Yazar "Mohsin, Muhammad" seçeneğine göre listele

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    Öğe
    Assessing the generalization of forecasting ability of machine learning and probabilistic models for complex climate characteristics
    (Springer, 2024) Batool, Aamina; Ali, Zulfiqar; Mohsin, Muhammad; Masmoudi, Atef; Kartal, Veysi; Satti, Samina
    Climate changes and global warming increase risk of recurrent extreme and complex climatic features. It necessitates accurate modeling and forecasting of climate phenomena for sustainable development goals. However, machine learning algorithms and advanced statistical models are extensively employed to analyze complex data and make predictions related to climate phenomena. It is important to have comprehensive knowledge to use these models and consider their potential implications. This study aims to evaluate and compare some popular machine learning and probabilistic methods by analyzing various time series indices associated with precipitation and temperature. For application, time series data of Standardized Precipitation Temperature Index (SPTI), Standardized Temperature Index (STI), Standardized Compound Drought and Heat Index (SCDHI), and Biased Diminished Weighted Regional Drought Index (BDWRDI) are used from various meteorological regions of Pakistan. The performance of each algorithm is compared using Residual Mean Square Error (RMSE) and Mean Average Error (MAE). The outcomes associated with this research indicate a higher preference of neural networks over machine learning methods in the training sets. However, the efficiency varies from model to model, indicator to indicator, time scale to time scale, and location to location during the testing phase. The most appropriate models are found by considering a list of candidates forecasting models and investigating the performance of each model.
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    Öğe
    DISCRETIZATION OF THE METHOD OF GENERATING AN EXPANDED FAMILY OF DISTRIBUTIONS BASED UPON TRUNCATED DISTRIBUTIONS
    (Vinca Inst Nuclear Sci, 2021) Farooq, Muhammad; Mohsin, Muhammad; Naeem, Muhammad; Farman, Muhammad; Akgul, Ali; Saleem, Muhammad Umar
    Discretization translates the continuous functions into discrete version making them more adaptable for numerical computation and application in applied mathematics and computer sciences. In this article, discrete analogues of a generalization method of generating a new family of distributions is provided. Several new discrete distributions are derived using the proposed methodology. A discrete Weibull-Geometric distribution is considered and various of its significant characteristics including moment, survival function, reliability function, quantile function, and order statistics are discussed. The method of maximum likelihood and the method of moments are used to estimate the model parameters. The performance o f the proposed model is probed through a real data set. A comparison of our model with some existing models is also given to demonstrate its efficiency.
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    Öğe
    Generalization method of generating the continuous nested distributions
    (Walter De Gruyter Gmbh, 2023) Farooq, Mian Muhammad; Mohsin, Muhammad; Farman, Muhammad; Akgul, Ali; Saleem, Muhammad Umer
    In many life time scenarios, life of one component or system nested in other components or systems. To model these complex structures some so called nested models are required rather than conventional models. This paper introduces the generalization of the method of generating continuous distribution proposed by N. Eugene, C. Lee, and F. Famoye, Beta-normal distribution and its applications, Commun. Stat. Theor. Methods, vol. 31, no. 4, pp. 497-512, 2002 and A. Alzaatreh, C. Lee, and F. Famoye, A new method for generating families of continuous distributions, Metron, vol. 71, no. 1, pp. 63-79, 2013 which nest one model in other to cope with complex systems. Some important characteristics of the proposed family of generalized distribution have been studied. The famous Beta, Kumaraswami and Gamma generated distributions are special cases of our suggested procedure. Some new distributions have also been developed by using the suggested methodology and their important properties have been discussed as well. A variety of real life data sets are used to demonstrate the efficacy of new suggested distributions and illation is made with baseline models.