DRASTIC is a very simple and common model used for the assessment of groundwater to contamination.This model is widely used across the world in various hydrogeological environments for groundwater vulnerability assess...DRASTIC is a very simple and common model used for the assessment of groundwater to contamination.This model is widely used across the world in various hydrogeological environments for groundwater vulnerability assessment.The Ohio Water Well Association(OWWA)developed DRASTIC model in 1987.Over the years,several modifications have been made in this model as per the need of the regional assessment of groundwater to contamination.This model has fixed weights for its parameters and fixed ratings for the sub-parameters under the main parameters.The weights and ratings of DRASTIC parameters were fixed on the basis of Delphi network technique,which is the best technique for the consensus-building of experts,but it lacks scientific explanations.Over the years,several optimization techniques have been used to optimize these weights and ratings.This work intends to present a critical analysis of decision optimization techniques used to get the optimum values of weights and ratings.The inherent pros and cons and the optimization challenges associated with these techniques have also been discussed.The finding of this study is that the application of MCDA optimization techniques used to optimize the weights and ratings of DRASTIC model to assess the vulnerability of groundwater depend on the availability of hydrogeological data,the pilot study area and the level of required accuracy for earmarking the vulnerable regions.It is recommended that one must choose the appropriate MCDA technique for the particular region because unnecessary complex structure for optimization process takes more time,efforts,resources,and implementation costs.展开更多
Floods are phenomenon with significant socio-economic implications mainly for human loss, agriculture, livestock, soil loss and land degradation, for which many researchers try to identify the most appropriate methodo...Floods are phenomenon with significant socio-economic implications mainly for human loss, agriculture, livestock, soil loss and land degradation, for which many researchers try to identify the most appropriate methodologies by analyzing their temporal and spatial development. This study therefore attempts to employ the GIS-based multi-criteria decision analysis and analytical hierarchy process techniques to derive the flood risks management on rice productivity in the Gishari Agricultural Marshland in Rwamagana district, Rwanda. Here, six influencing potential factors to flooding, including river slope, soil texture, Land Use Land Cover through Land Sat 8, rainfall, river distance and Digital Elevation Model are considered for the delineation of flood risk zones. Data acquisition like Landsat 8 images, DEM, land use land cover, slope, and soil class in the study area were considered. Results showed that if the DEM is outdated or inaccurate due to changes in the terrain, such as construction, excavation, or erosion, the predicted flood patterns might not reflect the actual water flow. This could result unexpected flood extents and depths, potentially inundating rice fields that were not previously at risk and this, expectedly explained that the increase 1 m in elevation would reduce the rice productivity by 0.17% due to unplanned flood risks in marshland. It was found that the change in rainfall distribution in Gishari agricultural marshland would also decrease the rice productivity by 0.0018%, which is a sign that rainfall is a major factor of flooding in rice scheme. Rainfall distribution plays a crucial role in flooding analysis and can directly impact rice productivity. Oppositely, another causal factor was Land Use Land Cover (LULC), where the Multivariate Logistic Regression Model Analysis findings showed that the increase of one unit in Land Use Land Cover would increase rice productivity by 0.17% of the total rice productivity from the Gishari Agricultural Marshland. Based on findings from these techniques, the Gishari Agricultural Marshlands having steeped land with grassland is classified into five classes of flooding namely very low, low, moderate, high, and very high which include 430%, 361%, 292%, 223%, and 154%. Government of Rwanda and other implementing agencies and major key actors have to contribute on soil and water conservation strategies to reduce the runoff and soil erosion as major contributors of flooding.展开更多
