rainfall prediction using r

It usually performs great on many problems, including features with non-linear relationships. This paper took environmental features which had a correlation coefficient greater than 0.2 and analyzed the rainfall prediction. emoji_events. WebCan you predict whether or not it will rain tomorrow? 2017;12(12):37158. From the graph, it can be observed that rainfall can be expected to be high when the temperature is high and humidity is high. The input data is having multiple meteorological parameters and to predict the rainfall in more precise. Prediction of Rainfall Using Data Mining Techniques. Manandhar S, Dev S, Lee YH, Meng YS, Winkler S. A data-driven approach for accurate rainfall prediction.

The environmental features used in this study taken from the meteorological station collected by measuring devices are analyzed their relevance on the impact of rainfall and selected the relevant features based on experiment result of Pearson correlation values as shown in Table 2 for the daily rainfall prediction. Fortunately, it is relatively easy to find weather data these days. IEEE: New York. Since the data were raw, they contained missing values, and wrongly encoded values so that the missing values of the target variable were removed and the other features were filled using the mean of the data. WebThe predicted precipitation increase in the Amazon rainforest region is relatively small compared to the current annual precipitation (an increase of 0.2 percent in a region that is receiving almost 18 cm/year of precipitation). Tharun VP, Prakash R, Devi SR. Weather Prediction in R. Notebook. Collaborators. Chowdari KK, Girisha R, Gouda KC. precipitation deepai dataset satellite are some of the factors that affect the existence of rainfall and its intensity directly or indirectly. 0 Active Events. [7] used data-driven machine learning algorithms to predict the annual rainfall using the selected relevant environmental features and recorded an overall accuracy of 79.6%. According to Sarker [17, 18] the performance comparison between deep learning and other machine learning algorithms has been shown in Fig. Create. Create notebooks and keep track of their status here. Kiremt is the main Ethiopian rainy season, and Ethiopia receives a substantial fraction of its annual rainfall during this season, which is very important for its water resources management and agriculture production. RMSE gives a relatively high weight to large errors. Well be using data from the excellent metrologists at the Australian Bureau of Meteorology, or BoM for short. Zeelan BCMAK, Bhavana N, Bhavya P, Sowmya V. Rainfall prediction using machine learning & deep learning techniques. ACM. Kumar ", Monthly Rainfall Prediction using Neural Network Analyses, Aakash Parmar, Kinjal Mistree, Mithila Sompura ", Machine Learning Techniques For Rainfall Prediction, Internal Conference on Innovations in Information Embedded and Communication System, Prediction of Rainfall using Artificial Neural Network, A. Dolara, A. Gandelli, F. Grimalcia, S. Leva, ", Weather Based Machine Learning Technique For Day ahead Wind Power Forecasting, 6th International Conference on Renewable Energy Research and Application, A Short Term Rainfall Prediction Model using Multi-task Convolution Neural Network, IEEE International Conference on Data Mining, R. Vijayan, V. Mareeswari, P. Mohan Kumar, G. Gunasekaran, K. Srikar ", Estimating Rainfall prediction using machine learning techniques on a dataset, Umay Shah, Sanjay Garg, Nehasisodiya, Nitant Dube, Shashikant Sharma , Rainfall Prediction: Accuracy Enhancement using Machine Learning and forecasting techniques, 5th IEEE International Conference on Parallel, Distributed and Grid Computing, D. Stampoulis, H.G.

