Browsing by Author "Abdo, Hazem Ghassan"
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Item 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 AbdullahThe 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.Item Assessing the impact of climate and land use change on flood vulnerability: a machine learning approach in coastal region of Tamil Nadu, India(Springer Science and Business Media Deutschland GmbH, 2025-01-27) Abijith, Devanantham; Saravanan, Subbarayan; Parthasarathy K.S.S.; Reddy, Nagireddy Masthan; Niraimathi, Janardhanam; Bindajam, Ahmed Ali; Mallick, Javed; Alharbi, Maged Muteb; Abdo, Hazem GhassanFlooding and other natural disasters threaten human life and property worldwide. They can cause significant damage to infrastructure and disrupt economies. Tamil Nadu coast is severely prone to flooding due to land use and climate changes. This research applies geospatial tools and machine learning to improve flood susceptibility mapping across the Tamil Nadu coast in India, using projections of Land Use and Land Cover (LULC) changes under current and future climate change scenarios. To identify flooded areas, the study utilised Google Earth Engine (GEE), Sentinel-1 data, and 12 geospatial datasets from multiple sources. A random forest algorithm was used for LULC change and flood susceptibility mapping. The LULC data are classified for the years 2000, 2010, and 2020, and from the classified data, the LULC for years 2030, 2040, and 2050 are projected for the study. Four future climate scenarios (SSP 126, 245, 370, and 585) were used for the average annual precipitation from the Coupled Model Intercomparison Project 6 (CMIP6). The results showed that the random forest model performed better in classifying LULC and identifying flood-prone areas. From the results, it has been depicted that the risk of flooding will increase across all scenarios over the period of 2000–2100, with some decadal fluctuations. A significant outcome indicates that the percentage of the area transitioning to moderate and very high flood risk consistently rises across all future projections. This study presents a viable method for flood susceptibility mapping based on different climate change scenarios and yields estimates of flood risk, which can provide valuable insights for managing flood risks. © The Author(s) 2025.Item 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 GhassanHydrological 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.Item 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 AbdullahAccurate 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.Item 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 GhassanA 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.Item Prioritizing sub‑watersheds for soil erosion using geospatial techniques based on morphometric and hypsometric analysis: a case study of the Indian Wyra River basin(Springer Nature, 2023-06-26) Shekar, Padala Raja; Mathew, Aneesh; Abdo, Hazem Ghassan; Almohamad, Hussein; Abdullah Al Dughairi, Ahmed; Al-Mutiry, MotrihThe hydrological availability and scarcity of water can be affected by geomorphological processes occurring within a watershed. Hence, it is crucial to perform a quantitative evaluation of the watershed’s geometry to determine the impact of such processes on its hydrology. Geographic information systems (GIS) and remote sensing (RS) techniques have become increasingly significant because they enable decision-makers and strategists to make accurate and efficient decisions. To prioritize sub-watersheds within the Wyra watershed, this research employs two methods: morphometric analysis and hypsometric analysis. The watershed was divided into eleven sub-watersheds (SWs). The prioritization of sub-watersheds in the Wyra watershed involved assessing several morphometric parameters, such as relief, linear, and areal features, for each sub-watershed. Furthermore, the importance of the sub-watersheds was determined by computing hypsometric integral (HI) values using the elevation–relief ratio method. The final prioritization of sub-watersheds based on morphometric analysis was determined through the integration of principal component analysis (PCA) and weighted sum approach (WSA). SW2 and SW9 have had higher priorities using morphometric analysis, whereas SW6, SW7, and SW10 have obtained higher priorities using hypsometric analysis. SW4 is the most common SW that shares the same priority. The most vulnerable sub-watersheds are those with the highest priority, and therefore, programmes for soil and water conservation should pay more attention to them. The conclusions of the study may prove useful to various stakeholders involved in initiatives related to watershed development and management.Item Spatiotemporal dynamics of urban heat island effect and air pollution in Bengaluru and Hyderabad: implications for sustainable urban development(Springer