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Dr. Gharbia is an Assistant Lecturer of Water and Environmental Engineering at IT Sligo. Integrated environmental dynamic systems modelling using GIS & Machine Learning Techniques is his main field of interest. Dr. Gharbia is a civil engineer with a master degree in water & environmental sciences, in addition to a postgraduate diploma in statistics. His PhD developed an integrated modelling platform (GEO-CWB) to study water vulnerability in large catchments.
Parametrising the spatially distributed dynamic catchment water balance is a critical factor in studying the hydrological system responses to climate and land use changes. This study presents the development of a geographic information system (GIS)-based set of algorithms (geographical spatially distributed water balance model (GEO-CWB)), which is developed from integrating physical, statistical, and machine learning models. The GEO-CWB tool has been developed to simulate and predict future spatially distributed dynamic water balance using GIS environment at the catchment scale in response to the future changes in climate variables and land use through a user-friendly interface. The tool helps in bridging the gap in quantifying the high-resolution dynamic water balance components for the large catchments by reducing the computational costs. Also, this paper presents the application and validation of GEO-CWB on the Shannon catchment in Ireland as an example of a large and complicated hydrological system. It can be concluded that climate and land use changes have significant effects on the spatial and temporal patterns of the different water balance components of the catchment.
Salem Gharbia; Laurence Gill; Paul Johnston; Francesco Pilla. GEO-CWB: GIS-Based Algorithms for Parametrising the Responses of Catchment Dynamic Water Balance Regarding Climate and Land Use Changes. Hydrology 2020, 7, 39 .
AMA StyleSalem Gharbia, Laurence Gill, Paul Johnston, Francesco Pilla. GEO-CWB: GIS-Based Algorithms for Parametrising the Responses of Catchment Dynamic Water Balance Regarding Climate and Land Use Changes. Hydrology. 2020; 7 (3):39.
Chicago/Turabian StyleSalem Gharbia; Laurence Gill; Paul Johnston; Francesco Pilla. 2020. "GEO-CWB: GIS-Based Algorithms for Parametrising the Responses of Catchment Dynamic Water Balance Regarding Climate and Land Use Changes." Hydrology 7, no. 3: 39.
The increase of temperature attributed to anthropogenic emissions is projected to continue in future climate scenarios. Protocols and policies are being put in place in several European countries to reduce both emissions and impact of human activities on climate. The Irish Reforestation policy is a good example of such protocols. Nevertheless, often contemplated policies do not take into account their potential effects on the atmospheric variables. This study aims to assess the influence of the increase of vegetation cover over Ireland, on surface temperature, livestock, and human heat comfort, using the Weather Research Forecast (WRF-ARW 3.7.1) model. Multi-scale numerical simulations are performed under two scenarios: (i) a “control scenario” considering no change in vegetation cover with respect to the prescribed one and (ii) a “green scenario” with increased tree cover based on the introduced Irish Reforestation policy. To simulate this policy, the cropland and vegetative mosaic is substituted with evergreen broad-leaf forest, increasing the total forest area from 19.7 to 36.2% of the land in the analyzed domain. This change in vegetation cover increases the temperature over the simulated domain up to \(0.7~^{\circ }\)C and, moreover, it enhances both human and livestock heat discomfort during the daytime, with different magnitude all over the domain. It is concluded that the reforestation policy, which is introduced to mitigate the climate warming and greenhouse gas emissions, causes a further increase in temperature along with heat discomfort to both human and livestock.
Arianna Valmassoi; Salem Gharbia; Silvana Di Sabatino; Prashant Kumar; Francesco Pilla. Future impacts of the reforestation policy on the atmospheric parameters in Ireland: a sensitivity study including heat discomfort impacts on humans and livestock. Personal and Ubiquitous Computing 2018, 23, 707 -721.
AMA StyleArianna Valmassoi, Salem Gharbia, Silvana Di Sabatino, Prashant Kumar, Francesco Pilla. Future impacts of the reforestation policy on the atmospheric parameters in Ireland: a sensitivity study including heat discomfort impacts on humans and livestock. Personal and Ubiquitous Computing. 2018; 23 (5-6):707-721.