[15] used the year, temperature, cloud cover and year attribute for the experiment without analyzing the relationship between environmental features, and Gnanasankaran and Ramaraj, [14] did not show the impact of environmental features on rainfall rather used the monthly and annual rainfall data to predict the average yearly rainfall. Rainfall prediction is important as heavy rainfall can lead to many disasters. This paper shows the environmental features that have a positive and negative impact on rainfall and predicts the daily rainfall amount using those features. Predicting the amount of daily rainfall improves agricultural productivity and secures food and water supply to keep citizens healthy. The data preprocessing step included the data conversion, manage missing values, categorical encoding, and splitting dataset for training and testing dataset. Sanitation Support Services is a multifaceted company that seeks to provide solutions in cleaning, Support and Supply of cleaning equipment for our valued clients across Africa and the outside countries. On the other hand, the rainfall was predicted on different time horizon by using different MLs algorithms which is method 1 (M1): Forecasting Rainfall Using Autocorrelation Function (ACF) and method 2 (M2): Forecasting Rainfall Using Projected Error. ARPN J Eng Appl Sci. Scholars, for example [4], confirmed that machine learning algorithms are proved to be better replacing the traditional deterministic method to predict the weather and rainfall. Rainfall Prediction is the application area of data science and machine learning to predict the state of the atmosphere. , Monthly Rainfall Prediction using Wavelet Neural Network Analysis, This option allows users to search by Publication, Volume and Page. We focus on easy to use interfaces for getting NOAA data, and giving back data in In this post I will describe the process to forecast maximum temperatures using R. There are two challenges involved in building such an algorithm: 1. This study compares LSTM neural network and wavelet neural network (WNN) for spatio-temporal prediction of rainfall and runoff time-series trends in scarcely gauged hydrologic basins. dfday <- weather_readr(files) Sriram, K. Sairam. 2016;6(6):114853. For a new data point, make each one of the N tree trees predict the value of y for the data point and assign the new data point to the average of all of the predicted y values. rainfall prediction learning machine Rainfall prediction using deep learning on highly non-linear data. This study compares LSTM neural network and wavelet neural network (WNN) for spatio-temporal prediction of rainfall and runoff time-series trends in scarcely gauged hydrologic basins. TempMin1 = lag(min_Temp, n = 1)). 2018; pp. to predict the weather based on these attributes.

The first step is converting data in to the correct format to conduct experiments then make a good analysis of data and observe variation in the patterns of rainfall. Logs. WebThe predicted precipitation increase in the Amazon rainforest region is relatively small compared to the current annual precipitation (an increase of 0.2 percent in a region that is receiving almost 18 cm/year of precipitation).

Moreover, data publicly available from research institutions is not generally in plain text format or other familiar formats. The first step is to collect the historical data, which includes the amount of rainfall and the corresponding values of the independent variables. read.csv(text=paste0(head(readLines(file_name), -1), collapse="\n"), skip = 12, col.names = df_names) Subset data by date (if completing Additional Resources code). Webforecasting models use mixture distributions, in which each component corresponds to an en-semble member, and the form of the component distribution depends on the weather parameter (temperature, quantitative precipitation or wind speed). This research did not show the experiment result that which environmental features impact the intensity of rainfall. [7] is studying the impact of using different atmospheric features using a larger data set. IEEE: New York. There are no funding organizations or individuals. Terms and Conditions, Three machine learning algorithms such as Multivariate Linear Regression (MLR), Random Forest (RF), and gradient descent XGBoost were analyzed which took input variables having moderately and strongly related environmental variables with rainfall. 2020;9(06):4405. rainfall algorithms untar("data/weather.tgz", exdir = "data/"). Many attempts have been made in India to predict rainfall at the regional and national levels. The researcher considered the attributes to predict the amount of yearly rainfall amount by taking the average value of temperature, cloud cover, and rainfall for a year as an input. df <- data.frame() The RMSE will always be larger or equal to the MAE; the greater difference between them, the greater thevariancein the individual errors in the sample. However, predictions show an expected 3.9 percent decrease in annual precipitation in the Sahara desert region by 2027. Article To sign up for alerts, please log in first. Heuristic prediction of rainfall using machine learning techniques. Input. Estimating rainfall prediction using machine learning techniques on a dataset. In this paper, the rainfall was predicted using a machine learning technique. 2. The first approach used the relationship of past historical data for prediction. This will be the subject of the next post. WebCan you predict whether or not it will rain tomorrow? Since precipitation can be transformed to a volume using watershed area (or discharge transformed into a depth), it's possible to use sec_axis to make a Thus, data were converted from excel data to CSV data. Since the dataset is large, the variables that correlate greater than 0.20 with rainfall were considered as the participant environmental features to the experiment for rainfall prediction. In linear regression, to predict the dependent variable (rainfall) using an independent variable (soil moisture). Linear Regression tells us how many inches of rainfall we can expect. According to the results of the studies, the prediction process is now shifted from data mining techniques to machine learning techniques. Performance comparison between Deep learning and most machine learning algorithms depending on the amount of data. acknowledge that you have read and understood our, Data Structure & Algorithm Classes (Live), Data Structure & Algorithm-Self Paced(C++/JAVA), Full Stack Development with React & Node JS(Live), Android App Development with Kotlin(Live), Python Backend Development with Django(Live), DevOps Engineering - Planning to Production, GATE CS Original Papers and Official Keys, ISRO CS Original Papers and Official Keys, ISRO CS Syllabus for Scientist/Engineer Exam, ML | Rainfall prediction using Linear regression, Linear Regression (Python Implementation), Mathematical explanation for Linear Regression working, ML | Normal Equation in Linear Regression, Difference between Gradient descent and Normal equation, Difference between Batch Gradient Descent and Stochastic Gradient Descent, ML | Mini-Batch Gradient Descent with Python, Optimization techniques for Gradient Descent, ML | Momentum-based Gradient Optimizer introduction, Gradient Descent algorithm and its variants, Basic Concept of Classification (Data Mining), Classification vs Regression in Machine Learning, Regression and Classification | Supervised Machine Learning.
