Browsing by Author "Al Dughairi, Ahmed Abdullah"

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    Air quality analysis and PM2.5 modelling usingmachine learning techniques: A study of Hyderabad city in India
    (Cogent OA, 2023-08-13) Mathew, Aneesh; Gokul, P R; Shekar, Padala Raja; Arunab, K S; Abdo, Hazem Ghassan; Almohamad, Hussein; Al Dughairi, Ahmed Abdullah
    The rapid urbanization and industrialization in many parts of the world have made air pollution a global public health problem. A study conducted by the Swiss organization IQAir indicated that 22 of the top 30 most polluted cities in the world are in India. This creates the problem of air pollution, which is very relevant to India as well. Exposure to air pollutants has both acute (short-term) and chronic (long-term) impacts on health. Among the major air pollutants, particulate matter 2.5 (PM2.5) is the most harmful, and its long-term exposure can impair lung functions. Pollutant concentrations vary temporally and are dependent on the local meteorology and emissions at a given geographic location. PM2.5 forecasting models have the potential to develop strategies for evaluating and alerting the public regarding expected hazardous levels of air pollution. Accurate measurement and forecasting of pollutant concentrations are critical for assessing air quality and making informed strategic decisions. Recently, data-driven machine learning algorithms for PM2.5 forecasting have received a lot of attention. In this work, a spatio-temporal analysis of air quality was first performed for Hyderabad, indicating that average PM2.5 concentrations during the winter were 68% higher than those during the summer. Following that, PM2.5 modelling was done using three different techniques: multilinear regression, K-nearest neighbours (KNN), and histogram-based gradient boost (HGBoost). Among these, the HGBoost regression model, which used both pollution and meteorological data as inputs, outperformed the other two techniques. During testing, the model acquired an amazing R2 value of 0.859, suggesting a significant connection with the actual data. Additionally, the model exhibited a minimum Mean Absolute Error (MAE) of 5.717 μg/m3 and a Root Mean Square Error (RMSE) of 7.647 μg/m3, further confirming its accuracy in predicting PM2.5 concentrations. In our investigation, we discovered that the HGBoost3 model beat other PM2.5 modelling models by having the lowest error and the highest R2 value. This study made a substantial addition by incorporating the spatiotemporal relationship between air pollutants and meteorological variables in predicting air quality. This method has the potential to improve the creation of more precise air pollution forecast models.
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    Effects of Climate Change on Streamflow in the Godavari Basin Simulated Using a Conceptual Model including CMIP6 Dataset
    (MDPI, 2023-04-27) Reddy, Nagireddy Masthan; Saravanan, Subbarayan; Almohamad, Hussein; Al Dughairi, Ahmed Abdullah; Abdo, Hazem Ghassan
    Hydrological reaction to climate change anticipates water cycle alterations. To ensure long-term water availability and accessibility, it is essential to develop sustainable water management strategies and better hydrological models that can simulate peak flow. These efforts will aid in water resource planning, management, and climate change mitigation. This study develops and compares Sacramento, Australian Water Balance Model (AWBM), TANK, and SIMHYD conceptual models to simulate daily streamflow at Rajegaon station of the Pranhita subbasin in the Godavari basin of India. The study uses daily Indian Meteorological Department (IMD) gridded rainfall and temperature datasets. For 1987–2019, 70% of the models were calibrated and 30% validated. Pearson correlation (CC), Nash Sutcliffe efficiency (NSE), Root mean square error (RMSE), and coefficient of determination (CD) between the observed and simulated streamflow to evaluate model efficacy. The best conceptual (Sacramento) model selected to forecast future streamflow for the SSP126, SSP245, SSP370, and SSP585 scenarios for the near (2021–2040), middle (2041–2070), and far future (2071–2100) using EC-Earth3 data was resampled and bias-corrected using distribution mapping. In the far future, the SSP585 scenario had the most significant relative rainfall change (55.02%) and absolute rise in the annual mean temperature (3.29 °C). In the middle and far future, the 95th percentile of monthly streamflow in the wettest July is anticipated to rise 40.09% to 127.06% and 73.90% to 215.13%. SSP370 and SSP585 scenarios predicted the largest streamflow increases in all three time periods. In the near, middle, and far future, the SSP585 scenario projects yearly relative streamflow changes of 72.49%, 93.80%, and 150.76%. Overall, the findings emphasize the importance of considering the potential impacts of future scenarios on water resources to develop effective and sustainable water management practices.
