Rahman Khatibi - Academia.edu (original) (raw)

Papers by Rahman Khatibi

Environmental Science and Pollution Research, Feb 13, 2017

Vulnerability indices of an aquifer assessed by different fuzzy logic (FL) models often give rise... more Vulnerability indices of an aquifer assessed by different fuzzy logic (FL) models often give rise to differing values with no theoretical or empirical basis to establish a validated baseline or to develop a comparison basis between the modeling results and baselines, if any. Therefore, this research presents a supervised committee fuzzy logic (SCFL) method, which uses artificial neural networks to overarch and combine a selection of FL models. The indices are expressed by the widely used DRASTIC framework, which include geological, hydrological, and hydrogeological parameters often subject to uncertainty. DRASTIC indices represent collectively intrinsic (or natural) vulnerability and give a sense of contaminants, such as nitrate-N, percolating to aquifers from the surface. The study area is an aquifer in Ardabil plain, the province of Ardabil, northwest Iran. Improvements on vulnerability indices are achieved by FL techniques, which comprise Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Larsen fuzzy logic (LFL). As the correlation between estimated DRASTIC vulnerability index values and nitrate-N values is as low as 0.4, it is improved significantly by FL models (SFL, MFL, and LFL), which perform in similar ways but have differences. Their synergy is exploited by SCFL and uses the FL modeling results "conditioned" by nitrate-N values to raise their correlation to higher than 0.9.

Urban Climate

Aspects of ecological issues arising from rapid urbanization are investigated in this paper using... more Aspects of ecological issues arising from rapid urbanization are investigated in this paper using Land Surface Temperature (LST) in terms of radiation budgets studied by heat conduct balance using a geospatial approach. It uses the data from USGS Landsat ETM+ 4-5 and Landsat-8 OLI imagery acquired on February 6, 1988 and October 14, 2021. The geospatial technique examines the effects of changes in Land Use/Land Cover (LSLC) in terms of NDVI classification on the distribution of surface temperature. The remote sensing approach identifies changes in land use, their influence on surface temperature, and variations in average LST caused by these hotspots. This study examines the distribution of LST in Kancheepuram district, Tamil Nadu, India. The LSTs ranged from 14 • C to 31 • C in 1985, from 25 • C to 39 • C in 2005, and from 31 • C to 47 • C in 2021. The results show a 152% increase in the mean LST in 4 decades. The LST high values were observed in urban and industrial regions with buildings, impervious sidewalks, and sparse vegetation. The intensity at hotspots was highest in urbanized and sparsely vegetated regions. The low Urban Heat Island (UHI) effect was measured in vegetated areas. Industrial areas and parking lots were commonly classified as high-intensity hotspots. The elevated temperatures are attributable to the materials used in the construction at these locations. Further research is needed to better understand the reflectance of thermal radiations by certain materials used in urban regions and how to minimize temperatures in such locations.

Risk, Reliability and Sustainable Remediation in the Field of Civil and Environmental Engineering, 2022

Estuarine, Coastal and Shelf Science, 2020

This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Water Resources Management, 2020

Mapping for Groundwater Potential Indices (GPI) is investigated for study areas with sparse data ... more Mapping for Groundwater Potential Indices (GPI) is investigated for study areas with sparse data by the customary ten general-purpose data layers with a scoring system of rates and weights but assigning their values give rise to subjectivity. Learning rates/weights from site-specific data reduces subjectivity through unsupervised models. The use of supervised models requires target values, and the paper derives their values from the record at all the productive wells by developing a binary classification model. The paper formulates an Inclusive Multiple Modelling (IMM) strategy to learn from the site data at two levels: at Level 1 : two unsupervised ‘base’ models and four supervised ‘base’ models are investigated; at Level 2 the IMM strategies include a supervised ‘combiner’ model, which uses outputs of unsupervised base models; as well as an unsupervised ‘combiner’ model, which uses outputs of supervised base models. Performance metrics are derived by the Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC). The results show that unsupervised learning at Level 2 (using supervised base models) may reduce subjectivity but even supervised learning at Level 1 can be effective in extracting essential information from target values. Although unsupervised models would extract marginal information from models at Level 1, a supervised model at Level 2 can extract good information from unsupervised models at Level 1. Graphical Abstract

