Department of Statistics

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Author: search.filters.author.Dr. Mian Muhammad FarooqSubject: search.filters.subject.Statistics, computer and systems science

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    Drought Analysis using Transmuted Weibull Distribution
    (Library Information Services, COMSATS University Islamabad, Lahore Campus., 2020-04-01) Ali Hassan; CIIT/FA19-BST-005/LHR; Dr. Mian Muhammad Farooq; LHR TP 9940
    The study aims to predict the drought analysis using monthly rainfall and temperature data. Over the past years drought is becoming important factor that is affected by climate changes in Pakistan. In this study we analyze the impact of drought in Punjab Districts. For this purpose, firstly data collected by Pakistan Metrological Department of Punjab Districts for the years 1993 to 2022 and then further competitive strategy analysis that fits our analysis or predictions are sought and critically reviewed. Then the chosen parts from strategy analysis are applied for the graphical presentation. Firstly, Applying Standardized Precipitation Index based on precipitation data by fitting transmuted Weibull distribution from the year 1993 to 2022 of each District (Bahawalnagar, Bahawalpur, Jehlum, Jhang, Lahore, Sialkot, Sargodha, Faisalabad, and Multan) to get know about classification of drought against each observed precipitation values and understand the impact of precipitation on drought. Transmuted Weibull Distribution is fitted on precipitation data to normalize it as SPI is calculated after fitting distribution on it. Further to identify trends of drought SPI calculated at different time scales by moving average of Three months, six months, and Twelve months to find SPI values and then these values are classified according to their drought conditions as moderate, severe, extreme near to normal, slightly wet, and extreme wet. Secondly applying Standardized Anomaly Index is another index that is widely used for drought analysis based on temperature data. SAI is calculated against each District (Bahawalnagar, Bahawalpur, Jehlum, Jhang, Lahore, Sialkot, Sargodha, Faisalabad, and Multan) from 1993 to 2023. By SAI values we can analyze the impact of temperature on drought. SAI values are shown in graphical form below in Chapter Results, that shows the trend and the SAI values against each year of a particular District.
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    Spatial and Temporal Analysis of Drought
    (Library Information Services, COMSATS University Islamabad, Lahore Campus., 2020-04-01) Muhammad Furqan Ahmad; CIIT/FA19-BST-002/LHR; Dr. Mian Muhammad Farooq; LHR TP 9938
    In the recent years climate change is the hot topic of research. Climate change can have severe effect on the environment. The drought is an important factor of the climate. If a drought occur in some area it will affect the agriculture, water resources and other important factors of the country. In this study, the impact of drought in different districts of Punjab, Pakistan was studied using Standardized Precipitation Index (SPI) and Standardized Anomaly Index (SAI). The SPI is applied on the rain data and the SAI was applied on the temperature data. The data was collected from Pakistan Metrological Department. At first, we fit the data on Length Biased Exponential Distribution and then we applied Standardized Precipitation Index on the rain data received from Pakistan Meteorological Department from 1993 to 2022. We also computed SPI using 3-month, 6-month, and 12-month moving average of the original data. We conducted spatial and temporal analysis using SPI and for the visual representation we also plot maps of the past five years for better understanding of the drought. Standardized Anomaly Index (SAI) is a useful tool to measure and monitor deviations from normal conditions in terms of temperature or other relevant variables associated with drought. We used temperature to better explain the drought impact in Pakistan. After applying SAI, we find out that temperate of the data is above average which means there is drought impact in different districts of Pakistan. The results show the clear drought pattern in different districts of Pakistan. We ignore the 0 and negative values of rain data before fitting the Length Biased Exponential Distribution as it is undefined for these values and then we compute the results. The graphs show that after taking moving average the drought patterns decrease in districts and also few years does not show any drought pattern.