Chicago/Turabian StyleArianna Valmassoi; Salem Gharbia; Silvana Di Sabatino; Prashant Kumar; Francesco Pilla. 2018. "Future impacts of the reforestation policy on the atmospheric parameters in Ireland: a sensitivity study including heat discomfort impacts on humans and livestock." Personal and Ubiquitous Computing 23, no. 5-6: 707-721.
The future planning, management and prediction of water demand and usage should be preceded by long-term variation analysis for related parameters in order to enhance the process of developing new scenarios whether for surface-water or ground-water resources. This paper aims to provide an appropriate methodology for long-term prediction for the water flow and water level parameters of the Shannon river in Ireland over a 30-year period from 1983–2013 through a framework that is composed of three phases: city wide scale analytics, data fusion, and domain knowledge data analytics phase which is the main focus of the paper that employs a machine learning model based on deep convolutional neural networks (DeepCNNs). We test our proposed deep learning model on three different water stations across the Shannon river and show it out-performs four well-known time-series forecasting models. We finally show how the proposed model simulate the predicted water flow and water level from 2013–2080. Our proposed solution can be very useful for the water authorities for better planning the future allocation of water resources among competing users such as agriculture, demotic and power stations. In addition, it can be used for capturing abnormalities by setting and comparing thresholds to the predicted water flow and water level.
Haytham Assem; Salem Ghariba; Gabor Makrai; Paul Johnston; Laurence Gill; Francesco Pilla. Urban Water Flow and Water Level Prediction Based on Deep Learning. Privacy Enhancing Technologies 2017, 317 -329.
AMA StyleHaytham Assem, Salem Ghariba, Gabor Makrai, Paul Johnston, Laurence Gill, Francesco Pilla. Urban Water Flow and Water Level Prediction Based on Deep Learning. Privacy Enhancing Technologies. 2017; ():317-329.
Chicago/Turabian StyleHaytham Assem; Salem Ghariba; Gabor Makrai; Paul Johnston; Laurence Gill; Francesco Pilla. 2017. "Urban Water Flow and Water Level Prediction Based on Deep Learning." Privacy Enhancing Technologies , no. : 317-329.
Over the years, urban growth models have proven to be effective in describing and estimating urban development and have consequently proven to be valuable for informed urban planning decision. Therefore, this paper investigates the implementation of an urban growth Cellular automata (CA) model using a GIS platform as a support tool for city planners, economists, urban ecologists and resource managers to help them establish decision making strategies and planning towards urban sustainable development. The area used as a test case is the River Shannon Basin in Ireland. This paper investigates the spatio-temporally varying effects of urbanization using a combined method of CA and GIS rasterization. The results generated from Cellular automata model indicated that the historical urban growth patterns in the River Shannon Basin area, in considerable part, be affected by distance to district centres, distance to roads, slope, neighbourhood effect, population density, and environmental factors with relatively high levels of explanation of the spatial variability. The optimal factors and the relative importance of the driving factors varied over time, thus, providing a valuable insight into the urban growth process. The developed model for Shannon catchment has been calibrated, validated, and used for predicting the future land use scenarios for the future time intervals 2020, 2050 and 2080. By involving natural and socioeconomic variables, the developed Cellular automata (CA) model had proved to be able to reproduce the historical urban growth process and assess the consequence of future urban growth. This paper presented as a novel application to the integrated CA-GIS model using a complicated land use dynamic system for Shannon catchment. The major conclusion from this paper was that land use simulation and projection without GIS rasterization formats cannot perform a multi-class, multi factors analysis which makes high accuracy simulation is impossible.
Salem S. Gharbia; Sara Abd Alfatah; Laurence Gill; Paul Johnston; Francesco Pilla. Land use scenarios and projections simulation using an integrated GIS cellular automata algorithms. Modeling Earth Systems and Environment 2016, 2, 151 .
AMA StyleSalem S. Gharbia, Sara Abd Alfatah, Laurence Gill, Paul Johnston, Francesco Pilla. Land use scenarios and projections simulation using an integrated GIS cellular automata algorithms. Modeling Earth Systems and Environment. 2016; 2 (3):151.
Chicago/Turabian StyleSalem S. Gharbia; Sara Abd Alfatah; Laurence Gill; Paul Johnston; Francesco Pilla. 2016. "Land use scenarios and projections simulation using an integrated GIS cellular automata algorithms." Modeling Earth Systems and Environment 2, no. 3: 151.