Arabian Journal of Geosciences, 2016

An application of remote sensing and geographic information system (GIS) has become one of the es... more An application of remote sensing and geographic information system (GIS) has become one of the essential techniques in the field of hydrogeological science that helps in assessing, monitoring, conserving, and managing groundwater resources. In the present study, modified DRASTIC model has been used to access the groundwater vulnerable zone of Kharun Basin. The modification has been done by replacing hydraulic conductivity parameter to land use. The aim of this study is to determine susceptible zone for groundwater pollution by integrating hydrogeological layers in GIS environment. The final index map shows that the resulting index value varies from 86 to 191; further, the zones are divided into five classes. The results showed that out of the total area of 4191 km2, an area of about 8, 197, 2730, 1229, and 27 km2 lies in very low, low, moderate, high and very high vulnerable zones, respectively. Sensitivity analysis is also performed in this study, and it reveals that depth of water table, land use, and topography parameters cause large variations in vulnerability index as compared to other parameters. Moreover, land use and topography were found to be more effective parameters in assessing groundwater vulnerability than assumed by original model. Model validation shows a good correlation between nitrate sample and resulting DRASTI-LU index. This study produces a very valuable tool for policy makers as it reveals a very comprehensive indication of vulnerability to ground water contamination. The knowledge about high vulnerable zone is important for local authorities to manage and monitor groundwater resources.

Meteorology and Atmospheric Physics, 2015

Hourly wind speed forecasting is presented by a modeling study with possible applications to prac... more Hourly wind speed forecasting is presented by a modeling study with possible applications to practical problems including farming wind energy, aircraft safety and airport operations. Modeling techniques employed in this paper for such short-term predictions are based on the machine learning techniques of artificial neural networks (ANNs) and genetic expression programming (GEP). Recorded values of wind speed were used, which comprised 8 years of collected data at the Kersey site, Colorado, USA. The January data over the first 7 years (2005-2011) were used for model training; and the January data for 2012 were used for model testing. A number of model structures were investigated for the validation of the robustness of these two techniques. The prediction results were compared with those of a multiple linear regression (MLR) method and with the Persistence method developed for the data. The model performances were evaluated using the correlation coefficient, root mean square error, Nash-Sutcliffe efficiency coefficient and Akaike information criterion. The results indicate that forecasting wind speed is feasible using past records of wind speed alone, but the maximum lead time for the data was found to be 14 h. The results show that different techniques would lead to different results, where the choice between them is not easy. Thus, decision making has to be informed of these modeling results and decisions should be arrived at on the basis of an understanding of inherent uncertainties. The results show that both GEP and ANN are equally credible selections and even MLR should not be dismissed, as it has its uses.

Ecological Indicators, 2018

A novel methodology is introduced for the spatial indexing of groundwater drought 'risks' (GDRs).... more A novel methodology is introduced for the spatial indexing of groundwater drought 'risks' (GDRs). It combines reliability analysis and standardised water-level index (SWI), which is readily applicable to areas with sparse data on groundwater depth (GWD) measurements. In reliability analysis, GWDs are reformulated in terms of load, which accounts for external effects, e.g. withdrawals and recharge, as well as resistance, which accounts for system capacity with regard to drought intensities (mild, moderate, severe and extreme). Reliability analysis formulates a novel procedure by using loads and resistance to formulate a performance function, which can be treated by statistical techniques, and thereby derives values of GDR, defined as failure of an operational system but without considering consequences. GDRs at observation wells are spatially distributed by using an interpolation technique. The methodology allows for estimating time variability in GDR to derive an environmental/ ecological hazard indicator (EHI), which can serve in the management and planning of predicting groundwater drought. A Graphical User Interface (GDR V.1.0) is developed to serve as a decision support system and to derive GDR and EHI values.

