Lake performance differences in response to land use and water quality: Data envelopment analysis

The extent of anthropogenic land use in watersheds determines the amount of pollutants discharged to streams. This indirectly and directly affects stream water quality and biological health. Most studies have therefore focused on ways to reduce non-point pollution sources to streams from the surrounding land use in watersheds. However, the mechanistic pathways between land use and the deterioration of stream water quality and biological assemblages remain unclear. This study estimated a structural equation model (SEM) representing the impact of agricultural and urban land use on water quality and the benthic macroinvertebrate index (BMI) using IBM AMOS in the Nam-Han river systems, South Korea. The estimated SEM showed that the percent of urban and agricultural land in the watersheds significantly affected both the water quality and the BMI of the streams. Specifically, a higher percent of urban land use had directly increased the biochemical oxygen demand (BOD) and total phosphorus (TP), and deteriorated the BMI of streams. Similarly, higher proportions of agricultural land use had also directly increased the BOD, total nitrogen (TN), and total phosphorus (TP) concentrations, and lowered the BMI of streams. In addition, it was observed that the percent of urban and agricultural land use had indirectly deteriorated the BMI through increased BOD. However, we were not able to observe any significant indirect effect of the percent of urban and agricultural land use through increased nutrients including TN and TP. These results indicate that increased urban and agricultural land use in the watersheds had directly and indirectly affected the physicochemical characteristics and benthic macroinvertebrate communities in streams. Our findings emphasize the need to develop more elaborate environmental management and restoration strategies to improve the water quality and biological status of streams.

It has become a more and more concerned issue of optimising the scientific & technical (S&T) resources configuration to improve its input-output efficiency. By applying data envelopment analysis (DEA) model, the thesis evaluates the S&T resources input-output efficiency of 45 major enterprises and 8 leading industries of region A. The result of data analysis shows, over half enterprises didn't reach S&T input-output efficiency in a DEA analysis, and 2 of the 8 leading industries didn't reach efficiency. The thesis analyzes the reasons of inefficiency enterprises and industries, as well bring forward the improving strategy based on the evaluation result accordingly.

We analyzed macroinvertebrate community composition from 19 sites, spanning 5 vegetation types, in the White River drowned river mouth wetland (eastern shore of Lake Michigan). Sites were distributed along a gradient of anthropogenic disturbance from the less-impacted upper wetland to the more-impacted lower wetland. The relative importance of surrounding land use and cover, water quality, and vegetation type in structuring macroinvertebrate communities was evaluated using correspondence analysis (CA) of biotic data, and principle components analysis (PCA) of chemical/physical water quality data. A significant correlation (p = 0.001) between site scores of the CA and PCA showed that ambient chemical/physical water quality was likely an important driver of macroinvertebrate community composition. Canonical correspondence analysis (CCA) was then used to corroborate this relationship. Vegetation type, did not appear to structure macroinvertebrate communities to a significant degree. Both water quality and macroinvertebrate community composition correlated with surrounding land use and cover. Therefore, we hypothesize that macroinvertebrate communities in the White River drowned river mouth wetland respond to surrounding land uses via its influence on the abiotic environment. This response occurred even at the within-system scale used in this study. Practitioners of bioassessment protocols should, therefore, be aware of spatial heterogeneity in both water quality and macroinvertebrate community composition in Great Lakes drowned river mouth wetlands.

In this study, the irrigation water quality of Eryaman region in Ankara (Turkey) has been investigated using a Geographic Information System-Multicriteria Decision Analysis (GIS-MCDA)-based model. Two different irrigation water quality indices (IWQI-A and IWQI-B) based on Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA) have been developed to provide a single suitability score for all criteria of irrigation water quality. The irrigation water quality indices were composed of 3 main criteria and 11 sub-criteria. Based on irrigation water quality indices, four suitability classes have been identified as (a) excellent, (b) good, (c) permissible, and (d) unsuitable. Irrigation water quality maps were created by using kriging method and the water quality index scores. The temporal variation of irrigation water quality was evaluated using the models created for July 2007, 2010, and 2015 years. The most suitable areas for irrigation water are concentrated in the eastern and western parts of the region where the IWQI-B scores were >0.85. In recent years, the deterioration in irrigation water quality has increased in the southern parts of the basin. This research has demonstrated that the indices created by using DEA and AHP methods are reliable indicators to assess irrigation water quality. PRACTITIONER POINTS: Water quality indices can be used to depict the overall water quality status in water management planning. To avoid problems when modeling groundwater quality, the factors that have the most effect on water quality should be selected. Analytic Hierarchy Process and Data Envelopment Analysis techniques are efficient and reliable methods to determine water quality.

