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  Authors

Dipanjan Mondal,Arpan Sen,Dhairya Mohan Jha,Javed Ansari,Ranjana Ray

  Keywords

rainfall prediction, machine learning, dataset, visualization, classifiers, model evaluation

  Abstract


Rainfall is of utmost importance in agriculture, but predicting it accurately has become a significant challenge in recent times. Accurate forecasts of precipitation provide valuable insights in advance, enabling individuals to take necessary precautions and devise better crop strategies. The impact of global warming on nature and humanity has accelerated changes in climatic conditions, resulting in adverse effects such as flooding and drought. As a result, accurately predicting rainfall has become one of the best techniques to understand the climate. However, forecasting precipitation is a demanding and uncertain task that significantly impacts human society. Precise and timely predictions can proactively mitigate potential human and financial losses. To address this issue, a series of experiments were conducted using prevalent machine learning methods to construct models that anticipate whether it will rain the following day based on weather data for that day in major Australian cities. The outcomes offer a comparison of diverse evaluation metrics of these machine learning approaches and their capability to predict precipitation by scrutinizing the weather data. The study aims to provide accurate climate descriptions to clients from various domains such as agriculture, research, power generation, etc., to comprehend the need for climate transformation and its parameters, such as temperature, humidity, precipitation, and wind speed, which ultimately lead to rainfall predictions. It is essential to note that predicting rainfall is a challenging task that also depends on geographic location.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2304993

  Paper ID - 235534

  Author type - Indian Author

  Page Number(s) - h707-h715

  Pubished in - Volume 11 | Issue 4 | April 2023

  DOI (Digital Object Identifier) -    http://doi.one/10.1729/Journal.34140

  No Of Downloads - 146

  Author Country - India, 741235, Kalyani, Kalyani, 741235, Science and Technology

  Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Published Paper PDF : - http://www.ijcrt.org/papers/IJCRT2304993

  Published Paper URL: : - http://ijcrt.org/viewfull.php?&p_id=IJCRT2304993

  Published Paper PDF Downlaod: - download.php?file=IJCRT2304993

  Cite this article

Dipanjan Mondal,Arpan Sen,Dhairya Mohan Jha,Javed Ansari,Ranjana Ray,   "Rainfall Prediction Using Machine Learning Techniques", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.11, Issue 4, pp.h707-h715, April 2023, Available at :http://www.ijcrt.org/papers/IJCRT2304993.pdf

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The International Journal of Creative Research Thoughts (IJCRT) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world.

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