Journal of Water and Climate Change, Jul 19, 2021

Topical research on hydrological behaviour of climate change in terms of downscaling of monthly p... more Topical research on hydrological behaviour of climate change in terms of downscaling of monthly precipitation is investigated in this paper by formulating an inclusive multiple modelling (IMM) strategy. IMM strategies manage multiple models at two levels and the paper uses statistical downscaling model, Sugeno fuzzy logic and support vector machine at Level 1 and feeds their outputs to a neuro-fuzzy model at Level 2. In the downscaling stage, large-scale NCEP (National Centres for Environmental Prediction)/NCAR (National Centre for Atmospheric Research) data are used for a station with local data record from 1961 to 2005 for training and testing Level 1 models. The results are found to be 'fit-for-purpose', but the variations between them signify some room for improvements. The model at Level 2 combines outputs of those at Level 1 and produces Level 2 results, which improve compared with those at the Level 1 models in terms of dispersion of residual errors. In this way, IMM provides a more defensible modelling strategy for application in the projection stage. The comparison between observed and projected precipitation indicates that precipitation will be likely to reduce compared with observed precipitation in cold seasons (October-February), but the projected precipitation will be likely to increase slightly in wet seasons (April and May).

Journal of Hydrology, Aug 1, 2020

Abstract A framework is formulated in this paper for data-driven modelling practices to character... more Abstract A framework is formulated in this paper for data-driven modelling practices to characterise Inclusive Multiple Modelling (IMM) practices with multiple goals of enhancing the extracted information from given datasets and learning from multiple models. This can be a shift from traditional practices with the single goal of selecting a ‘superior’ model from multiple models without a statistical justification, which may be referred to as Exclusionary Multiple Modelling (EMM) practices. The dimensions of the framework for IMM practices are: Model R euse (M R ), H ierarchy and/or Recursion ( H R), a provision of ‘ E lastic’ model-Learning Environment ( E LE) and Goal- O rientation (G O ) – leading to the acronym of RHEO. Proof-of-concept is presented for IMM-RHEO using three testcases: the Caspian Sea (19-years of data), Lake Urmia (50-years of data) and Lake Van (73-years of data), approx. 500 km apart. IMM practices are implemented by investigating four strategies for each testcase. The learning from the results includes: (i) the IMM strategies are capable of enhancing the accuracy of predicted water levels; (ii) the accuracy of predicting the sea-state of the Caspian Sea serves confidence building on accuracy; and (iii) the time-length of the record of Lake Van is long enough for the confidence building on the study of possible trends. IMM serves a bottom-up learning opportunity for Lake Urmia that its distressed state is due to being deprived of compensation flows without contributions from climate change. Arguably, a good management policy is the key for its restoration. IMM is at its infancy but arguably, its potential application areas are wide.

Social Science Research Network, 2022

Journal of Hydraulic Engineering, May 1, 1999

Journal of Environmental Management, Sep 1, 2020

Nitrate pollution of groundwater through spatial models is investigated in this paper by using a ... more Nitrate pollution of groundwater through spatial models is investigated in this paper by using a sample of nitrate values at monitoring wells using the data from four seasons of a year, in which data are sparse. Two spatial modelling strategies are formulated at two levels, in which Strategy 1 comprises: three variations of krigingbased models (ordinary kriging, simple kriging and universal kriging), which are constructed at Level 1 to predict nitrate concentrations; and a Multiple Co-Kriging (MCoK) model is used at Level 2 to enhance the accuracy of the predictions. Strategy 2 is also at two levels but employs Indicator Kriging (IK) at Level 1 as a probabilistic spatial model to predict areas at risk of exceeding two thresholds of 37.5 mg/L and 50 mg/L of nitrate concentration, and Multiple Co-Indicator Kriging (MCoIK) at Level 2 for a better accuracy. The improvements at Level 2 for both strategies are remarkable and hence they are used to gain an insight into inherent problems. The results of a study delineate areas with excessive nitrate concentrations, which are in the vicinity of urban areas and hence reflect poor planning practices since the 1990s. The results further reveal the patterns on sensitivities to seasonal variations driven by aquifer recharge and strong dilution processes in spring times; and on the role of pumpage impacting aquifers giving rise to possible hotspots of nitrate concentrations.

Journal of Cleaner Production, Aug 1, 2021

Abstract The topical research on Human Health Risk Assessment (HHRA) is investigated in this pape... more Abstract The topical research on Human Health Risk Assessment (HHRA) is investigated in this paper but in the context of uncertainty using Monto Carlo Simulation (MCS) tools. This study aims to capture some of the inherent uncertainties by implementing MCS in two dimensions: Dimension 1 considers the variability within the prescribed parameters; and Dimension 2 captures the uncertainty due to functional definitions of some of the moments of the selected distributions and interdependency of correlated parameters at a higher level. The 2D MCS model of HHRA is applied to risk assessment of a study area contaminated by arsenic, a challenging case in which arsenic has a geogenic origin but the risk is triggered by human activities. The results indicate that (i) the uncertainty in the results for the site reflects a probability distribution of risk with a positive skew; and (ii) the uncertainty increases by increasing arsenic concentration, as indicated by whisker box diagrams. The study sheds light on identifying remedial strategies since risk corresponding to Reasonable Maximum Exposure (RME Risk) is higher than the concern level of risk recommended by USEPA. The risk corresponding to the central tendency exposure is also higher than the concern level of risk in most of the samples. The paper investigates current water supply sources in the residential areas and their risk values and accordingly identifies a set of possible action plans to mitigate risk. However, the formulated 2D MCS can be extended by employing local data for deriving probability distributions and different uncertainty techniques.

Journal of Environmental Management, Apr 1, 2023

Ecology and the Environment, Jun 24, 2008

The landscape of flood risk is being populated by applying the risk concept to a wide range of fl... more The landscape of flood risk is being populated by applying the risk concept to a wide range of flood management systems against a background where rethinking is driving the shift from flood defence to flood risk management. This is in a background, where tools, concepts, systems and applications are diversifying by adapting to varying complexities. It is concerning that inherent interconnections are overlooked among systems exposed to flood risk from different sources. This paper aims to stimulate debate on interconnectivity among systems with exposures to flood risk, refers to this as the systemic flood risk, and recognises some reciprocity between interconnectivity and diversity.

Systemic knowledge management in hydraulic systems:

Environmental Science and Pollution Research, Feb 13, 2017

Vulnerability indices of an aquifer assessed by different fuzzy logic (FL) models often give rise... more Vulnerability indices of an aquifer assessed by different fuzzy logic (FL) models often give rise to differing values with no theoretical or empirical basis to establish a validated baseline or to develop a comparison basis between the modeling results and baselines, if any. Therefore, this research presents a supervised committee fuzzy logic (SCFL) method, which uses artificial neural networks to overarch and combine a selection of FL models. The indices are expressed by the widely used DRASTIC framework, which include geological, hydrological, and hydrogeological parameters often subject to uncertainty. DRASTIC indices represent collectively intrinsic (or natural) vulnerability and give a sense of contaminants, such as nitrate-N, percolating to aquifers from the surface. The study area is an aquifer in Ardabil plain, the province of Ardabil, northwest Iran. Improvements on vulnerability indices are achieved by FL techniques, which comprise Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Larsen fuzzy logic (LFL). As the correlation between estimated DRASTIC vulnerability index values and nitrate-N values is as low as 0.4, it is improved significantly by FL models (SFL, MFL, and LFL), which perform in similar ways but have differences. Their synergy is exploited by SCFL and uses the FL modeling results "conditioned" by nitrate-N values to raise their correlation to higher than 0.9.

