Saturday, May 25, 2024

How to calculate feels like temperature

Let’s consider a wind speed of 11km/hr., actual temperature of 30 degrees C, and humidity of 79%. Now we calculate feels like temperature as follows:

Feels Like Temperature Calculation

Feels Like Temperature Calculation

To calculate the "feels like" temperature, we can use the Heat Index formula. The Heat Index (HI) is a measure of how hot it really feels when relative humidity is factored in with the actual air temperature. The formula for calculating the Heat Index (HI) is:

HI = c1 + c2T + c3R + c4TR + c5T2 + c6R2 + c7T2R + c8TR2 + c9T2R2


  • T is the temperature in degrees Fahrenheit.
  • R is the relative humidity as a percentage.
  • The constants are:
    • c1 = -42.379
    • c2 = 2.04901523
    • c3 = 10.14333127
    • c4 = -0.22475541
    • c5 = -6.83783 × 10-3
    • c6 = -5.481717 × 10-2
    • c7 = 1.22874 × 10-3
    • c8 = 8.5282 × 10-4
    • c9 = -1.99 × 10-6

First, we need to convert the temperature from Celsius to Fahrenheit:

T(°F) = T(°C) × (9/5) + 32

T(°F) = 30 × (9/5) + 32

T(°F) = 86

Next, we can plug the values into the Heat Index formula:

HI = -42.379 + 2.04901523 × 86 + 10.14333127 × 79 + (-0.22475541 × 86 × 79)
+ (-0.00683783 × 862) + (-0.05481717 × 792)
+ (0.00122874 × 862 × 79) + (0.00085282 × 86 × 792)
+ (-0.00000199 × 862 × 792)

HI ≈ 99.3

Converting this back to Celsius:

HI(°C) = (99.3 - 32) × (5/9) ≈ 37.4

So, the "feels like" temperature is about 37.4°C.

Calculations Summary:

  1. Convert temperature from Celsius to Fahrenheit:
  2. T(°F) = 30 × (9/5) + 32 = 86

  3. Plug values into the Heat Index formula:
  4. HI = -42.379 + 2.04901523 × 86 + 10.14333127 × 79 + (-0.22475541 × 86 × 79)
    + (-0.00683783 × 862) + (-0.05481717 × 792)
    + (0.00122874 × 862 × 79) + (0.00085282 × 86 × 792)
    + (-0.00000199 × 862 × 792)

    HI ≈ 99.3

  5. Convert Heat Index back to Celsius:
  6. HI(°C) = (99.3 - 32) × (5/9) ≈ 37.4

Therefore, the "feels like" temperature is 37.4°C.

Tuesday, April 30, 2024

Research level GIS Courses: Coupon Applied Udemy

  1. Crop Yield Estimation using Remote Sensing and GIS ArcGIS Enhance your proficiency in estimating crop yields using cutting-edge techniques in remote sensing and Geographic Information Systems (GIS) with a focus on ArcGIS. This course delves deep into the methodologies and tools essential for accurate crop yield estimation, equipping you with the skills needed to address contemporary agricultural challenges. By leveraging remote sensing data and GIS analysis within the ArcGIS platform, Enroll now to unlock the potential of remote sensing and GIS in revolutionizing crop yield estimation methodologies.

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  1. SWAT CUP Calibration Validation and write values to ArcSWAT Master the intricacies of SWAT (Soil and Water Assessment Tool) calibration and validation techniques through this comprehensive course. Explore how to effectively calibrate and validate SWAT models, ensuring their accuracy and reliability in simulating hydrological processes. Additionally, learn how to seamlessly integrate these calibrated values into ArcSWAT, empowering you to conduct sophisticated hydrological analyses with ease. Whether you're a seasoned GIS professional or a newcomer to hydrological modeling, this course offers invaluable insights to enhance your skills in watershed management and water resources assessment. Enroll today and elevate your proficiency in SWAT modeling and ArcSWAT integration.

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  1. Groundwater Potential Zones GIS - Complete Project ArcGIS Dive into the fascinating realm of groundwater potential mapping using Geographic Information Systems (GIS) and ArcGIS. This course offers a comprehensive guide to assessing and delineating groundwater potential zones, crucial for sustainable water resource management. Learn advanced GIS techniques for analyzing geological, hydrological, and environmental data to identify areas with high groundwater potential. With hands-on projects and practical exercises, you'll gain the skills and confidence to undertake groundwater assessments and inform decision-making processes. Whether you're a hydrogeologist, environmental scientist, or GIS specialist, this course equips you with the tools to make informed decisions regarding groundwater resource utilization. Enroll today and unlock the power of GIS in groundwater studies.

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  1. Land use Land cover classification GIS, ERDAS, ArcGIS, ML Elevate your expertise in land use and land cover classification using a blend of GIS, ERDAS, ArcGIS, and machine learning techniques. This course provides a comprehensive overview of the methodologies and tools essential for accurate land cover mapping and classification. Explore advanced image processing techniques, including object-based classification and machine learning algorithms, to extract meaningful information from satellite imagery. Whether you're involved in environmental monitoring, urban planning, or natural resource management, this course equips you with the skills to generate detailed land use maps and analyze land cover dynamics. Enroll now to unlock the full potential of GIS and remote sensing in land cover analysis.

