Rainfall Prediction in South-Eastern Part of Bangladesh by Linear Regression Method (original) (raw)
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Rainfall has been extensively considered as one of the initial point towards the apprehension of climate change courses. Bangladesh is recently experiencing climate change impact related to hazards like cyclone, rainfall, flood, draught etc. Climate variable like rainfall is the most important parameter which is linked with agricultural aspects too for this country. Most of the rain occurred during monsoon period in Bangladesh. This study investigates temporal variability of rainfall and liner trend models on the NorthWest part of Bangladesh over the period of 1975-2014 using data from the Bangladesh Meteorological Department. We computed and analyzed the linear trend models by using least square estimation. We estimated mean with standard deviation, cross-correlation and linear trends of annual and monsoon rainfall using MS Excel and SPSS v21. The variability of rainfall between the stations was measured by correlation test. The annual and monsoon rainfall has been found in decreasing trends in recent times. In some areas in the NorthWestern part of the country, the amount of annual and monsoon mean rainfall may be decreased abruptly comparing with average normal rainfall all over the country. The linear trend analysis of rainfall reveals a bit different trend for the last four decades. The observed data and linear trend line shows the decreasing trend of annual rainfall rate is 0.102 mm per year, whereas the decreasing trend of monsoon season rainfall rate is 0.080 mm per year. The time series statistical analysis of this study also provided the information about the correlation coefficients of rainfall among the selected five stations of the SouthWest region. The result of this study would hopefully help the planners and program managers to take necessary actions and to measure disaster management, agricultural production, drought mitigation, flood control etc.
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Regression analysis is a statistical technique for investigating the relationship between variables. In this paper, rainfall and water level prediction models are discussed with the use of empirical statistical technique, Simple Linear Regression and analyzed the development of the predictive power of Linear Regression model to forecast the predicting rainfall and water level over Kalay in Sagaing Region for 10 years 2008 2017 .The data of the monthly rainfall and water level used in this study were obtained from Meteorology and Hydrology Department of Kalay, Myanmar. In July 2015, Kalay was affected by the floods. So the rainfall and water level are predicted for next five years in this paper. Ohnmar Myint "Linear Regressions of Predicting Rainfall over Kalay Region" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26711.pdf
Time Series, Factors and Impacts Analysis of Rainfall in North-Eastern Part in Bangladesh
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ANALYSIS OF PERSISTENCE IN YEARLY RAINFALL OVER BANGLADESH
Rainfall is the input of hydrological system. Many engineering issues i.e. flood protection, size of spillways, flood gates in dam etc. are affected by possible maximum flow in stream, due to the maximum rainfall intensity and distribution. Rainfall provides available water for agriculture and human consumption. Bangladesh is a land of abundant rainfall. Maximum rainfall occurs in the monsoon period from the month of June to September. Long term rainfall data have been compiled for 34 meteorological stations in our country. Certain simple entities as like " Dependable Rainfall " have baffled planner's right up to present. In simplest sense, it will ensure that the irrigation schedule is planned to make optimum use of rainfall to avoid over irrigation, water logging and salinity, which destroyed agricultural lands in the past decades through rising water tables. Because irrigation planning using mean values for rainfall is very unreliable. The planner needs to know the amount of rainfall, which can be dependable upon with a certain degree of probability. The study has demonstrated that estimation of monthly or yearly dependable rainfall is not an easy task, especially where rainfall data series are not available. The result showed that no single equation or procedure is quite adequate to describe the different climatic conditions of the world. These procedures were developed for estimating monthly and yearly dependable rainfall for yearly/monthly rainfall data series.
MACHINE LEARNING TECHNIQUES FOR THE INVESTIGATION OF RAINFALL IN BANGLADESH
Hello-Teen Society, 2022
Rainfall significantly affects human lives in various contexts, including natural calamities like landslides, droughts, and floods. In these industries, artificial intelligence technologies have a strong probability of success. The development of a machine learning-based rain prediction model is the aim of this study. This model is based on daily and monthly rainfall data (millimeters) over the period of 1970 to 2020 from the 32 weather stations of Bangladesh obtained from the Bangladesh Meteorological Department (BMD). Significant rainfall since the projection is so directly related to the economy and human lifespan, it could be a significant setback for the earth science division. For nations like Bangladesh, where agriculture is the mainstay of the economy, the accuracy of the rainfall statement is very important. Python programming language is used to investigate and predict precipitation using machine learning techniques and to compare different machine learning models. There was a total of six models used during the experiment. Each model was trained using five input characteristics, and then the precipitation projections were validated. All of the model's performance results were positive. However, Random Forest successfully predicts the dataset, making it the best option for this investigation. Higher accuracy and low Mean Absolute Error were produced using the Random Forest classifier. The amount of rainfall is observed to rise throughout the months of July, August, and September. The year 2017 saw an overall rainfall maximum of 99790.0 mm while the year 1972 saw an overall rainfall minimum of 32020.0 mm. And 71827.59mm is the average rainfall over all 32 weather stations. The month of July saw the highest monthly average rainfall of 539.10mm, and the month of January saw the lowest monthly average rainfall of 7.45mm. Additionally, 202.96mm of rain falls on average per month. The seasonal amount of rainfall in the 32 meteorological stations that are represented reveals that Cox's Bazar, Sylhet, and Teknaf have good volumes of rainfall.