The Institution of Engineers (India)
Question
Asked 23rd Apr, 2023
How can we predict precipitation distribution in practice?
Knowing this distribution provides a good chance to regulate crop planting date to be shorter than usual state to consume less water in order to be more adaptive in climate changes circumstances.
All Answers (3)
Rainfall Prediction is the application area of data science and machine learning to predict the state of the atmosphere. It is important to predict the rainfall intensity for effective use of water resources and crop production to reduce mortality due to flood and any disease caused by rain. Precipitable water is measured using information from weather balloons and water vapour imagery from weather satellites. The equation of the line is given as Y=wx+b. It provides an estimate of rainfall using various atmospheric variables like cloud cover, humidity, wind, and average temperature to predict rainfall.it is accepted that the main variables for predicting precipitation are temperature and humidity . Nevertheless, there are other four meteorological parameters—air, dewpoint temperature (or relative humidity), wind speed, and cloud cover—which are strongly correlated with rainfall.To predict rainfall, several types of research have been conducted using data mining and machine learning techniques of different countries’ environmental datasets.To choose the better machine learning algorithms to study the daily rainfall amount prediction, various papers can be reviewed concerning rainfall prediction. To predict the daily rainfall intensity using the real-time environmental data, three algorithms such as MLP, RF, and XGBoost gradient descent can chosen for the experiment. Hence, the three machine learning algorithms experiment with and compared to report the better algorithms to predict the daily rainfall amount.
Conference Paper Prediction of rainfall using image processing
University of Agriculture Faisalabad
Predicting precipitation distribution involves a combination of both theoretical and observational methods. Here are some common approaches used in practice:
- Meteorological Models: These are computer models that simulate the behavior of the atmosphere and predict future weather conditions based on mathematical equations. Precipitation can be predicted using meteorological models that take into account atmospheric conditions such as temperature, humidity, pressure, wind speed and direction.
- Satellite Imagery: Satellites equipped with sensors can measure cloud cover, water vapor, and temperature of the Earth's surface. This information can be used to determine the likelihood of precipitation in a given area. The data collected by satellites is analyzed to create maps of precipitation distribution.
- Radar: Doppler radar can detect precipitation in the atmosphere by bouncing radio waves off precipitation particles. The data collected by radar can be used to create maps of precipitation intensity and movement.
- Historical Data: Past precipitation data can be used to create a model of future precipitation distribution. This can be done by analyzing past weather patterns and trends, as well as using statistical methods to identify correlations between different variables.
- Ensemble Forecasting: This involves running multiple simulations with slightly different starting conditions to create a range of possible outcomes. This method helps to identify the most likely scenarios and potential outliers.
It's important to note that predicting precipitation distribution is complex and not always accurate. The use of multiple methods and data sources can help improve accuracy, but it's important to also consider the limitations of each method and the uncertainty associated with any prediction.
Similar questions and discussions
How to calculate which agri farming field needs irrigation first?
We are currently working on a sensoring system which measures soil humidity, temperature, nitrogen, phosphor, potassium and ph level. We are trying to stimulate the efficiency of farming and support local farmers. One of our problems is that we are able to measure the level of humidity in percentages but this does not help farmers enough. Our advice is not accurate when we just compare the level of humidity of different farming fields and do not account for other factors like rainfall, temperature/humidity of the air and maybe even other factors like different crops. My question is: is there a formula that is able to calculate which farming field would need irrigation the most. We would like to be able to calculate every field and give a ranking which field would need irrigation the most, we think this way we could help save water and also stimulate the food supply.
If you think you could help me any way feel free to respond on this post or send me a direct email on mjd.lacrois@student.han.nl. Thank you in advance!
How to convert nitrogen in mg/kg to kg/ha?
We developed a sensoring system for monitoring agricultural soil. But struggle with finding the right formula to convert mg/kg to kg/hectare. The formula we got right now is shown below and we use nitrogen for example.
(mg N/kg x soil density in g/cm3 x depth of measuring in cm x humidity/moisture level of soil in %)10
For example the total nitrogen in mg/kg= 1230mg/kg
Soil density= 1.474 g/cm3
Measuring depth= 25 cm
Humidity/ moisture level=20%
/10 to convert mg/kg to kg/ha
All the values given above are factual only the moisture level is estimated because that was not given in a report given to us.
(1230x1.474x25x0.20)/10= 906.51
which is false because the actual total Nitrogen in kg/ha is 4530
the formula would be correct if it would end with /2 instead of /10 so there is a mistake with a factor of 5 but we can not identify it. It might be the moisture level being incorrect or something else.
If anyone has the solution or an other formula I would love to hear from you.
this is my mail if you want to contact me directly mjd.lacrois@student.han.nl
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