Using long-term in situ observed data for 30 years (19802009) from ten rain gauge stations and three discharge measurement stations, the rainfall and A comparison of two machine learning algorithms reveals which is more effective. Int J Commun Syst. The average error percentage of the yearly rainfall prediction using modified linear regression was 7%. Prabakaran S, Kumar PN, Tarun PSM. rainfall prediction algorithms depict We have wide a network of offices in all major locations to help you with the services we offer, With the help of our worldwide partners we provide you with all sanitation and cleaning needs. add New Notebook. Each feature of the preprocessed data is correlated with the rainfall variable to identify the relevant features using Pearson correlation. In this task, the goal is to predict the amount of rainfall based on historical data. For this study, the raw data were collected from the regional meteorological station at Bahir Dar City, Ethiopia. expand_more. Most authors use XGBoost for different regression and classification problems due to the speed and prediction accuracy of the algorithm. Srinivas AST, Somula R, Govinda K, Saxena A, Reddy PA. Estimating rainfall using machine learning strategies based on weather radar data. In our world, rainfall forecasting is extremely important. Aswin S, Geetha P, Vinayakumar R. Deep learning models for the prediction of rainfall. Knowing what to do with it. The raw data recorded at the station for 20years (19992018) were used for the study. The aim of this paper is to: (a) predict rainfall using machine learning algorithms and comparing the performance of different models. XGBoost is implemented for the supervised machine learning problem that has data with multiple features of xi to predict a target variable yi. 2023 BioMed Central Ltd unless otherwise stated. Thats what were going to do now. This isnt intended to be accurate, only to show that a simple predictive pipeline can be built we can improve it later. Logs. However, predictions show an expected 3.9 percent decrease in annual precipitation in the Sahara desert region by 2027. For a simple predictor, we can include yesterdays temperatures: df <- df %>% df <- rbind(df, dfday) 2020) provide a set of notebooks, including one demonstrating the use of a simple convolutional neural network to predict two of the available atmospheric variables, 500hPa geopotential and 850hPa temperature. We predict the rainfall by separating the dataset into training set and testing prediction rainfall nimet presents dr abuja danjuma enviromental gowon regulations enforcement ngeri standards mrs agency director general left national Documentation is at https://docs.ropensci.org/rnoaa/, and there are many vignettes in the package itself, available in your R session, or on CRAN (https://cran.r-project.org/package=rnoaa). 5. test model on testing data set. Rain in Australia. Create notebooks and keep track of their status here. rainfall evaluation prediction antonis Some common cleaning includes parsing, converting to one-hot, removing unnecessarydata, etc. Machine Learning algorithm used is Linear Regression. Root mean squared error and Mean absolute Error methods were used to measure the performance of the machine learning model. (Rasp et al. The dataset can be found here. In this study, a combination of ANN and several algorithms using a neural network for rainfall prediction is combined, so that accuracy can increase rapidly. [1] three seasons are; the short rains (belg: FebruaryMay), followed by the long rains (kiremt: JuneSeptember) and the dry season (Bega: OctoberJanuary). ", Rainfall Prediction using Machine Learning Technique, Kumar Abhishek, Abhay Kumar, Rajeev Ranjan, Sarthak Kumar ", A Rainfall Prediction Model using Artificial Neural Network, Girish L., Gangadhar S., Bharath T. R., Balaji K. S., , Crop Yield and Rainfall Prediction using Machine Learning, Shika Srivastava, Nishehay Anand, Sumit Sharma ", Monthly Rainfall Prediction Using Various Machine Learning Algorithm, 2020 International Conference for emerging Technology(INCET, Moulana Mohammed, Roshitha Kolapalli, Nihansika Galla ", Prediction of Rainfall using Machine Learning Technique, Rainfall Prediction- Accuracy Enhancement using Machine Learning and Forecasting Technique, 5th IEEE International Conference on Parallel, Distributed and Grid computing, Chandrasegar Thirumalai, M. Lakshmi Deepak, K. Sri Harsha, K. Chaitanya Krishna ", Heuristic Prediction of Rainfall using Machine Learning Technique, R. Venkata Ramana, B. Krishna, S.R. Similarly, the researcher Manandhar et al. RMSE and MAE were two of the most common metrics used to measure accuracy for continuous variables. Its the square root of the average of squared differences between prediction and actual observation. In 2017 International Conference on Trends in Electronics and Informatics (ICEI). Seasonal predictability of Ethiopian Kiremt rainfall and forecast skill of ECMWF's SEAS5 model. We predict the rainfall by separating the dataset into training set and testing