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    Machine Learning Approaches for Streamflow Modeling in the Godavari Basin with CMIP6 Dataset
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023-08-11) Saravanan, Subbarayan; Reddy, Nagireddy Masthan; Pham, Quoc Bao; Alodah, Abdullah; Abdo, Hazem Ghassan; Almohamad, Hussein; Al Dughairi, Ahmed Abdullah
    Accurate streamflow modeling is crucial for effective water resource management. This study used five machine learning models (support vector regressor (SVR), random forest (RF), M5-pruned model (M5P), multilayer perceptron (MLP), and linear regression (LR)) to simulate one-day-ahead streamflow in the Pranhita subbasin (Godavari basin), India, from 1993 to 2014. Input parameters were selected using correlation and pairwise correlation attribution evaluation methods, incorporating a two-day lag of streamflow, maximum and minimum temperatures, and various precipitation datasets (including Indian Meteorological Department (IMD), EC-Earth3, EC-Earth3-Veg, MIROC6, MRI-ESM2-0, and GFDL-ESM4). Bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets were utilized in the modeling process. Model performance was evaluated using Pearson correlation (R), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and coefficient of determination (R2). IMD outperformed all CMIP6 datasets in streamflow modeling, while RF demonstrated the best performance among the developed models for both CMIP6 and IMD datasets. During the training phase, RF exhibited NSE, R, R2, and RMSE values of 0.95, 0.979, 0.937, and 30.805 m3/s, respectively, using IMD gridded precipitation as input. In the testing phase, the corresponding values were 0.681, 0.91, 0.828, and 41.237 m3/s. The results highlight the significance of advanced machine learning models in streamflow modeling applications, providing valuable insights for water resource management and decision making.
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    Multi-Criterion Analysis of Cyclone Risk along the Coast of Tamil Nadu, India—A Geospatial Approach
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023-08-16) Saravanan, Subbarayan; Abijith, Devanantham; Kulithalai Shiyam Sundar, Parthasarathy; Reddy, Nagireddy Masthan; Almohamad, Hussein; Al Dughairi, Ahmed Abdullah; Al-Mutiry, Motrih; Abdo, Hazem Ghassan
    A tropical cyclone is a significant natural phenomenon that results in substantial socio-economic and environmental damage. These catastrophes impact millions of people every year, with those who live close to coastal areas being particularly affected. With a few coastal cities with large population densities, Tamil Nadu’s coast is the third-most cyclone-prone state in India. This study involves the generation of a cyclone risk map by utilizing four distinct components: hazards, exposure, vulnerability, and mitigation. The study employed a Geographical Information System (GIS) and an Analytical Hierarchical Process (AHP) technique to compute an integrated risk index considering 16 spatial variables. The study was validated by the devastating cyclone GAJA in 2018. The resulting risk assessment shows the cyclone risk is higher in zones 1 and 2 in the study area and emphasizes the variations in mitigation impact on cyclone risk in zones 4 and 5. The risk maps demonstrate that low-lying areas near the coast, comprising about 3%, are perceived as having the adaptive capacity for disaster mitigation and are at heightened risk from cyclones regarding population and assets. The present study can offer valuable guidance for enhancing natural hazard preparedness and mitigation measures in the coastal region of Tamil Nadu.
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    Thermal dynamics of Jaipur: Analyzing urban heat island effects using in-situ and remotely sensed data
    (Cogent OA, 2023-11-02) Mathew, Aneesh; Sarwesh, P; Khandelwal, Sumit; Shekar, Padala Raja; Alao, Joseph Omeiza; Abdo, Hazem Ghassan; Almohamad, Hussein; Al Dughairi, Ahmed Abdullah
    The Urban Heat Island (UHI) effect is a phenomenon where urban areas experience higher temperatures than surrounding rural areas. In these issues, enhanced air or surface temperature is one of the major issues that led to the UHI phenomenon. In this article, we come up with a study on the diurnal UHI effect caused in Jaipur city, India, and surrounding areas of Jaipur. In-situ temperature monitoring has been carried out at seven dispersed locations to properly understand and evaluate the effects of surface UHI (SUHI) and atmospheric UHI (AUHI), as well as to assess the thermal profile of diverse land surfaces in Jaipur. With the use of satellite data, the intensity of AUHI and SUHI has been determined between 10.30 a.m. and 10.30 p.m. The observations point out that positive AUHI intensity (AUHII) exists at many locations, irrespective of time periods. During the day period, negative SUHI intensity (SUHII) was noticed at many locations, whereas during the night period, positive SUHII was noticed. According to our observations, AUHI and SUHI have a direct correlation at night but a negative or inverse correlation during the day. That is, AUHI was active both during the day and at night. Various land surfaces play a significant role in contrasting the diurnal UHI effect. This study evaluates the potential of remotely sensed data in monitoring the UHI effect and provides recommendations for urban planners and policymakers to mitigate the UHI effect in the city of Jaipur.