Nature, 2025-02-25) Mathew, Aneesh; Aljohani, Taghreed Hamdi; Shekar, Padala Raja; Arunab, K. S.; Sharma, Atul Kumar; Ahmed, Mohamed Fatahalla Mohamed; Idris, Ummhani Idris Ahmed; Almohamad, Hussein; Abdo, Hazem GhassanUncontrolled growth in population is the cause of the unplanned, rapid, and unsustainable expansion of urban areas. This has led to a deterioration of environmental conditions for both global and local ecosystems. This research investigates the Urban Heat Island (UHI) phenomenon in Bengaluru and Hyderabad, India, including its spatial and temporal distribution and relation to air pollution. The investigation was conducted in both study locations during the summer and winter seasons, with data spanning from 2001 to 2021. The findings reveal that the maximum UHI intensity in both cities varies seasonally, with the highest values observed during the summer and the lowest during the winter. Annual maximum UHI intensities range from 4.65 °C to 6.69 °C in Bengaluru and from 5.74 °C to 6.82 °C in Hyderabad. The average UHI intensity also exhibits seasonal and annual variations, with the UHI effect being particularly pronounced in Bengaluru. In addition, the study provides the Urban Thermal Field Variance Index (UTFVI), which reveals that both cities consistently face intense UHI impacts throughout the year, greatly affecting the quality of life. Additionally, hotspot analysis reveals an increasing trend in UHI-affected areas over the years in both cities. The study also highlights air pollution concentrations and shows relationships between land surface temperature (LST) and air pollutants, emphasizing the need to alleviate urban heat, enhance air quality, and promote sustainability. This underscores the importance of UHI dynamics in urban environmental management and public health. This study enhances comprehension of UHI dynamics in swiftly urbanizing areas, providing a novel viewpoint on the complex interconnection between urbanization, climate, and air quality. These insights help develop sustainable urban strategies, reducing the negative effects of uncontrolled urbanization and benefiting local communities and the global ecosystem.Item 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 AbdullahThe 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.Item Unveiling urban air quality dynamics during COVID-19: a Sentinel-5P TROPOMI hotspot analysis(Nature portfolio, 2024-09-16) Mathew, Aneesh; Shekar, Padala Raja; Nair, Abhilash T; Mallick, Javed; Rathod, Chetan; Bindajam, Ahmed Ali; Alharbi, Maged Muteb; Abdo, Hazem GhassanIn India, the spatial coverage of air pollution data is not homogeneous due to the regionally restricted number of monitoring stations. In a such situation, utilising satellite data might greatly influence choices aimed at enhancing the environment. It is essential to estimate significant air contaminants, comprehend their health impacts, and anticipate air quality to safeguard public health from dangerous pollutants. The current study intends to investigate the spatial and temporal heterogeneity of important air pollutants, such as sulphur dioxide, nitrogen dioxide, carbon monoxide, and ozone, utilising Sentinel-5P TROPOMI satellite images. A comprehensive spatiotemporal analysis of air quality was conducted for the entire country with a special focus on five metro cities from 2019 to 2022, encompassing the pre-COVID-19, during-COVID-19, and current scenarios. Seasonal research revealed that air pollutant concentrations are highest in the winter, followed by the summer and monsoon, with the exception of ozone. Ozone had the greatest concentrations throughout the summer season. The analysis has revealed that NO2 hotspots are predominantly located in megacities, while SO2 hotspots are associated with industrial clusters. Delhi exhibits high levels of NO2 pollution, while Kolkata is highly affected by SO2 pollution compared to other major cities. Notably, there was an 11% increase in SO2 concentrations in Kolkata and a 20% increase in NO2 concentrations in Delhi from 2019 to 2022. The COVID-19 lockdown saw significant drops in NO2 concentrations in 2020; specifically, − 20% in Mumbai, − 18% in Delhi, − 14% in Kolkata, − 12% in Chennai, and − 15% in Hyderabad. This study provides valuable insights into the seasonal, monthly, and yearly behaviour of pollutants and offers a novel approach for hotspot analysis, aiding in the identification of major air pollution sources. The results offer valuable insights for developing effective strategies to tackle air pollution, safeguard public health, and improve the overall environmental quality in India. The study underscores the importance of satellite data analysis and presents a comprehensive assessment of the impact of the shutdown on air quality, laying the groundwork for evidence-based decision-making and long-term pollution mitigation efforts.