Linear Regressions of Predicting Rainfall over Kalay Region (original) (raw)

Empirical Statistical Modeling of Rainfall Prediction over Myanmar

2008

One of the essential sectors of Myanmar economy is agriculture which is sensitive to climate variation. The most important climatic element which impacts on agriculture sector is rainfall. Thus rainfall prediction becomes an important issue in agriculture country. Multi variables polynomial regression (MPR) provides an effective way to describe complex nonlinear input output relationships so that an outcome variable can be predicted from the other or others. In this paper, the modeling of monthly rainfall prediction over Myanmar is described in detail by applying the polynomial regression equation. The proposed model results are compared to the results produced by multiple linear regression model (MLR). Experiments indicate that the prediction model based on MPR has higher accuracy than using MLR.

Rainfall Prediction in South-Eastern Part of Bangladesh by Linear Regression Method

International Journal of Emerging Research in Management and Technology

Rainfall forecasting is very challenging task for the meteorologists. Over the last few decades, several models have been utilized, attempting the successful analysing and forecasting of rainfall. Recorded climate data can play an important role in this regard. Long-time duration of recorded data can be able to provide better advancement of rainfall forecasting. This paper presents the utilization of statistical techniques, particularly linear regression method for modelling the rainfall prediction over Bangladesh. The rainfall data for a period of 11 years was obtained from Bangladesh Meteorological department (BMD), Dhaka i.e. that was surface-based rain gauge rainfall which was acquired from 08 weather stations over Bangladesh for the years of 2001-2011. The monthly and yearly rainfall was determined. In order to assess the accuracy of it some statistical parameters such as average, meridian, correlation coefficients and standard deviation were determined for all stations. The mo...

Prediction Rainfall with Regression Analysis

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023

Weather forecasting is one of the many widely used applications of artificial intelligence. Forecasting precipitation is one of the most popular research topics because it results in a great deal of property damage and numerous fatalities. Large-scale flooding can have an impact on a variety of social and practical spheres, including agriculture and disaster preparedness. Even with the most advanced mathematical techniques, older, widely used precipitation prediction models were unable to achieve higher classification rates. This article introduces a cutting-edge new technique for forecasting monthly precipitation that makes use of linear regression analysis. Using quantitative data about the state of the atmosphere, forecast when it will rain. Complex information can be recognized by some machine learning systems. a mapping that joins inputs and outputs with a small number of samples. Because of how quickly the atmosphere may change, it is challenging to anticipate precipitation with absolute confidence. The variation in conditions from the previous year should be used to forecast the likelihood of precipitation. For several factors like temperature, humidity, and wind, I advise utilizing linear regression. Given that the suggested model frequently estimates precipitation based on historical data for a specific geographic area, this forecast should be more accurate. Comparing the model's performance to wellknown methods for precipitation prediction, it performs more accurately.

Linear Regression Analysis Using Log Transformation Model for Rainfall Data in Water Resources Management Krueng Pase, Aceh, Indonesia

International Journal of Design & Nature and Ecodynamics, 2022

Climate changes are one crucial factor that influenced water availability at one location since they affected the environmental, social, and agricultural systems. The study observed the agent factors that influenced the rainfall changes at Krueng Pasee Aceh watershed, Indonesia. The method used in this research is a linear regression with a log transformation approach on predictor variables. The data used in this study consisted of rainfall, a total of rainy days, temperature, humidity, duration of irradiation, and wind speed in the period ranging from 1992 to 2020. Results showed that the agent factors had not distributed normally. The regression model produced after log transformation had met the classical assumptions and can be used to predict the rainfall at R-quare 24.61% with an RMSE value of 57.676. From all factors studied, the wind speed should be excluded. Further study is recommended to use the nonlinear method to improve the model for rainfall prediction.

A MULTI-REGRESSION MODEL BASED ON MONTHLY RAINFALL PROGNOSTICATION: CASE STUDY OF KASESE DISTRICT, IN EAST AFRICA

bilmes En 2021 ISBN: 978-605-74786-5-8, 2021

Forecasting of rainfall extremes is still an eminent challenge, especially in developing regions where the difficulty in prediction of these rainfall extremes is partly due to lack of advanced scientific tools and reliable data sets. The economy of Uganda being heavily dependent on agriculture becomes vulnerable due to lack of adequate irrigation facilities. In this paper a statistical approach is used over the historical data to predict the rainfall and establish its relationship to various atmospheric variables. The multiple linear regression (MLR) methodology is applied on the data collected over 9 years of Kasese district, Uganda. The model forecasts precipitation for a year considering monthly precipitation data and building model was used for years 2010-2013. The model testing and validation were performed using years 2014-2018 dataset of monthly precipitation. The equation developed from the model thereby displayed a superb result. The model predictions showed an excellent association with the actual data. The coefficient of determination (R 2) and adjusted R 2 value was obtained to be 0.804 and 0.721 respectively. This understanding validates the application of the developed model over the study area to prognosis rainfall, thereby helping in proper planning and management.

Predictions of Future Aspects of the Rainy Season Using Simple and Multiple Linear Regression Analysis- A Case Study of Chingóme Mission Daily Rainfall Data in Zambia

Journal of Psychology of Science and Technology

Abstract This article demonstrates the point and interval predictions of the dependent variables Y using both simple and multiple linear regression analyses for the given independent X variables. The primary methodology was analysis of quantitative data collected at Chin’gombe mission, northern part of Zambia, weather station for a period of 25 years. The article begins by justifying why a particular approach was used for analysis by testing the available data for randomness. Since, time trends where not evident, a classical approach was adopted which involved the construction of models that reflect the available data as closely as possible. A distribution with two parameters was preferable for greater flexibility, hence, the truncated exponential distribution with two unknown parameters was investigated instead of other distributions such as; lognormal, gamma, or weibull. But the predictions obtained were not particularly informative for agricultural planning, water management, and...

Development of an Equation to Estimate the Monthly Rainfall: A Case Study for Catarman, Northern Samar, Philippines

International Journal of Trend in Scientific Research and Development, 2020

This study aimed to derived an equation to estimate the monthly rainfall for Catarman, Northern Samar.The observed monthly rainfall data for Catarman N. Samar, Catbalogan Samar, Legazpi City and Masbate were obtained from the Philippine Atmospheric Geographical Astronomical Services Administration (PAGASA). The monthly rainfall records of the three (3) neighboring stations (Catbalogan, Legazpi, Masbate) were used to identify which of the existing rainfall prediction methods, namely, Normal Ratio Method, Distance Power Method and Multi Linear Regression Method is the basis in the development of a new equation. The accuracy by which the existing methods predict the observed monthly rainfall in Catarman was evaluated using T-test for correlated samples and the Pearson’s Correlation Coefficient. Since none of the methods produced estimates nearest to the observed monthly rainfall in Catarman, an equation has been derived: