Yazar "Zulfiqar Ali" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • [ X ]
    Öğe
    A novel regional forecastable multiscalar standardized drought index (RFMSDI) for regional drought monitoring and assessment
    (Elsevier BV, 2025-03) Aamina Batool; Veysi Kartal; Zulfiqar Ali; Miklas Scholz; Farman Ali
    Drought is a complex recurrent natural phenomenon. It is the main outcome of climate change. It has long-term impacts on agriculture, human life as well as the environment. Therefore, quantifying drought at the regional level is essential for developing sustainable policies. This study introduced a new drought index for regional drought forecasting called the Regional Forecastable Multiscalar Standardized Drought Index (RFMSDI). The RFMSDI methodology is based on Forecastable Component Analysis (FCA) and K-Component Gaussian Mixture Distribution (K-CGMD). FCA reduce dimension by focus on components that are inherently more predictable. It ensures that reduced data has a built-in ability to predict future trends by selecting the maximized forecastable components. K-CGMD is utilized to model the multimodel time series data. The study application incorporates eight meteorological stations in Türkiye's Elazig province (Baskil, Agin, Elazig, Karakocan, Keban Maden, Palu and Sivrice). The effectiveness of RFMSDI is evaluated by analyzing precipitation data over these meteorological stations of Türkiye. The comparative assessment of the research signifies the superiority of FCA for regional data aggregation. In this research, the comparative assessment of RFMSDI is evaluated against the Standardized Precipitation Index (SPI) by analyzing Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics across different time scales using various machine learning and traditional time series models. The research findings include the following: 1) K-CGMD is a better fitting approach for standardizing RFMSDI and SPI based on reduced BIC values. 2) RFMSDI has superior performance over SPI based on the lower values of RMSE and MAE. 3) Both machine learning and classical methods reveal that RFMSDI outperforms SPI in predicting droughts. 4) SPI shows localized advantages with the ELM training set at 1- and 6-month time scales but RFMSDI offers a more comprehensive and consistent tool for drought prediction, especially when tested on unseen data. In general, the findings endorse the effectiveness of RFMSDI for monitoring drought on a regional level.
  • [ X ]
    Öğe
    Development of Trivariate Multiscalar–Standardized Drought Index (TMSDI) for assessing drought characteristics
    (Springer Science and Business Media LLC, 2025-02-11) Aamina Batool; Veysi KARTAL; Zulfiqar Ali
    Drought is an extensive natural hazard influenced by human activities. Drought has a substantial impact on environmental systems and socioeconomic activities globally, posing serious challenges to water resources, agriculture, and ecosystems. Drought as a complicated natural occurrence is difficult to monitor and anticipate. However, to address the detrimental issues of drought, this study examined the innovative Trivariate Multiscalar-Standardized Drought Index (TMSDI). The climatic factors of precipitation, temperature, and Normalized Difference Vegetation Index (NDVI) are components in the development of TMSDI. To check the association of the innovative index with the another drought indices, this study evaluated correlations between the proposed index (TMSDI) and traditional drought indices, i.e., the Standardized Precipitation Index (SPI) and the Standardized Precipitation Temperature Index (SPTI) at 1-, 3-, 6-, 9-, 12-, 24-, and 48-month time scales. The outcomes demonstrate that there is a consistent relationship between the TMSDI and SPI due to higher values of correlation. The lower correlation between TMSDI and SPTI shows that there is a substantial and consistent relationship between TMSDI and SPI than TMSDI and SPTI. Moreover, the long-term behavior of different drought conditions indicates that extreme drought is more likely than extreme wet across the Markov chain's Steady States Probabilities (SSPs). Consequently, the proposed index (TMSDI) is recommended as an effective tool to precisely and accurately monitor drought conditions over different time scales within different climate factors.
  • [ X ]
    Öğe
    Evaluating variogram models and kriging approaches for analyzing spatial trends in precipitation simulations from global climate models
    (Springer Science and Business Media LLC, 2025-02-07) Aamina Batool; Sufian Ahmad; Ayesha Waseem; Veysi Kartal; Zulfiqar Ali; Muhammad Mohsin
    Climate change has heightened the irregularity and unpredictability of weather patterns, influencing precipitation patterns. Accurate geographical projections of precipitation and other climatic variables are critical to sustainable water resource management and disaster preparedness. Variogram models are geostatistical techniques used to examine spatial correlation. Therefore, selecting the optimum variogram model for spatial interpolation is challenging. This study used six variogram models to assess spatial trends. Leave-one-out cross-validation (LOOCV) and K-fold cross-validation approaches are used to find the best variogram model based on metrics such as mean absolute error (MAE), root mean square error (RMSE), and mean bias. In this study, correlation data of 22 GCMs within observed data are predicted over 94 locations in Pakistan from 1950 to 2014. For evaluation, ordinary kriging (OK) and universal kriging (UK) are utilized as geostatistical approaches. The study highlights the suitability of the variogram models. Pentaspherical variogram (Pen) model is suggested as suitable model due to its minimum error metrics as well as the Hol effect (Hol) model has been considered beneficial for dealing with complicated data. From the geostatistical approaches, ordinary kriging (OK) yields the best prediction. Moreover, ordinary kriging (OK) and universal kriging (UK) both yield similar outcomes across some correlation-based data of 22 GCMs within observed data. Consequently, the implication of correlation analysis, optimum variogram models, and interpolation techniques enables the precise and accurate approach in the prediction of GCM performance. The efficiency of variogram models and interpolation approaches in managing data variability helps to enhance the consistency and interpretability of climate data.