Climate impact studies especially in the field of hydrology often depend on climate change projections at fine spatial resolution. General circulation models (GCMs), which are the tools for estimating future climate scenarios, run on a very coarse scale, so the output from GCMs need to be downscaled to obtain a finer spatial resolution. This paper aims to present GIS platform as a downscaling environment through a suggested algorithm, which applies statistical downscaling models to multidimensional GCM-Ensembles simulations. Climate change projections for the Shannon River catchment in Ireland were developed for several climate variables from multi-GCM ensembles for three future time intervals forcing by different Representative Concentration Pathways (RCP): all these processes are implemented in a GIS platform through designed and developed GIS-based algorithm. This algorithm is used as a downscaling tool in GIS environment, which is unprecedented in literature. Statistical downscaling methods were used in the projection process after a particular verification and performance evaluation using several techniques such as Taylor diagram for each GCM-ensembles within independent sub-periods. The established statistical relationships were used to predict the response of the future climate from simulated climate model changes of the coarse scale variables. Significant changes in temperature, precipitation, wind speed, solar radiation and relative humidity were projected at a very fine spatial scale. It was concluded that the main source of uncertainty was related to the GCMs simulation and selection. In addition, it was obvious to conclude that GIS platform is an efficient tool for spatial downscaling using raster data forms.
Salem S. Gharbia; Laurence Gill; Paul Johnston; Francesco Pilla. Multi-GCM ensembles performance for climate projection on a GIS platform. Modeling Earth Systems and Environment 2016, 2, 1 -21.
AMA StyleSalem S. Gharbia, Laurence Gill, Paul Johnston, Francesco Pilla. Multi-GCM ensembles performance for climate projection on a GIS platform. Modeling Earth Systems and Environment. 2016; 2 (2):1-21.
Chicago/Turabian StyleSalem S. Gharbia; Laurence Gill; Paul Johnston; Francesco Pilla. 2016. "Multi-GCM ensembles performance for climate projection on a GIS platform." Modeling Earth Systems and Environment 2, no. 2: 1-21.
General circulation models (GCMs) are used for estimating future climate scenarios, run on a very coarse scale, so the outputs from GCMs need to be downscaled to obtain a finer spatial resolution. This paper provides a methodology for GCM-Ensembles performance evaluation using a GIS platform by applying statistical spatial downscaling methods. Statistical downscaling methods were used in the projection process after validation and performance evaluation using several techniques such as Taylor diagram for each GCM-ensembles within independent sub-periods. Climate change projections for the Shannon River catchment in Ireland were developed for temperature and precipitation from multi-GCM ensembles for three future time intervals forcing by different Representative Concentration Pathways (RCP). The changes in temperature and precipitation were spatially projected at a very fine spatial scale.
Salem Gharbia; Paul Johnston; Laurence Gill; Francesco Pilla. Using GIS based algorithms for GCMs' performance evaluation. 2016 18th Mediterranean Electrotechnical Conference (MELECON) 2016, 1 -6.
AMA StyleSalem Gharbia, Paul Johnston, Laurence Gill, Francesco Pilla. Using GIS based algorithms for GCMs' performance evaluation. 2016 18th Mediterranean Electrotechnical Conference (MELECON). 2016; ():1-6.
Chicago/Turabian StyleSalem Gharbia; Paul Johnston; Laurence Gill; Francesco Pilla. 2016. "Using GIS based algorithms for GCMs' performance evaluation." 2016 18th Mediterranean Electrotechnical Conference (MELECON) , no. : 1-6.
