A STUDY OF RAINFALL PATTERN KOLHAPUR DISTRICT USING STATISTICAL TECHNIQUES (original) (raw)
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Acta scientific agriculture, 2021
The Haridwar belong to Western Himalayan Region with geographical coordinates are 29.948 deg latitude, 78.160 deg longitude. The trend analysis of rainfall pattern in Haridwar is studied through time series analysis of rainfall for a long period of 20 years. The Kharif crops production is dependent on this rainfall. The average annual rainfall of region during 1999-2018 is 1051mm. and south west monsoon rainfall 925.9 mm observed.. The annual rainfall in year 2007 showing the highest positive rainfall anomaly (2.31) while the other years show rainfall below normal with 2009 Showing the lowest negative rainfall deviation (-2.03). and the South west (1999 to 2018) rainfall of Haridwar District in year 2018 showing the highest positive rainfall anomaly (2.15) while the other years show rainfall below normal with 2009 Showing the lowest negative rainfall deviation (-2.05). The R 2 value-1.408 means that only-140.8 percent variations is observed in twenty years. The maximum South west rainfall recoded 1564 mm in year 2008 and lowest rainfall recoded 439 mm in year 2001. The annual rainfall highest SIAP value 2.31 is observed in year 2007 whereas highest negative value-2.03 is observed in year 2009. The Southwest rainfall highest SIAP value 2.15 observed in year 2018 whereas lowest SIAP value-2.05 observed in year 2009. On the basis, the future forecast of rainfall for a period of ten years from 2019 to 2030 has been observed a negative trend for the coming years. In future, expected annual rainfall may be more in year 2030 observed 1024.9 mm in the district. In future, expected annual rainfall may be less in year 2030 observed 1025 mm in the District. The south west rainfall in the year 2030; expected rainfall may be 922.8 mm. The trends are showing increasing trend pattern from year 2019 to 2030. The trend analysis gives the scenario of current to expected future situation. Water is a vital component for agricultural crops and in abnormal period crops are irrigated by available source viz. tube well, submersible, canal, irrigation channels and other sources. The statistical analysis of annual and south west rainfall of the study area will help to better water management. Today rainfall is not regular fashion so farmers are not more dependent much more on rainfall. The source of irrigation, mechanization and knowledge of current situation of weather and climate change related pattern and adaptation of technology is maintain to crops yield trend.
Geosfera Indonesia, 2022
Although the variability and prediction of rainfall is an essential issue of the Santhal Pargana Division of the Jharkhand State but the issue is still far from its' conclusive statement till date. Therefore, this study aimed to simulate the monthly rainfall from 1901 to 2020 using an eight-step procedure. After downloading the monthly rainfall for the Santhal Pargana Division from 1901 to 2020, the TBATS and Naive models were used to simulate the rainfall. The accuracy assessment of each model was done by using the MASE, MAE, RMSE, ME, and R. For the Naïve model, the Godda station was noticed with a comparatively high combined error. The lowest combined error was found for the Pakur station in case of Naïve models. Similar result was also obtained for the TBATS model. The TBATS was found with comparatively higher accuracy, as the combined error was less for the TBATS. The spatial assessment for the standardized rainfall varied from 84.419 mm. to 149.225 mm. For the Naïve predicted model, the rainfall was marked in between 8.133 mm. to 67.059 mm. For the TBATS fitted model, the rainfall fluctuated from the 37.127 mm. to 62.993 mm. Dumka station was noticed with comparatively low rainfall (i.e.,37.127 mm.). Deoghar and Jamtara stations were marked with a moderate rainfall. Remaining stations were marked with higher amount of rainfall for the TBATS fitted model. The Wilcoxon test proved that each model was significant at 95% confidence interval. The result produced in this research is fruitful enough to be utilized for agricultural planning in the Santhal Pargana Division of the Jharkhand state, India.
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...
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Probability and Trend Analysis of Monthly Rainfall in Haryana
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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
Different statistical models based on weather parameters in Navsari district of Gujarat
Mausam, 2023
Agriculture plays very important role in development of country. Rice is a staple food for more than half of world's population. Timely and reliable forecasting provides vital and appropriate input, foresight and informed planning. The present investigation was carried out to forecast Kharif rice yield using two different statistical techniques, viz., discriminant function analysis and logistic regression analysis. The statistical models were developed using data from 1990 to 2012 and validation of developed models was done by using remaining data, i.e., 2013 to 2016. It was observed that value of adjusted R 2 varied from 73.00 per cent to 93.30 per cent in different models. The best forecast model was selected based on high value of adjusted R 2 , Forecast error and RMSE. Based on obtained results in Navsari district, the discriminant function analysis technique (Model-5) was found better than logistic regression analysis (Model-12) for pre-harvest forecasting of rice crop yield. The results revealed that Model-5 showed comparatively low forecast error (%) along with highest value of Adj. R 2 (93.30) and lowest value of RMSE (120.07). Also Model-5 is able to generate yield forecast a week earlier (39 th SMW) than Model-12 (40 th SMW).
2021
1Assistant Professor, Dept. of Civil Engineering, PESCE, Mandya, Karnataka, India 2Former Associate Professor, Dept. of Civil Engineering, PESCE, Mandya, Karnataka, India 3U.G. Student, Dept. of Civil Engineering, PESCE, Mandya, Karnataka, India ---------------------------------------------------------------------***---------------------------------------------------------------------Abstract Climatic variability particularly rainfall and temperature has received a greater attention throughout the world. Climate change and its variability in the climatic parameters may adversely affect the agriculture sector and water resources of agrarian country like India. The research on change of rainfall, occurrence and its allocation are the most significant way for sustainable water resource management and food security. Hence an understanding of rainfall patterns in the changing environment will help in better decision making and improving the adopting of the communities to sustain the extr...
Probability Analysis for prediction of rainfall of Raipur region (Chhattisgarh)
The Allahabad Farmer
Consecutive days of annual maximum rainfall data corresponding to different return periods are required for economic planning and design of hydraulic structures like small dams, bridges, culverts, drainage works etc. Different probability distribution models namely, Normal, Log Normal, Log Pearson type III and Gumbel were tested for Raipur Region by comparing the Chi-square values. The Gumbel distribution was found to be fit best for one day annual maximum rainfall. Log Pearson type III distribution was found to be best fitted for two, three and four consecutive days of annual maximum rainfall. Normal distribution was found to be best fitted for five consecutive days annual maximum rainfall. The one day annual maximum rainfall and two to five consecutive days annual maximum rainfall exhibited strong Linear relationships (R2 = 0.9191 to 0.9494). The regression equations developed in the present studies can be successfully used for prediction of rainfall of consecutive days ranging from two to five annual maximum rainfalls with one day annual maximum rainfall for Raipur region.