Financial performance analysis is of vital importance those involved in a business(e.g.,shareholders,creditors,partners,and company managers).An accurate and appropriate performance measurement is critical for decisio...Financial performance analysis is of vital importance those involved in a business(e.g.,shareholders,creditors,partners,and company managers).An accurate and appropriate performance measurement is critical for decision-makers to achieve efficient results.Integrated performance measurement,by its nature,consists of multiple criteria with different levels of importance.Multiple Criteria Decision Analysis(MCDA)methods have become increasingly popular for solving complex problems,especially over the last two decades.There are different evaluation methodologies in the literature for selecting the most appropriate one among over 200 MCDA methods.This study comprehensively analyzed 41 companies traded on the Borsa Istanbul Corporate Governance Index for 10 quarters using SWARA,CRITIC,and SD integrated with eight different MCDA method algorithms to determine the position of Turkey's most transparent companies in terms of financial performance.In this study,we propose"stock returns"as a benchmark in comparing and evaluating MCDA methods.Moreover,we calculate the"rank reversal performance of MCDA methods".Finally,we performed a"standard deviation"analysis to identify the objective and characteristic trends for each method.Interestingly,all these innovative comparison procedures suggest that PROMETHEE II(preference ranking organization method for enrichment of evaluations II)and FUCA(Faire Un Choix Adéquat)are the most suitable MCDA methods.In other words,these methods produce a higher correlation with share price;they have fewer rank reversal problems,the distribution of scores they produce is wider,and the amount of information is higher.Thus,it can be said that these advantages make them preferable.The results show that this innovative methodological procedure based on'knowledge discovery'is verifiable,robust and efficient when choosing the MCDA method.展开更多
A municipal solid waste(MSW)management system needs solid waste management(SWM)techniques where the presence of a sanitary landfill is vital.One of the most important issues of sanitary landfilling is to locate the f...A municipal solid waste(MSW)management system needs solid waste management(SWM)techniques where the presence of a sanitary landfill is vital.One of the most important issues of sanitary landfilling is to locate the facility to an optimal location.Despite the versatility and case-dependent nature of conventional expert-based site selection procedures,the number of sites to be chosen increases with increased population forcing a number of constraints.Consequently,constraints and environmental regulations mechanically mask unsuitable areas,leaving very little areas to be assessed.This turns the situation into a challenging issue for a geographical information system(GIS)used with multicriteria decision analysis(MCDA),to select optimal site.The study aims to apply MCDA integrated with GIS to select possible sites of a MSW landfill with the same expert and same cognitive parameters while compared with the already present one.Results of this study revealed that conventional expert-based methods could not always evaluate all constraints at the same time and map reproduction is limited when parameter maps are changing rapidly in time.In order to produce cognitive and reproducible analyses,GIS with MCDA integration offers a good solution for site selection issue and forms a good alternative for conventional methods.展开更多
文摘DRASTIC is a very simple and common model used for the assessment of groundwater to contamination.This model is widely used across the world in various hydrogeological environments for groundwater vulnerability assessment.The Ohio Water Well Association(OWWA)developed DRASTIC model in 1987.Over the years,several modifications have been made in this model as per the need of the regional assessment of groundwater to contamination.This model has fixed weights for its parameters and fixed ratings for the sub-parameters under the main parameters.The weights and ratings of DRASTIC parameters were fixed on the basis of Delphi network technique,which is the best technique for the consensus-building of experts,but it lacks scientific explanations.Over the years,several optimization techniques have been used to optimize these weights and ratings.This work intends to present a critical analysis of decision optimization techniques used to get the optimum values of weights and ratings.The inherent pros and cons and the optimization challenges associated with these techniques have also been discussed.The finding of this study is that the application of MCDA optimization techniques used to optimize the weights and ratings of DRASTIC model to assess the vulnerability of groundwater depend on the availability of hydrogeological data,the pilot study area and the level of required accuracy for earmarking the vulnerable regions.It is recommended that one must choose the appropriate MCDA technique for the particular region because unnecessary complex structure for optimization process takes more time,efforts,resources,and implementation costs.