The concatenated effects of increased frequency of intense precipitations due to climate change and anthropogenic impacts in the form of construction in floodplains, channel straightening and increased presence of impermeable surfaces are increasing the incidence of floods in urban areas. This paper investigates behavioral responses to a natural hazard (flooding) by examining residential property values. The results of this investigation can be used to develop benefit/cost studies to assess the economic merits of policies that mitigates the risk of floods by using the residential housing market as a proxy for estimating these values since the choice of where to live often includes the choice of hazard level. The methodology described here also provides a mechanism for testing consumer behavior under uncertainty. This study uses a hedonic property price function to estimate the effects of flood hazards on residential property values. The study utilizes data from 158,890 residential home sales in Dublin, Ireland between 2006 and 2015. This area experienced significant flooding in October 2011. GIS is used to spatially characterize the houses included in the analysis by linking them to the following set of parameters included into the baseline regression: house price, house type and size (number of bedrooms and bathrooms), when it was on the market, and its location. Once the baseline regression model is built, then the variables included in it are regressed against the flood-risk. The distance between a set of amenities and the properties is also calculated using GIS. Results show that a house located within a floodplain has a lower market value than an equivalent house located outside the floodplain. Finally, the benefits resulting from the use of GIS-based spatial indicators of properties in hedonic regression models to quantify the accessibility to amenities as network travel distances are also demonstrated.
Salem Gharbia; Owen Naughton; Vincent Farrelly; Ronan Lyons; Francesco Pilla. Attitudes to systemic risk: The impact of flood risk on the housing market in Dublin. 2016 18th Mediterranean Electrotechnical Conference (MELECON) 2016, 1 -5.
AMA StyleSalem Gharbia, Owen Naughton, Vincent Farrelly, Ronan Lyons, Francesco Pilla. Attitudes to systemic risk: The impact of flood risk on the housing market in Dublin. 2016 18th Mediterranean Electrotechnical Conference (MELECON). 2016; ():1-5.
Chicago/Turabian StyleSalem Gharbia; Owen Naughton; Vincent Farrelly; Ronan Lyons; Francesco Pilla. 2016. "Attitudes to systemic risk: The impact of flood risk on the housing market in Dublin." 2016 18th Mediterranean Electrotechnical Conference (MELECON) , no. : 1-5.
Salem Gharbia; Adnan Aish; Thaer Abushbak; Ghassan Qishawi; Izz Al- Shawa; Abdalkarim Gharbia; Martina Zelenakova; Laurence Gill; Francesco Pilla. Evaluation of wastewater post-treatment options for reuse purposes in the agricultural sector under rural development conditions. Journal of Water Process Engineering 2016, 9, 111 -122.
AMA StyleSalem Gharbia, Adnan Aish, Thaer Abushbak, Ghassan Qishawi, Izz Al- Shawa, Abdalkarim Gharbia, Martina Zelenakova, Laurence Gill, Francesco Pilla. Evaluation of wastewater post-treatment options for reuse purposes in the agricultural sector under rural development conditions. Journal of Water Process Engineering. 2016; 9 ():111-122.
Chicago/Turabian StyleSalem Gharbia; Adnan Aish; Thaer Abushbak; Ghassan Qishawi; Izz Al- Shawa; Abdalkarim Gharbia; Martina Zelenakova; Laurence Gill; Francesco Pilla. 2016. "Evaluation of wastewater post-treatment options for reuse purposes in the agricultural sector under rural development conditions." Journal of Water Process Engineering 9, no. : 111-122.
Groundwater quality is a major environmental aspect which needs to be analyzed and managed depending on its spatial distribution. Utilization of insufficient management of groundwater resources in Gaza Strip, Palestine, produces not only a reduction in quantity but also deterioration in quality of groundwater. The aim of this study is to provide an overview for evaluation of groundwater quality in the Gaza Strip area as a case study for applying spatially distributed by using Geographic Information System (GIS) and geostatistical algorithms. The groundwater quality parameters, pH, total dissolved solids, total hardness, alkalinity, chloride, nitrate, sulfate, calcium, magnesium, and fluoride, were sampled and analyzed from the existing municipal and agricultural wells in Gaza Strip; maps of each parameter were created using geostatistical (Kriging) approach. Experimental semivariogram values were tested for different ordinary Kriging models to identify the best fitted for the ten water quality parameters and the best models were selected on the basis of mean square error (MSE), root mean square error (RMSE), average standard error (ASE), and root mean square standardized error (RMSSE). Maps of 10 groundwater quality parameters were used to calculate the groundwater quality index (GWQI) map using the index method. In general, the results showed that this integrated method is a sufficient assessment tool for environmental spatially distributed parameters.
