International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022
p-ISSN: 2395-0072
www.irjet.net
Climate Monitoring and Prediction using Supervised Machine Learning Rudransh Kush1, Shubham Gautam2, Sai Suvam Patnaik3, Tanisha Chaudhary4 1,2,3,4Student,
Department of Computer Science and Engineering, Bennett University, Greater Noida, Uttar Pradesh, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract – The objective of the research paper is to firstly
analyse about the problems occurring due to the climatic changes happening irregularly, and therefore arising the need of a climate or weather monitoring Machine learning based prediction system. Aim of the paper will be fulfilled by proposing a methodology related to predicting precipitation type and Weather climate by considering several other factors also on which environment depends both directly and indirectly. There are various factors like cloud formations, wind patterns, continentally which impact climate predictions. Current climate systems [1, 2] have been fairly successful in including such factors. But this success comes with some limitations, this is because since recent times there has been another scientifically acclaimed factor added to the list which is human society intervention ranging from deforestation for development purposes to pollution caused by industries. This paper brings forward with itself not only the methodology in order to predict the climate, but also inculcating a need for preservation of our environment by using data visualizations methods which in most cases create a bigger impact in the society. Key Words: Machine Learning, Climate, Monitoring, Prediction, Classification, Regression, Random Forest
Figure 1: Vulnerability meter in several districts range from 0 – 1.
1. INTRODUCTION
With increasing cases of sudden Events like soil eruption, sudden rainfall and wind gusts, the most affected niche is the agricultural industry. These irregularities have arisen mostly due to Global warming leading to a dire need for better and improved climate prediction and monitoring systems to be placed. There have been recurring and ambiguous patterns of natural disasters and climate fluctuations which have now been prevalent more than ever. This is a serious problem which absolutely calls for monitoring to help improve the affected economy sectors while also help reducing the human impact on the climate patterns. Climate prediction is necessary to embrace for any natural climatic deformities. Alongside these climate models is also a need for consistent, methodical, and periodical monitoring of climate and various geological events affecting it. Therefore, there is an urgent need to monitor and predict the climate changes happening all over the world due to causes identified till date.
As the world's population increases, so do the demands on ecosystems for resources and the consequences of our global impact. Natural resources are neither indestructible nor limitless. The ecological consequences of human actions are becoming more apparent: air and water quality are deteriorating, pests and illnesses are spreading outside their historical ranges, and deforestation is worsening flooding and biodiversity loss downstream. Ecosystem services are not only restricted, but they are also threatened by human activity, as society is increasingly conscious. Long-term ecosystem health and its role in permitting human settlement and economic activities must be prioritized. There are several scientific evidence which are highly suggestive of increase in variability of climatic events. These events have proved to be a major factor affecting human ecosystem and several factors of the general economy. There are several irregularities as seen in the past which has caused disruption to many sectors that are influenced by it, with one such sector being agriculture.
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2. METHODOLOGY Dataset Preparation. The methodology will be demonstrated in this section in order to layout proper flow of the model as proposed to predict and monitor the irregular
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