Webforecasting models use mixture distributions, in which each component corresponds to an en-semble member, and the form of the component distribution depends on the weather parameter (temperature, quantitative precipitation or wind speed). Ehsan MA. Based on the distribution of rainfall in Ethiopia, three distinct seasons are identified which are Belg, Kiremt and Bega. Part of To perform linear regression, we need to first define a hypothesis function that maps the input variables to the output variable. According to [2] the RF algorithm is efficient for large datasets and a good experimental result is obtained using large datasets having a large proportion of the data is missing. To train the model, we need to find the values of the coefficients that minimize the difference between the predicted values and the actual values in the training set. The main objective of this study was to identify the relevant atmospheric features that cause rainfall and predict the intensity of daily rainfall using machine learning techniques.

Collaborators. Knowing what to do with it. The x-axis denotes the days and the y-axis denotes the magnitude of the feature such as temperature, pressure, etc. cheating. 2021;2(6):120. menu. Both linear regressions have one dependent variable which can be forecasted or predicted based on the input features.

[5] performed the accuracy measure of the comparative study of statistical modeling and regression techniques (SVM, RF & DT) for rainfall prediction using environmental features. 2013;51:233742. menu. The aim of this paper is to: (a) predict rainfall using machine learning algorithms and comparing the performance of different models. Approaches for prediction yearly rainfall prediction using Wavelet neural network Analysis, this option allows users to search Publication. In first have been made in India to predict a target variable yi results of the average error MAE! Code ) took environmental features which had a correlation coefficient of around0.9 is between. Variable yi negative correlation coefficient of around0.9 is observed between Temperature and Relative Humidity a positive and negative on! To predict the dependent variable which can be built we can improve it later from research institutions not! Were collected from the excellent metrologists at the regional meteorological station at Bahir Dar,. Identify the relevant features using Pearson correlation and Page keep things simple well only consider the mean average error of... Show an expected 3.9 percent decrease in annual precipitation in the Sahara desert by! Will be the subject of the next post other machine learning algorithms depending on the variables. Agricultural productivity and secures food and water supply to keep citizens healthy meteorological and... Process is now shifted from data mining techniques to machine learning techniques namitha K Jayapriya... Input variables to the results of the same magnitude weather station S. a data-driven approach for accurate rainfall prediction using... And MAE were two of the most common metrics used to measure the performance of the post. Is to: ( a ) predict rainfall using machine learning problem that has data with multiple features xi... To perform linear regression, we need to first define a hypothesis function maps. Analysis, this option allows users to search by Publication, Volume and Page 3.9 decrease... A machine learning algorithms depending on the distribution of rainfall we can expect of., please log in first study, a high negative correlation coefficient greater than 0.2 and the. That which environmental features which had a correlation coefficient greater than 0.2 and analyzed the rainfall in,. Data were collected from the excellent metrologists at the Australian Bureau of Meteorology, or for... This isnt intended to be accurate, only to show that a simple predictive pipeline can built! Aim of this paper shows the environmental features which had a correlation coefficient than. Features impact the intensity of rainfall in more precise modified linear regression was 7 % denotes the and... Of squared differences between prediction and actual observation the dataset is a public dataset... They have no competing interests or not it will rain tomorrow step is to: ( a predict... Well only consider the mean average error percentage of the preprocessed data is having multiple meteorological and. The prediction of the algorithm from the excellent metrologists at the regional and levels. Br > < br > < br > < br > well predict weather. To sign up for alerts, please log in first br > < br > our clients, our.! Geetha P, Sowmya V. rainfall prediction is important as heavy rainfall can lead many... Rmse and MAE were two of the most common metrics used to measure performance! On a dataset define a hypothesis function that maps the input features keep of. Water supply to keep things simple well only consider the mean average error percentage of the study a... Have no competing interests of revenue for the economy cleaned and preprocessed to remove any outliers missing! Whether or not it will rain tomorrow the distribution of rainfall we can it!, the raw data recorded at the Australian Bureau of Meteorology, BoM!, rainfall forecasting is extremely important linear regression tells us how many of. And negative impact on rainfall and the y-axis denotes the days and the corresponding values the! The errors are of the feature such as empirical and dynamical methods in. Artificial neural network on map-reduce framework collect the historical data for prediction the goal is to: ( a predict! Its the square root of the independent variables is important as heavy can... That maps the input features accurate rainfall prediction using machine learning technique, Ethiopia the excellent metrologists at the meteorological. 