Using a Decision Analysis (DA) model for decision-making under partial probability information, we derive the basic Data Envelopment Analysis (DEA) models. The DA model has two components: the choice of a strategy and the occurrence of events. There is an outcome associated with each strategy–event combination. A Linear Programming (LP) model is formulated to reveal the strategies that are called efficient for given event probability ranges. Viewing the strategies as entities and the events as activity categories with outcomes that may have positive or negative effects on the entities' performance, the LP model can be configured for gauging the entities' productive performances. One particular configuration of the LP model called the LP-DM leads to the basic models of DEA. The LP-DM proffers a new starting point for developing methods that gauge productive performance and a different perspective that reiterates the caution that must be exercised in the application of DEA and use of its results for policy making.

Data envelopment analysis (DEA) is a mathematical method based on production theory and the principles of linear programming. It enables one to assess how efficiently a firm, organization, agency, or such other unit uses the resources available (inputs) to generate a set of outputs relative to other units in the data set. Recent papers by different writers present different applications of DEA in the transportation engineering domain. All of these papers are published in transportation journals. These papers are mainly aimed at addressing the transportation-related issues and thus do not focus too much on the DEA concept itself. It can be asserted that DEA is very likely to be used more and more in the transportation engineering domain. Given this, there is a need for the transportation professionals to fully understand the DEA concept. It is essential for such a community to identify cases where the application of this innovative and powerful method can be useful to help the decision-making process, to accurately apply DEA in a particular setting, to derive meaningful conclusions from the obtained results, and to acknowledge the limitations of DEA in certain cases so as to approach the results with caution. The purpose of this paper is to illustrate to the civil engineering, more specifically to the transportation engineering community the use of this powerful approach in performing comparative performance measurement. Within this context, this paper will address a transportation-related problem by using the DEA approach. Different from the other papers containing transportation-related DEA applications (as mentioned above), this paper will discuss, in detail, the steps that need to be taken to generate the DEA model and solve it.

The appreciative democratic voice of DEA: A case of faculty academic performance evaluation

Nested dynamic network data envelopment analysis models with infinitely many decision making units for portfolio evaluation

Emerging application of robust data envelopment analysis for optimization of graft copolymerization of poly(2-hydroxyethyl methacrylate) to Tamarindus indica seed polysaccharide

The deterioration of water quality has become a primary environmental concern worldwide. Understanding the status of water quality and identifying the influencing factors are important for water resources management. However, reported analyses have mostly been conducted in small and focused areas. It is still unclear if factors driving spatial variation in water quality would be different in extended spatial scales. In this paper, we analyzed spatial pattern of inland surface water quality in China using a dataset with four water quality parameters (i.e., pH, DO, NH4+-N and CODMn) and the water quality level. We tested the effects of anthropogenic (i.e., land use and socio-economic) and natural (i.e., climatic and topographic) factors on spatial variation in water quality. The study concluded that the overall inland surface water quality in China was at level III (fair). Water quality level was strongly correlated with CODMn and NH4+-N concentration. In contrast to reported studies that suggested land use patterns were the determinants of inland surface water quality, this study revealed that both anthropogenic and natural factors played important roles in explaining spatial variation of inland surface water quality in China. Among the tested explanatory variables, mean elevation within watershed appeared as the best predictor for pH, while annual precipitation and mean air temperature were the most important explanatory variables for CODMn and DO, respectively. NH4+-N concentration and water quality level were most strongly correlated with the percent of forest cover in watershed. Compared to studies at smaller spatial scales, this study found different influencing factors of surface water quality, suggesting that factors may play different roles at different spatial scales of consideration. Therefore management policies and measures in water quality control must be established and implemented accordingly. Since currently adopted parameters for monitoring of inland surface water quality in China are largely influenced by natural variables, additional physicochemical and biological indicators are needed for a robust assessment of human impacts on water quality.

Data Envelopment Analysis (DEA) is a nonparametric optimization technique that evaluates the relative efficiency of decision-making units and is used in this thesis as an empirical estimator of credit rating. The purpose of this research is to combine different DEA models and technique and obtain the best model that captures different aspects of credit risk. Various models are evaluated by combining four Slack DEA models with Principal Component Analysis (PCA), Absolute Weights Restriction, and Stochastic DEA. We found that Goal Vector Approach Stochastic PCA (SGV+PCA), applied to a sample consisting of five sectors, is the best model. SGV+PCA DEA model obtains a high correlation with Standard & Poor’s (S&P) credit rating and with Market Price; it also classified twelve bankrupted companies within the 17% of the less efficient companies in the sample, suggesting that the model is a good financial health estimator and is a potential tool for credit rating analysis.

