Authors: Kaushal Kailas Sarda
Certificate: View Certificate
Weather forecasting has now entered the era of Data Visualisation due to the advancement of climate observing systems like satellite meteorological observation and also because of the increase in the collection of weather data. So, the traditional intelligence models are not adequate to predict the weather precisely. Hence, machine learning-based techniques are important to process massive datasets that can learn and make predictions more precise based on past data. This research paper provides a thorough review of different rainfall prediction techniques, along with the help of publicly available datasets. Statistical techniques for rainfall forecasting cannot predict well for long-term rainfall forecasting due to the constant change of climate phenomena every year. This paper delivers a precise classification of rainfall forecasting models and discusses potential future research methods in this area.
A forecast is calculation or estimation of future events, especially for economical trends or future weather. As an example, in the tropics regions which several countries only had two seasons in a year , many countries especially country which relies so much on agricultural field need to forecast rainfall in term to decide the best time to start the production and planning.Rainfall play important role in forming of fauna and flora of natural life. It is not just significant for the human beings but also for other species like animals, plants and all living things. It plays a significant role in agriculture and farming and ; water is one of the most natural resources on earth. The changing climatic conditions in the country and the increasing global warming effects have made it difficult for the human beings and the planet to experience necessary amount of rainfall that is required to satisfy the human needs and its uninterrupted use . Therefore, it has become significant to analyze the changing patterns of the rainfall and try to predict the rain not just for the human needs but also to predict for natural disasters that could cause by the unexpected heavy rainfalls. The prediction of rainfall has serious importance in various dimensions and scope Reducing the impact of sudden and heavy rainfall can be very beneficial by taking appropriate safety measures before any natural disaster . To be more specific and aware of the devastating climatic changing and stay updated; predicting rainfall has been the focus of computer scientist and engineers. The dynamic approach and predictions are generated by physical models and Probability Mode; based on system of equations that predict the future Rainfall. The forecasting of weather by computer using equations are known as numerical weather predictions. Numerical weather prediction (NWP) uses mathematical models of the oceans to predict the weather based on current weather conditions To predict the weather by numeric means, meteorologist has develop atmospheric models that approximate the change in humidity, temperature, etc using mathematical equations
II. RELATED WORK
Many researchers had already worked on the rainfall prediction and their connection with climate change with different methods of machine learning. Several studies are being discussed in India to predict the climate and, researchers used different lengths of information and now studies have been reported using information over a long time. Various researches have explained that predicting and analysis of monthly rainfall was suggesting different algorithms and methods. Some of them are:
There are several steps involved in the visualisation of data. From the studying of the datasets to plotting it in graphs, each step taken for the visualisation of the data is explained in this section.
A. Algorithm and Methods
This predictive model is used for prediction of the rainfall. The first step is converting data in to the correct format and data cleaning to conduct analysis then observe variations in the patterns of rainfall. We predict the rainfall by separating the dataset into training set and testing set then we apply different machine learning approaches and statistical techniques and compare and draw analysis over various approaches used in the project. With the help of numerous approaches we attempt to minimize the error.
IV. RESULTS AND METHODS
The following models were found to be suitable for studying rainfall patterns as per many reference papers:
For prediction we organized data in such a way, given the rainfall in the last three months we predict the rainfall in the next consecutive month.
The important thing is that how well the training sets, testing sets and validation data sets describe the feature space. If the number of points in the whole data set is large then any division may work fine but when the data set is limited, division ratio may play a crucial role.For all the experiments we used 80:20 training and test ratio.
a. Linear regression
b. Support Vector Regression
c. Random Forest Regressor
d. Testing metrics: We used Mean absolute error to train the models. In the research two types of trainings once training on complete dataset and other with training with only 1state data
e. All means are standard deviation and observations are written, 1st one represents ground truth, 2nd one represents predictions.
This figure shows the represents ground truth, second one represents predictions using Linear Regression Model
THIS figure shows the represents ground truth, second one represents predictions using Support Vector Regression
This paper reports a detailed report on Rainfall Predictions using different machine learning techniques extensively used over last 20 years. From the literature survey it has been found that most of the researchers used artificial neural networks for rainfall prediction and got accurate results. The research paper also gives a conclusion that the forecasting techniques that use machine learn techniques are suitable to predict rainfall than other statistical and numerical methods. The prediction of rainfall in the city utilizing three methods: supporting vector machine, random forest and linear regression. A classification system is used for successful prediction in which the input data goes via a preprocessing stage and was cleaned and normalized until the classification process. Ten preparing and test information proportions are utilized from 10:90 to 90:10 to investigate the exhibition reliance of arrangement methodologies on preparing information. Results show that the characterization procedures utilized performed well for no- downpour class however for downpour class, the systems didn\'t perform well. The purposes for the lower downpour class exactness may incorporate missing qualities, absence of significant climatic characteristics in the dataset and a lower in general precipitation rate in the district. For future work, it is proposed that further forecasts should be carried out by testing further techniques of classification and climate attributes on different weather dates. Henceforth, accuracy is based on random forest and logistic regression and future support vector machine is used to estimate accuracy.
 Estimating Rainfall Prediction using Machine Learning Techniques on a Dataset INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 06, JUNE 2020 ISSN 2277-8616 440 R Vijayan, V Mareeswari, P Mohankumar, G Gunasekaran K Srikar.  International Journal ofiComputer Application (2250-1797)iVolume 6– No.2, March April 2016 28 A Survey On Rainfall Prediction Techniques MR. DHAWAL HIRANI , DR. NITIN MISHRA  G. Tripathi and R. Gupta, \"AiNew Approach for Rainfall Prediction using Artificial Neural Network\", International Journal for Research in Appliedi Science & Engineering Technologyi(IJRASET),ivol. 7, no. VII, July 2019.  WeatheriForecasting usingiIncremental K- means ClusteringiSANJAY CHAKRABORTYiProf. N.K.NAGWANI LOP AMUDRA DEY  Heuristic Prediction ofiRainfall Using Machine Learning Techniques,May 2017,Conference: International Conference on Trends in Electronics and In formaticsiICOEI 2017 and authors:Chandra Segar Thirumalai,K Sri Harsha ,MiLakshmi Deepak,K Chaitanya Krishna  Rainfall Prediction Using Data Visualisation Techniques Yogesh KumariJo shi Department ofiCSE Amity University Uttar Pradesh, Noida, UP,iIndia UditiChawla Department ofiCSE Amity University UttariPradesh, Noida, UP,iShipra Shukla Department ofiCSE Amity University UttariPrades  Trends in the rainfall pattern over India Article in International Journal of Climatology · September 2008. Pulak Guhathakurta,India Meteorological Department Pune and M. Rajeevan M.Sc, Ph.D
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