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Tuesday, April 23, 2024

ArcGIS vs ArcGIS Pro

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FeatureArcGISArcGIS Pro
User InterfaceTraditional desktop application with a ribbon-style interfaceModern ribbon-style interface with contextual tabs
3D VisualizationBasic 3D capabilities through ArcSceneEnhanced 3D visualization with integrated 3D scene views
2D MappingComprehensive 2D mapping tools and functionalityAdvanced 2D mapping tools with improved cartography
GeoprocessingGeoprocessing tools available through toolboxUpdated geoprocessing tools with more options and speed
Python IntegrationPython scripting available for automation and customizationStronger Python integration with a more user-friendly environment
Web GIS IntegrationLimited web GIS integrationSeamless integration with ArcGIS Online and Portal for ArcGIS
Layouts and PrintingLayouts created using ArcMapImproved layout and printing capabilities with dynamic elements
PerformanceSingle-threaded processing, can be slower for large datasetsMulti-threaded processing, optimized for better performance
CollaborationLimited collaboration featuresEnhanced collaboration tools with project sharing
LicensingLicense based on concurrent useLicense based on named users with subscription model

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Analysis ToolArcGISArcGIS Pro
Spatial AnalysisOffers a wide range of spatial analysis tools such as buffer, overlay, and spatial statisticsExpanded spatial analysis toolbox with additional tools and improvements
Network AnalysisBasic network analysis tools for routing, service area, and network optimizationEnhanced network analysis tools with improved performance and additional functionalities
Geostatistical AnalysisProvides basic geostatistical tools for interpolation, kriging, and spatial modelingImproved geostatistical analysis tools with additional methods and model validation
Image AnalysisBasic image analysis tools for remote sensing and raster data processingEnhanced image analysis capabilities with improved raster functions and deep learning tools
Terrain AnalysisBasic tools for terrain analysis such as slope, aspect, and hillshadeAdvanced terrain analysis tools with better visualization and terrain processing capabilities
3D AnalysisLimited 3D analysis capabilities for surface analysis and 3D visualizationExpanded 3D analysis tools for terrain modeling, viewshed analysis, and 3D feature extraction
Time Series AnalysisBasic tools for temporal analysis and time series visualizationEnhanced time series analysis capabilities with better temporal aggregation and trend analysis

Difference between wind speed and wind gusts in weather

 Wind speed and wind gusts are both measurements related to the movement of air, but they represent slightly different aspects of wind behavior:

Wind Speed: Wind speed refers to the average speed of the wind over a specific period of time, usually measured over intervals such as minutes or hours. It is a continuous measurement that indicates how fast the air is moving in a particular direction at a given moment. Wind speed is typically reported in units such as miles per hour (mph), kilometers per hour (km/h), or knots (nautical miles per hour).

    Wind Gusts: Wind gusts, on the other hand, represent sudden increases in wind speed above the prevailing or average wind speed. Gusts are temporary bursts of stronger wind that occur over short periods, usually lasting only a few seconds to a minute. These sudden increases in wind speed can be caused by various factors, such as atmospheric disturbances, passing weather fronts, or local terrain features. Wind gusts are often measured alongside average wind speed and are reported as peak wind speeds reached during a particular time frame, such as within the past hour.

In summary, while wind speed represents the average velocity of the wind over a given time period, wind gusts indicate short-lived bursts of stronger wind speed above the prevailing conditions. Both measurements are important for understanding and forecasting weather conditions, especially in terms of their impact on activities such as sailing, aviation, and outdoor events.

Friday, January 27, 2023

Crop yield estimation using remote sensing and GIS 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 and geographic information systems (GIS) are powerful tools 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).  we will explore the use of NDVI in conjunction with remote sensing and GIS to estimate crop yields.
Remote sensing is the process of collecting and analyzing data about the earth's surface using sensors mounted on aircraft or satellites. These sensors can detect and measure various characteristics of the earth's surface, such as temperature, humidity, and vegetation cover. 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.
GIS is a collection of software and data that can be used to analyze, visualize, and manage geographic data. GIS can be used to analyze remote sensing data, such as NDVI images, to estimate crop yields.By developing a regression model on NDVI and crop yield. GIS can also be used to create maps that show crop yields across an entire region, making it easier to identify areas that may need additional resources or attention.
ArcGIS is a popular GIS software that can be used to analyze remote sensing data and estimate crop yields. It can be used to process NDVI images and create maps that show crop yields across an entire region. The software also has a wide range of tools that can be used to analyze and manipulate spatial data, making it an ideal tool for crop yield estimation.
Model development required a number of tools and logic to use together.  

Tutorial 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, December 31, 2022

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



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.



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Monday, October 10, 2022

Udemy course coupons GIS

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Dear Research Scholars,

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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.



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.



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Sunday, August 21, 2022

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