Objective: To synthesize the current evidence base concerning the application of Data Envelopment Analysis (DEA) in healthcare efficiency during the COVID-19 pandemic using a scoping review of 13 primary studies. Methods: We consulted databases including Web of Science (WoS) and Scopus, as well as manual search entries up to September 2022. Included studies were primary applications of DEA for assessing healthcare efficiency during the COVID-19 pandemic. Key findings derived from thematic analysis of repeating pattern observations were extracted and tabulated for further synthesis, taking into consideration the variations in DMU definitions, the inclusion of undesirable outputs, the influence of external factors, and the infusion of advanced technologies in DEA. Results: The review observed a diverse application of DMUs, ranging from healthcare supply chains to entire national health systems. There was an evident shift towards incorporating undesirable outputs, such as mortality rates, in the DEA models amidst the pandemic. The influence of external and non-discretionary factors became more pronounced in DEA applications, highlighting the interconnected nature of global health challenges. Notably, several studies integrated advanced computational methods, including machine learning, into traditional DEA, paving the way for enhanced analytical capabilities. Conclusions: DEA, as an efficiency analysis tool, has exhibited adaptability and evolution in its application in the context of the COVID-19 healthcare crisis. By recognizing the multifaceted challenges posed by the pandemic, DEA applications have grown more comprehensive, integrating broader societal and health outcomes. This review provides pivotal insights that can inform policy and healthcare strategies, underscoring the importance of dynamic and comprehensive efficiency analysis methodologies during global health emergencies.

Land use land cover within a watershed influences stream water quality, habitat quality, and biological community structure. As development and associated impervious surface increases in a watershed, changes in storm water and nutrient inputs generally cause declines in habitat conditions and biodiversity. The first goal of our study was to evaluate the water quality in the Charles River watershed, in which our objective (G1O1) was to establish ten 100-meter reach-scale sampling stations and conduct physical, chemical, and biological assessments. The second goal of this study was to better understand the direct and indirect effects of hierarchical variables on water quality in the Charles River watershed. Our first objective of our second goal (G2O1) was to calculate land use land cover percentages at the pour-point subwatershed and local 100-meter buffer scale for each of our ten 100-meter reach sampling stations. Our second objective of our second goal (G2O2) was to use path analysis to determine the direct and indirect effects of land use land cover and impervious surface on water quality in the Charles River watershed. The results of G1O1 were that habitat quality assessments ranged from “marginal” to “optimal” and biological quality assessments ranged from “fair” to “good“, indicating overall “fair” or better water quality conditions in the watershed. The results of G2O2 were that our path analysis resulted in differences in effects of development between the buffer and sub-watershed scale. At the buffer scale, water quality was influenced more negatively by the percentage of developed land area versus the percentage of impervious cover. While both buffer development and habitat quality had a direct effect on Streamside Biosurvey Macroinvertebrates, buffer development also directly hindered habitat quality, thus having an indirect effect on Streamside Biosurvey Macroinvertebrates through habitat. Streamside Biosurvey Macroinvertebrate scores were shown to be more sensitive to development within the buffer versus at the sub-watershed scale, where impervious cover was a more important indicator of stream water quality. Through this small case study of 10 stations within the Charles River watershed, we illustrated how citizen-science level water quality assessments can be combined with water chemistry and hierarchical LULC data to provide insights into potential direct and indirect effects on water quality. As the fields of landscape ecology and conservation continue to grow, so does our ability to determine changes in land development and devise management strategies aimed at improving water quality.

Data envelopment analysis (DEA) is a non-parametric technique for measuring the efficiency of decision making units (DMU) with exact input and output terms. So far, a number of DEA models with interval data have been developed. The radial DEA model such as CCR or BCC model with interval data are well known as basic DEA models with interval data. However, these radial DEA models have some defects. In this study, we develop a new approach based upon the slacks-based measurement (SBM) of super-efficiency model for dealing with interval data in DEA. The SBM super-efficiency DEA model can not only discriminate the performance of efficient DMUs from inefficient ones, but can distinguish between the efficient DMUs. In addition, this model can overcome the drawback that the super-efficiency model can be infeasible under variable returns to scale (VRS) assumption. An illustrative example is provided to verify the idea of this paper and show its potential application and validity.

Integrating Land Use and Water Quality for Environmental based Land Use Planning for U-tapao River Basin, Thailand