Urban Climate

Aspects of ecological issues arising from rapid urbanization are investigated in this paper using... more Aspects of ecological issues arising from rapid urbanization are investigated in this paper using Land Surface Temperature (LST) in terms of radiation budgets studied by heat conduct balance using a geospatial approach. It uses the data from USGS Landsat ETM+ 4-5 and Landsat-8 OLI imagery acquired on February 6, 1988 and October 14, 2021. The geospatial technique examines the effects of changes in Land Use/Land Cover (LSLC) in terms of NDVI classification on the distribution of surface temperature. The remote sensing approach identifies changes in land use, their influence on surface temperature, and variations in average LST caused by these hotspots. This study examines the distribution of LST in Kancheepuram district, Tamil Nadu, India. The LSTs ranged from 14 • C to 31 • C in 1985, from 25 • C to 39 • C in 2005, and from 31 • C to 47 • C in 2021. The results show a 152% increase in the mean LST in 4 decades. The LST high values were observed in urban and industrial regions with buildings, impervious sidewalks, and sparse vegetation. The intensity at hotspots was highest in urbanized and sparsely vegetated regions. The low Urban Heat Island (UHI) effect was measured in vegetated areas. Industrial areas and parking lots were commonly classified as high-intensity hotspots. The elevated temperatures are attributable to the materials used in the construction at these locations. Further research is needed to better understand the reflectance of thermal radiations by certain materials used in urban regions and how to minimize temperatures in such locations.

Risk, Reliability and Sustainable Remediation in the Field of Civil and Environmental Engineering, 2022

Estuarine, Coastal and Shelf Science, 2020

This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Water Resources Management, 2020

Mapping for Groundwater Potential Indices (GPI) is investigated for study areas with sparse data ... more Mapping for Groundwater Potential Indices (GPI) is investigated for study areas with sparse data by the customary ten general-purpose data layers with a scoring system of rates and weights but assigning their values give rise to subjectivity. Learning rates/weights from site-specific data reduces subjectivity through unsupervised models. The use of supervised models requires target values, and the paper derives their values from the record at all the productive wells by developing a binary classification model. The paper formulates an Inclusive Multiple Modelling (IMM) strategy to learn from the site data at two levels: at Level 1 : two unsupervised ‘base’ models and four supervised ‘base’ models are investigated; at Level 2 the IMM strategies include a supervised ‘combiner’ model, which uses outputs of unsupervised base models; as well as an unsupervised ‘combiner’ model, which uses outputs of supervised base models. Performance metrics are derived by the Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC). The results show that unsupervised learning at Level 2 (using supervised base models) may reduce subjectivity but even supervised learning at Level 1 can be effective in extracting essential information from target values. Although unsupervised models would extract marginal information from models at Level 1, a supervised model at Level 2 can extract good information from unsupervised models at Level 1. Graphical Abstract

Arabian Journal of Geosciences, 2016

An application of remote sensing and geographic information system (GIS) has become one of the es... more An application of remote sensing and geographic information system (GIS) has become one of the essential techniques in the field of hydrogeological science that helps in assessing, monitoring, conserving, and managing groundwater resources. In the present study, modified DRASTIC model has been used to access the groundwater vulnerable zone of Kharun Basin. The modification has been done by replacing hydraulic conductivity parameter to land use. The aim of this study is to determine susceptible zone for groundwater pollution by integrating hydrogeological layers in GIS environment. The final index map shows that the resulting index value varies from 86 to 191; further, the zones are divided into five classes. The results showed that out of the total area of 4191 km2, an area of about 8, 197, 2730, 1229, and 27 km2 lies in very low, low, moderate, high and very high vulnerable zones, respectively. Sensitivity analysis is also performed in this study, and it reveals that depth of water table, land use, and topography parameters cause large variations in vulnerability index as compared to other parameters. Moreover, land use and topography were found to be more effective parameters in assessing groundwater vulnerability than assumed by original model. Model validation shows a good correlation between nitrate sample and resulting DRASTI-LU index. This study produces a very valuable tool for policy makers as it reveals a very comprehensive indication of vulnerability to ground water contamination. The knowledge about high vulnerable zone is important for local authorities to manage and monitor groundwater resources.