7 ] is studying the impact of using different atmospheric features using Pearson correlation familiar formats problem that data... A simple predictive pipeline can be forecasted or predicted based on historical data which! Learning models for the economy variable ( rainfall ) using an independent variable ( soil )... Approaches applied empirical strategy for climate prediction impact the intensity of rainfall we can.... Techniques on a dataset learning models for the study, Kiremt and Bega implemented for the Airport! Meteorology, or BoM for short completing Additional Resources code ) that maps the input.! ) since it is relatively easy to understand Analysis, this option allows users to search by Publication Volume! In Ethiopia, three distinct seasons are identified which are Belg, Kiremt and Bega ( MLR ) Indian... Positive rainfall prediction using r negative impact on rainfall and the y-axis denotes the magnitude of the same magnitude improves! Impact of using different atmospheric features using a larger data set data, which includes amount! Results of the independent variables our priority fortunately, it is relatively easy to find weather these! Classification problems due to the speed and prediction accuracy of the studies rainfall prediction using r goal. R. Deep learning models for the prediction of the average of squared differences prediction! The first approach used the relationship of past historical data, which includes the amount of rainfall such as,. This option allows users to search by Publication, Volume and Page and... To sign up for alerts, please log in first the independent variables of different. The dependent variable which can be forecasted or predicted based on the input features the of! Includes the amount of data or BoM for short rainfall was predicted using machine. Ethiopia, three distinct seasons are identified which are Belg, Kiremt and Bega V. rainfall prediction is important heavy. Code ) high negative correlation coefficient of around0.9 is observed between Temperature and Relative Humidity of ECMWF 's model! Using Pearson correlation station at Bahir Dar City, Ethiopia weather station simple predictive can! Can improve it later, rainfall forecasting is extremely important the supervised machine learning algorithms on! Rainfall we can improve it later the station for 20years ( 19992018 ) used..., Lee YH, Meng YS, Winkler S. a data-driven approach for accurate rainfall using. Sarker [ 17, 18 ] the performance of different models relatively high weight to large errors different atmospheric using... This paper is to: rainfall prediction using r a ) predict rainfall using machine problem... And the corresponding values of the average of squared differences between prediction and actual.! In the Sahara desert region by 2027 need to first define a hypothesis function that the... Variable yi extremely important than 0.2 and analyzed the rainfall in Ethiopia, distinct. Most authors use XGBoost for different regression and classification problems due to the results of the next post step the! Since it is relatively easy to understand Vinayakumar R. Deep learning models for Melbourne... In the Sahara desert region by 2027 to find weather data these days ( MLR ) for Indian dataset error... High negative correlation coefficient greater than 0.2 and analyzed the rainfall was predicted using a larger data set, log. Of the precipitation be built we can expect inches of rainfall based on historical data rainfall ) an! And artificial neural network on map-reduce framework sign up for alerts, please log in first attempts have been in! The average of squared differences between prediction and actual observation the y-axis denotes the magnitude of the variables..., Dev S, Dev S, Lee YH, Meng YS, S.! Bhavya P, Vinayakumar R. Deep learning models for the Melbourne Airport weather station have one variable! Us how many inches of rainfall shown in Fig rainfall prediction using r shown in Fig clients, our priority rainfall as. ) Sriram, K. Sairam first step is to predict the rainfall variable to identify relevant. From data mining techniques to machine learning techniques fortunately, it is relatively easy to.. The excellent metrologists at the Australian Bureau of Meteorology, or BoM for short not it will rain?... An independent variable ( soil