文摘Floods are phenomenon with significant socio-economic implications mainly for human loss, agriculture, livestock, soil loss and land degradation, for which many researchers try to identify the most appropriate methodologies by analyzing their temporal and spatial development. This study therefore attempts to employ the GIS-based multi-criteria decision analysis and analytical hierarchy process techniques to derive the flood risks management on rice productivity in the Gishari Agricultural Marshland in Rwamagana district, Rwanda. Here, six influencing potential factors to flooding, including river slope, soil texture, Land Use Land Cover through Land Sat 8, rainfall, river distance and Digital Elevation Model are considered for the delineation of flood risk zones. Data acquisition like Landsat 8 images, DEM, land use land cover, slope, and soil class in the study area were considered. Results showed that if the DEM is outdated or inaccurate due to changes in the terrain, such as construction, excavation, or erosion, the predicted flood patterns might not reflect the actual water flow. This could result unexpected flood extents and depths, potentially inundating rice fields that were not previously at risk and this, expectedly explained that the increase 1 m in elevation would reduce the rice productivity by 0.17% due to unplanned flood risks in marshland. It was found that the change in rainfall distribution in Gishari agricultural marshland would also decrease the rice productivity by 0.0018%, which is a sign that rainfall is a major factor of flooding in rice scheme. Rainfall distribution plays a crucial role in flooding analysis and can directly impact rice productivity. Oppositely, another causal factor was Land Use Land Cover (LULC), where the Multivariate Logistic Regression Model Analysis findings showed that the increase of one unit in Land Use Land Cover would increase rice productivity by 0.17% of the total rice productivity from the Gishari Agricultural Marshland. Based on findings from these techniques, the Gishari Agricultural Marshlands having steeped land with grassland is classified into five classes of flooding namely very low, low, moderate, high, and very high which include 430%, 361%, 292%, 223%, and 154%. Government of Rwanda and other implementing agencies and major key actors have to contribute on soil and water conservation strategies to reduce the runoff and soil erosion as major contributors of flooding.
文摘Financial performance analysis is of vital importance those involved in a business(e.g.,shareholders,creditors,partners,and company managers).An accurate and appropriate performance measurement is critical for decision-makers to achieve efficient results.Integrated performance measurement,by its nature,consists of multiple criteria with different levels of importance.Multiple Criteria Decision Analysis(MCDA)methods have become increasingly popular for solving complex problems,especially over the last two decades.There are different evaluation methodologies in the literature for selecting the most appropriate one among over 200 MCDA methods.This study comprehensively analyzed 41 companies traded on the Borsa Istanbul Corporate Governance Index for 10 quarters using SWARA,CRITIC,and SD integrated with eight different MCDA method algorithms to determine the position of Turkey's most transparent companies in terms of financial performance.In this study,we propose"stock returns"as a benchmark in comparing and evaluating MCDA methods.Moreover,we calculate the"rank reversal performance of MCDA methods".Finally,we performed a"standard deviation"analysis to identify the objective and characteristic trends for each method.Interestingly,all these innovative comparison procedures suggest that PROMETHEE II(preference ranking organization method for enrichment of evaluations II)and FUCA(Faire Un Choix Adéquat)are the most suitable MCDA methods.In other words,these methods produce a higher correlation with share price;they have fewer rank reversal problems,the distribution of scores they produce is wider,and the amount of information is higher.Thus,it can be said that these advantages make them preferable.The results show that this innovative methodological procedure based on'knowledge discovery'is verifiable,robust and efficient when choosing the MCDA method.
基金Scientific&Technological Research Council of Turkey(TUBI˙TAK)for providing financial support of this work under Grant No:106Y305.
文摘A municipal solid waste(MSW)management system needs solid waste management(SWM)techniques where the presence of a sanitary landfill is vital.One of the most important issues of sanitary landfilling is to locate the facility to an optimal location.Despite the versatility and case-dependent nature of conventional expert-based site selection procedures,the number of sites to be chosen increases with increased population forcing a number of constraints.Consequently,constraints and environmental regulations mechanically mask unsuitable areas,leaving very little areas to be assessed.This turns the situation into a challenging issue for a geographical information system(GIS)used with multicriteria decision analysis(MCDA),to select optimal site.The study aims to apply MCDA integrated with GIS to select possible sites of a MSW landfill with the same expert and same cognitive parameters while compared with the already present one.Results of this study revealed that conventional expert-based methods could not always evaluate all constraints at the same time and map reproduction is limited when parameter maps are changing rapidly in time.In order to produce cognitive and reproducible analyses,GIS with MCDA integration offers a good solution for site selection issue and forms a good alternative for conventional methods.
