Department of Civil Engineering

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Browsing Department of Civil Engineering by Author "Almohamad, Hussein"

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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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    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, Motrih
    The 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.
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    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 Ghassan
    Uncontrolled 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.
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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.