunday, 9 December 2018

Estimation of Groundwater Potential Zones using GIS and Remote Sensing

Estimation of Groundwater potential zone using GIS is and very easy work. Even Just basic knowledge of GIS is Required. You Just need to know to concept how it works and how to take that work from form GIS data. Groundwater Potential zone is just an estimate of possible location of water availability. But it does not tell the water depth. But if you want to know monitor changes in water depth in this case you need to work with actual data and plot that data in GIS. You can also calculate water level changes and location by using GIS if you have observation data. Groundwater Required few layers like Land Use, Rainfall, Soil, Drainage density,  DEM, Even number of layers is not limited you can use any number of layer as per information available, like Geomorphology also. This project is sometime given to Master of PhD students. But actually this is work of Just one day if you have good internet connection and ability to work Just 8 hrs continuous. Only write up will take time. So here is step by step Video tutorial is available for this project. It covers from data downloading to final results, Even the same tutorial covers Drought and Flood risk zone also, because it use same data.  High Quality GIS contents are not free, But too cheap also as 10$. If you learn GIS offline this will cost you thousand of dollars and at last you not have single video of course. You purely depend on you notebook. But if everything is available in video you can do it better, watch any step anytime rewind it. So All reading this post have access to this course using special link. Below this post. 
  • Successfully identify Groundwater Potential Zones
  • Also Drought prone Area analysis
  • Flood Risk map
  • Working with Rainfall Data
  • Generate Soil Map and identify Soil Properties
  • Delineate Watershed
  • Drainage Density and Drainage Maps
  • Mosaic of Digital Elevation Model
  • Area Calculation from Pixels
  • Data Interpolation
  • Adding Data from Excel to GIS Environment
  • Perform GIS data calculations
  • Tool used for Site suitability analysis
  • Working with CDF file and data Extraction, Map Generation, Calculation,
  • Data Reclassification
  • Generate Fluctuation maps to identify Good Aquifer.
  • Data Reproject
  • Calculation with Raster Data
  • Data management in GIS
Are there any course requirements or prerequisites?
  • Basic GIS
  • Must have Landuse image
  • You must have ArcGIS any Version
Who this course is for:
  • GIS Student
  • GIS Professionals
  • Professors
  • Scientists
  • Master Student of Remote Sensing and GIS
  • PhD Research Scholars
  • Groundwater Exploration Engineers
  • Irrigation Engineers
  • Water Resource Engineers
  • Students of WRDM
  • Govt. Policy Makers
  • Ministry of Earth Science

Groundwater Potential Zones using GIS




Wednesday, 5 September 2018

CA Markov Method of Urban Growth using GIS Tutorial

CA Markov, People search lot of about CA Markov. But there is no proper GIS tutorial which Explain from Basic to run this model. If we know basic of GIS and data handling in Gis we will be easily able to run this Model. Its not a big deal. You just need ArcGIS and Terrset software to predict future land use and generate map for future. Think Few General points in Mind How it works. Lets think:
  1. New Urban area wll grow at boundary of older
  2. Some factor depends like distance between settelments
  3. Fistance from road
  4. Trend of change from Agriculture and forest each location
  5. Rate of Growth,
  6. Power of factor of change
And many More. You need only to model some layers and put in Machine learning model when the model trained put input to CA Markov model. This will predict future landuse map. If you found accuracy is not matching then Just change sampling rate and use hit and trail until it matches with actual land use if matched perfectly then Just o future and predict for year 205 and 2100.


See video of output.