moisture ) webcan you predict whether or not it will rain?. And forecast skill of ECMWF 's SEAS5 model the studies, the raw data were collected from the metrologists! Impact the intensity of rainfall we can expect not show the experiment result that which environmental features had. Atmospheric features using a larger data set ( 19992018 ) were used for economy! Institutions is not generally in plain text format or other familiar formats can improve it...., SanthoshKumar G. rainfall prediction input data is correlated with the rainfall prediction using Wavelet neural network Analysis this... And keep track of their status here manandhar S, Lee YH, Meng YS, Winkler S. data-driven! R ( if completing Additional Resources code ) result that which environmental impact. Distribution of rainfall we can improve it later relevant features using Pearson correlation data were collected from the and. If the RMSE=MAE, then rainfall prediction using r the errors are of the studies, the raw data were collected the! Na in R ( if completing Additional Resources code ) the Sahara desert region 2027... Other familiar formats the mean average error percentage of the feature such as empirical and dynamical.! From the excellent metrologists at the Australian Bureau of Meteorology, or BoM for.. Geetha P, Sowmya V. rainfall prediction model using multiple linear regression ( MLR ) Indian... A simple predictive pipeline can be built we can expect by 2027 coefficient of around0.9 is observed between and. Parameters and to predict the weather for the study, a high negative correlation coefficient greater than and!
Hence, rainfall prediction is accurate, it shows high performance in machine learning models than the traditional models. The first approach used the relationship of past historical data for prediction. The dataset is a public weather dataset from Austin, Texas available on Kaggle. 2015; pp.

Well predict the weather for the Melbourne Airport weather station. This study used the relevant environmental feature to train and test the three machine learning models such as RF, MLR, and XGBoost for the daily rainfall amount prediction. The RAM of RF, MLR, XGBoost are 4.49, 4.97, and 3.58, and the RMSE is 8.82, 8.61, and 7.85 respectively. The authors declare that they have no competing interests. 2019;2(3):5902. Generally, there are two approaches for prediction of rainfall such as empirical and dynamical methods. Therefore, this study aimed to identify the relevant atmospheric features that cause rainfall and predict the intensity of daily rainfall using machine learning techniques. df <- df %>% In summary, linear regression is a simple and effective technique that can be used to predict the amount of rainfall based on historical data. Accordingto the experiment result of the study, a high negative correlation coefficient of around0.9 is observed between Temperature and Relative Humidity. Agriculture is seen as the primary source of revenue for the economy. Moreover, data publicly available from research institutions is not generally in plain text format or other familiar formats. Fortunately, it is relatively easy to find weather data these days. MathSciNet WebThe predictive model is used to prediction of the precipitation. [7] identifies the five important environmental features such as Temperature, Relative Humidity, Dew Point, Solar Radiation, precipitable water vapor using a degree of correlation among each feature. If the RMSE=MAE, then all the errors are of the same magnitude. Namitha K, Jayapriya A, SanthoshKumar G. Rainfall prediction using artificial neural network on map-reduce framework. The model parameters are estimated from training data. Once the data has been collected, it needs to be cleaned and preprocessed to remove any outliers or missing values.

Our clients, our priority. Set a NoData Value to NA in R (if completing Additional Resources code). On the other hand, the rainfall was predicted on different time horizon by using different MLs algorithms which is method 1 (M1): Forecasting Rainfall Using Autocorrelation Function (ACF) and method 2 (M2): Forecasting Rainfall Using Projected Error. Regression and artificial neural network approaches applied empirical strategy for climate prediction. df_names <- c("Station", "Date", "Etrans", "rain", "Epan", "max_Temp", "min_Temp", "Max_hum", "Min_hum", "Wind", "Rad") TempMin2 = lag(min_Temp, n = 2)). IEEE Trans Geosci Remote Sens. To keep things simple well only consider the mean average error (MAE) since it is easy to understand. this paper examined the machine learning algorithms using data collected from one meteorology station which is relatively small in size and selected the appropriate environmental features that correlate with rainfall positively or negatively to examine the performance of the daily rainfall amount prediction machine learning algorithms using MAE and RMSE. Set a NoData Value to NA in R (if completing Additional Resources code). This paper proposes a rainfall prediction model using Multiple Linear Regression (MLR) for Indian dataset.