Abdalkarim S. Gharbia; Salem Gharbia; Thaer Abushbak; Hisham Wafi; Adnan Aish; Martina Zelenakova; Francesco Pilla. Groundwater Quality Evaluation Using GIS Based Geostatistical Algorithms. Journal of Geoscience and Environment Protection 2016, 04, 89 -103.
AMA StyleAbdalkarim S. Gharbia, Salem Gharbia, Thaer Abushbak, Hisham Wafi, Adnan Aish, Martina Zelenakova, Francesco Pilla. Groundwater Quality Evaluation Using GIS Based Geostatistical Algorithms. Journal of Geoscience and Environment Protection. 2016; 04 (02):89-103.
Chicago/Turabian StyleAbdalkarim S. Gharbia; Salem Gharbia; Thaer Abushbak; Hisham Wafi; Adnan Aish; Martina Zelenakova; Francesco Pilla. 2016. "Groundwater Quality Evaluation Using GIS Based Geostatistical Algorithms." Journal of Geoscience and Environment Protection 04, no. 02: 89-103.
Salem Gharbia; Adnan Aish. Impacts of Climate Change on Groundwater of the Gaza Coastal Aquifer Using GIS and MODFLOW. Impacts of Climate Change on Groundwater of the Gaza Coastal Aquifer Using GIS and MODFLOW 2021, 1 .
AMA StyleSalem Gharbia, Adnan Aish. Impacts of Climate Change on Groundwater of the Gaza Coastal Aquifer Using GIS and MODFLOW. Impacts of Climate Change on Groundwater of the Gaza Coastal Aquifer Using GIS and MODFLOW. 2021; ():1.
Chicago/Turabian StyleSalem Gharbia; Adnan Aish. 2021. "Impacts of Climate Change on Groundwater of the Gaza Coastal Aquifer Using GIS and MODFLOW." Impacts of Climate Change on Groundwater of the Gaza Coastal Aquifer Using GIS and MODFLOW , no. : 1.
Salem Gharbia; Francesco Pilla. Potential Effects of Climate Change on Groundwater Recharge - a Case Study of the Gaza Strip, Palestine. 2021, 1 .
AMA StyleSalem Gharbia, Francesco Pilla. Potential Effects of Climate Change on Groundwater Recharge - a Case Study of the Gaza Strip, Palestine. . 2021; ():1.
Chicago/Turabian StyleSalem Gharbia; Francesco Pilla. 2021. "Potential Effects of Climate Change on Groundwater Recharge - a Case Study of the Gaza Strip, Palestine." , no. : 1.
Salem Gharbia; Adnan Aish. Climate Changes projection for the Gaza Strip Using GIS. 2021, 1 .
AMA StyleSalem Gharbia, Adnan Aish. Climate Changes projection for the Gaza Strip Using GIS. . 2021; ():1.
Chicago/Turabian StyleSalem Gharbia; Adnan Aish. 2021. "Climate Changes projection for the Gaza Strip Using GIS." , no. : 1.
Salem Gharbia; Adnan Aish. Future Changes in Climate Projected by a Multi-Model Ensemble Over The Gaza Strip, Palestine. 2021, 1 .
AMA StyleSalem Gharbia, Adnan Aish. Future Changes in Climate Projected by a Multi-Model Ensemble Over The Gaza Strip, Palestine. . 2021; ():1.
Chicago/Turabian StyleSalem Gharbia; Adnan Aish. 2021. "Future Changes in Climate Projected by a Multi-Model Ensemble Over The Gaza Strip, Palestine." , no. : 1.
Salem S. Gharbia; Francesco Pilla. Correlating Biochemical and Chemical Oxygen Demand of Influents And Effluents - a Case Study of Selected Wastewater Treatment Plants in the Gaza Strip, Palestine. 2021, 1 .
AMA StyleSalem S. Gharbia, Francesco Pilla. Correlating Biochemical and Chemical Oxygen Demand of Influents And Effluents - a Case Study of Selected Wastewater Treatment Plants in the Gaza Strip, Palestine. . 2021; ():1.
Chicago/Turabian StyleSalem S. Gharbia; Francesco Pilla. 2021. "Correlating Biochemical and Chemical Oxygen Demand of Influents And Effluents - a Case Study of Selected Wastewater Treatment Plants in the Gaza Strip, Palestine." , no. : 1.