Meteorology and Atmospheric Physics, 2015

Hourly wind speed forecasting is presented by a modeling study with possible applications to prac... more Hourly wind speed forecasting is presented by a modeling study with possible applications to practical problems including farming wind energy, aircraft safety and airport operations. Modeling techniques employed in this paper for such short-term predictions are based on the machine learning techniques of artificial neural networks (ANNs) and genetic expression programming (GEP). Recorded values of wind speed were used, which comprised 8 years of collected data at the Kersey site, Colorado, USA. The January data over the first 7 years (2005-2011) were used for model training; and the January data for 2012 were used for model testing. A number of model structures were investigated for the validation of the robustness of these two techniques. The prediction results were compared with those of a multiple linear regression (MLR) method and with the Persistence method developed for the data. The model performances were evaluated using the correlation coefficient, root mean square error, Nash-Sutcliffe efficiency coefficient and Akaike information criterion. The results indicate that forecasting wind speed is feasible using past records of wind speed alone, but the maximum lead time for the data was found to be 14 h. The results show that different techniques would lead to different results, where the choice between them is not easy. Thus, decision making has to be informed of these modeling results and decisions should be arrived at on the basis of an understanding of inherent uncertainties. The results show that both GEP and ANN are equally credible selections and even MLR should not be dismissed, as it has its uses.

Ecological Indicators, 2018

A novel methodology is introduced for the spatial indexing of groundwater drought 'risks' (GDRs).... more A novel methodology is introduced for the spatial indexing of groundwater drought 'risks' (GDRs). It combines reliability analysis and standardised water-level index (SWI), which is readily applicable to areas with sparse data on groundwater depth (GWD) measurements. In reliability analysis, GWDs are reformulated in terms of load, which accounts for external effects, e.g. withdrawals and recharge, as well as resistance, which accounts for system capacity with regard to drought intensities (mild, moderate, severe and extreme). Reliability analysis formulates a novel procedure by using loads and resistance to formulate a performance function, which can be treated by statistical techniques, and thereby derives values of GDR, defined as failure of an operational system but without considering consequences. GDRs at observation wells are spatially distributed by using an interpolation technique. The methodology allows for estimating time variability in GDR to derive an environmental/ ecological hazard indicator (EHI), which can serve in the management and planning of predicting groundwater drought. A Graphical User Interface (GDR V.1.0) is developed to serve as a decision support system and to derive GDR and EHI values.

Journal of Water and Climate Change, Jul 19, 2021

Topical research on hydrological behaviour of climate change in terms of downscaling of monthly p... more Topical research on hydrological behaviour of climate change in terms of downscaling of monthly precipitation is investigated in this paper by formulating an inclusive multiple modelling (IMM) strategy. IMM strategies manage multiple models at two levels and the paper uses statistical downscaling model, Sugeno fuzzy logic and support vector machine at Level 1 and feeds their outputs to a neuro-fuzzy model at Level 2. In the downscaling stage, large-scale NCEP (National Centres for Environmental Prediction)/NCAR (National Centre for Atmospheric Research) data are used for a station with local data record from 1961 to 2005 for training and testing Level 1 models. The results are found to be 'fit-for-purpose', but the variations between them signify some room for improvements. The model at Level 2 combines outputs of those at Level 1 and produces Level 2 results, which improve compared with those at the Level 1 models in terms of dispersion of residual errors. In this way, IMM provides a more defensible modelling strategy for application in the projection stage. The comparison between observed and projected precipitation indicates that precipitation will be likely to reduce compared with observed precipitation in cold seasons (October-February), but the projected precipitation will be likely to increase slightly in wet seasons (April and May).

Journal of Hydrology, Aug 1, 2020

Abstract A framework is formulated in this paper for data-driven modelling practices to character... more Abstract A framework is formulated in this paper for data-driven modelling practices to characterise Inclusive Multiple Modelling (IMM) practices with multiple goals of enhancing the extracted information from given datasets and learning from multiple models. This can be a shift from traditional practices with the single goal of selecting a ‘superior’ model from multiple models without a statistical justification, which may be referred to as Exclusionary Multiple Modelling (EMM) practices. The dimensions of the framework for IMM practices are: Model R euse (M R ), H ierarchy and/or Recursion ( H R), a provision of ‘ E lastic’ model-Learning Environment ( E LE) and Goal- O rientation (G O ) – leading to the acronym of RHEO. Proof-of-concept is presented for IMM-RHEO using three testcases: the Caspian Sea (19-years of data), Lake Urmia (50-years of data) and Lake Van (73-years of data), approx. 500 km apart. IMM practices are implemented by investigating four strategies for each testcase. The learning from the results includes: (i) the IMM strategies are capable of enhancing the accuracy of predicted water levels; (ii) the accuracy of predicting the sea-state of the Caspian Sea serves confidence building on accuracy; and (iii) the time-length of the record of Lake Van is long enough for the confidence building on the study of possible trends. IMM serves a bottom-up learning opportunity for Lake Urmia that its distressed state is due to being deprived of compensation flows without contributions from climate change. Arguably, a good management policy is the key for its restoration. IMM is at its infancy but arguably, its potential application areas are wide.

Social Science Research Network, 2022

Journal of Hydraulic Engineering, May 1, 1999

Journal of Environmental Management, Sep 1, 2020

Nitrate pollution of groundwater through spatial models is investigated in this paper by using a ... more Nitrate pollution of groundwater through spatial models is investigated in this paper by using a sample of nitrate values at monitoring wells using the data from four seasons of a year, in which data are sparse. Two spatial modelling strategies are formulated at two levels, in which Strategy 1 comprises: three variations of krigingbased models (ordinary kriging, simple kriging and universal kriging), which are constructed at Level 1 to predict nitrate concentrations; and a Multiple Co-Kriging (MCoK) model is used at Level 2 to enhance the accuracy of the predictions. Strategy 2 is also at two levels but employs Indicator Kriging (IK) at Level 1 as a probabilistic spatial model to predict areas at risk of exceeding two thresholds of 37.5 mg/L and 50 mg/L of nitrate concentration, and Multiple Co-Indicator Kriging (MCoIK) at Level 2 for a better accuracy. The improvements at Level 2 for both strategies are remarkable and hence they are used to gain an insight into inherent problems. The results of a study delineate areas with excessive nitrate concentrations, which are in the vicinity of urban areas and hence reflect poor planning practices since the 1990s. The results further reveal the patterns on sensitivities to seasonal variations driven by aquifer recharge and strong dilution processes in spring times; and on the role of pumpage impacting aquifers giving rise to possible hotspots of nitrate concentrations.

Journal of Cleaner Production, Aug 1, 2021

Abstract The topical research on Human Health Risk Assessment (HHRA) is investigated in this pape... more Abstract The topical research on Human Health Risk Assessment (HHRA) is investigated in this paper but in the context of uncertainty using Monto Carlo Simulation (MCS) tools. This study aims to capture some of the inherent uncertainties by implementing MCS in two dimensions: Dimension 1 considers the variability within the prescribed parameters; and Dimension 2 captures the uncertainty due to functional definitions of some of the moments of the selected distributions and interdependency of correlated parameters at a higher level. The 2D MCS model of HHRA is applied to risk assessment of a study area contaminated by arsenic, a challenging case in which arsenic has a geogenic origin but the risk is triggered by human activities. The results indicate that (i) the uncertainty in the results for the site reflects a probability distribution of risk with a positive skew; and (ii) the uncertainty increases by increasing arsenic concentration, as indicated by whisker box diagrams. The study sheds light on identifying remedial strategies since risk corresponding to Reasonable Maximum Exposure (RME Risk) is higher than the concern level of risk recommended by USEPA. The risk corresponding to the central tendency exposure is also higher than the concern level of risk in most of the samples. The paper investigates current water supply sources in the residential areas and their risk values and accordingly identifies a set of possible action plans to mitigate risk. However, the formulated 2D MCS can be extended by employing local data for deriving probability distributions and different uncertainty techniques.

Journal of Environmental Management, Apr 1, 2023

Ecology and the Environment, Jun 24, 2008

The landscape of flood risk is being populated by applying the risk concept to a wide range of fl... more The landscape of flood risk is being populated by applying the risk concept to a wide range of flood management systems against a background where rethinking is driving the shift from flood defence to flood risk management. This is in a background, where tools, concepts, systems and applications are diversifying by adapting to varying complexities. It is concerning that inherent interconnections are overlooked among systems exposed to flood risk from different sources. This paper aims to stimulate debate on interconnectivity among systems with exposures to flood risk, refers to this as the systemic flood risk, and recognises some reciprocity between interconnectivity and diversity.

Systemic knowledge management in hydraulic systems: