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The monitoring of surface-water quality followed by water-quality modeling and analysis are essential for generating effective strategies in surface-water-resource management. However, worldwide, particularly in developing countries, water-quality studies are limited due to the lack of a complete and reliable dataset of surface-water-quality variables. In this context, several statistical and machine-learning models were assessed for imputing water-quality data at six monitoring stations located in the Santa Lucía Chico river (Uruguay), a mixed lotic and lentic river system. The challenge of this study is represented by the high percentage of missing data (between 50% and 70%) and the high temporal and spatial variability that characterizes the water-quality variables. The competing algorithms implement univariate and multivariate imputation methods (inverse distance weighting (IDW), Random Forest Regressor (RFR), Ridge (R), Bayesian Ridge (BR), AdaBoost (AB), Hubber Regressor (HR), Support Vector Regressor (SVR) and K-nearest neighbors Regressor (KNNR)). According to the results, more than 76% of the imputation outcomes are considered “satisfactory” (NSE > 0.45). The imputation performance shows better results at the monitoring stations located inside the reservoir than those positioned along the mainstream. IDW was the model with the best imputation results, followed by RFR, HR and SVR. The approach proposed in this study is expected to aid water-resource researchers and managers in augmenting water-quality datasets and overcoming the missing data issue to increase the number of future studies related to the water-quality matter.
Rafael Rodríguez; Marcos Pastorini; Lorena Etcheverry; Christian Chreties; Mónica Fossati; Alberto Castro; Angela Gorgoglione. Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach. Sustainability 2021, 13, 6318 .
AMA StyleRafael Rodríguez, Marcos Pastorini, Lorena Etcheverry, Christian Chreties, Mónica Fossati, Alberto Castro, Angela Gorgoglione. Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach. Sustainability. 2021; 13 (11):6318.
Chicago/Turabian StyleRafael Rodríguez; Marcos Pastorini; Lorena Etcheverry; Christian Chreties; Mónica Fossati; Alberto Castro; Angela Gorgoglione. 2021. "Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach." Sustainability 13, no. 11: 6318.
The monitoring of surface-water quality followed by water-quality modeling and analysis is essential for generating effective strategies in water-resource management. However, worldwide, particularly in developing countries, water-quality studies are limited due to the lack of a complete and reliable dataset of surface-water-quality variables. In this context, several statistical and machine-learning models were assessed for imputing water-quality data at six monitoring stations located in the Santa Lucía Chico river (Uruguay), a mixed lotic and lentic river system. The challenge of this study is represented by the high percentage of missing data (between 50% and 70%) and the high temporal and spatial variability that characterizes the water-quality variables. The competing algorithms implemented belonged to both univariate and multivariate imputation methods (inverse distance weighting (IDW), Random Forest Regressor (RFR), Ridge (R), Bayesian Ridge (BR), AdaBoost (AB), Hubber Regressor (HR), Support Vector Regressor (SVR), and K-nearest neighbors Regressor (KNNR)). According to the results, more than 76% of the imputation outcomes are considered satisfactory (NSE > 0.45). The imputation performance shows better results at the monitoring stations located inside the reservoir than the ones positioned along the mainstream. IDW was the most chosen model for data imputation.
Rafael Rodriguez; Marcos Pastorini; Lorena Etcheverry; Christian Chreties; Mónica Fossati; Alberto Castro; Angela Gorgoglione. Water-Quality Data Imputation With High Percentage of Missing Values: A Machine Learning Approach. 2021, 1 .
AMA StyleRafael Rodriguez, Marcos Pastorini, Lorena Etcheverry, Christian Chreties, Mónica Fossati, Alberto Castro, Angela Gorgoglione. Water-Quality Data Imputation With High Percentage of Missing Values: A Machine Learning Approach. . 2021; ():1.
Chicago/Turabian StyleRafael Rodriguez; Marcos Pastorini; Lorena Etcheverry; Christian Chreties; Mónica Fossati; Alberto Castro; Angela Gorgoglione. 2021. "Water-Quality Data Imputation With High Percentage of Missing Values: A Machine Learning Approach." , no. : 1.
Urban stormwater runoff represents a significant challenge for the practical assessment of diffuse pollution sources on receiving water bodies. Given the high dimensionality of the problem, the main goal of this study was the comparison of linear and non-linear machine learning (ML) methods to characterize urban nutrient runoff from impervious surfaces. In particular, the principal component analysis (PCA) for the linear technique and the self-organizing map (SOM) for the non-linear technique were chosen and compared considering the high number of successful applications in the water quality field. To strengthen this comparison, these techniques were supported by well-known linear and non-linear methods. Those techniques were applied to a complete dataset with precipitation, flow rate, and water quality (sediments and nutrients) records of 577 events gathered for a watershed located in Southern Italy. According to the results, both linear and non-linear techniques can represent build-up and wash-off, the two main processes that characterize urban nutrient runoff. In particular, non-linear methods are able to capture and represent better the rainfall-runoff process and the transport of dissolved nutrients in urban runoff (dilution process). However, their computational time is higher than the linear technique (0.0054 s vs. 15.24 s, for linear and non-linear, respectively, in our study). The outcomes of this study provide significant insights into the application of ML methods for the water quality field.
Angela Gorgoglione; Alberto Castro; Vito Iacobellis; Andrea Gioia. A Comparison of Linear and Non-Linear Machine Learning Techniques (PCA and SOM) for Characterizing Urban Nutrient Runoff. Sustainability 2021, 13, 2054 .
AMA StyleAngela Gorgoglione, Alberto Castro, Vito Iacobellis, Andrea Gioia. A Comparison of Linear and Non-Linear Machine Learning Techniques (PCA and SOM) for Characterizing Urban Nutrient Runoff. Sustainability. 2021; 13 (4):2054.
Chicago/Turabian StyleAngela Gorgoglione; Alberto Castro; Vito Iacobellis; Andrea Gioia. 2021. "A Comparison of Linear and Non-Linear Machine Learning Techniques (PCA and SOM) for Characterizing Urban Nutrient Runoff." Sustainability 13, no. 4: 2054.
To the best of our knowledge, this paper presents the first Internet Domain Name System (DNS) queries data study from a national K-12 Education Service Provider. This provider, called Plan Ceibal, supports a one-to-one computing program in Uruguay. Additionally, it has deployed an Information and Communications Technology (ICT) infrastructure in all of Uruguay’s public schools and high-schools, in addition to many public spaces. The main development is wireless connectivity, which allows all the students (whose ages range between 6 and 18 years old) to connect to different resources, including Internet access. In this article, we use 9,125,888,714 DNS-query records, collected from March to May 2019, to study Plan Ceibal user’s Internet behavior applying unsupervised machine learning techniques. Firstly, we conducted a statistical analysis aiming at depicting the distribution of the data. Then, to understand users’ Internet behavior, we performed principal component analysis (PCA) and clustering methods. The results show that Internet use behavior is influenced by age-group and time of the day. However, it is independent of the geographical location of the users. Internet use behavior analysis is of paramount importance for evidence-based decision making by any education network provider, not only from the network-operator perspective but also for providing crucial information for learning analytics purposes.
Alexis Arriola; Marcos Pastorini; Germán Capdehourat; Eduardo Grampín; Alberto Castro. Large-Scale Internet User Behavior Analysis of a Nationwide K-12 Education Network Based on DNS Queries. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 12249, 776 -791.
AMA StyleAlexis Arriola, Marcos Pastorini, Germán Capdehourat, Eduardo Grampín, Alberto Castro. Large-Scale Internet User Behavior Analysis of a Nationwide K-12 Education Network Based on DNS Queries. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; 12249 ():776-791.
Chicago/Turabian StyleAlexis Arriola; Marcos Pastorini; Germán Capdehourat; Eduardo Grampín; Alberto Castro. 2020. "Large-Scale Internet User Behavior Analysis of a Nationwide K-12 Education Network Based on DNS Queries." Transactions on Petri Nets and Other Models of Concurrency XV 12249, no. : 776-791.
Urban stormwater runoff is considered worldwide as one of the most critical diffuse pollutions since it transports contaminants that threaten the quality of receiving water bodies and represent a harm to the aquatic ecosystem. Therefore, a thorough analysis of nutrient build-up and wash-off from impervious surfaces is crucial for effective stormwater-treatment design. In this study, the self-organizing map (SOM) method was used to simplify a complex dataset that contains precipitation, flow rate, and water-quality data, and identify possible patterns among these variables that help to explain the main features that impact the processes of nutrient build-up and wash-off from urban areas. Antecedent dry weather, among the rainfall-related characteristics, and sediment transport resulted in being the most significant factors in nutrient urban runoff simulations. The outcomes of this work will contribute to facilitating informed decision making in the design of management strategies to reduce pollution impacts on receiving waters and, consequently, protect the surrounding ecological environment.
Angela Gorgoglione; Alberto Castro; Andrea Gioia; Vito Iacobellis. Application of the Self-organizing Map (SOM) to Characterize Nutrient Urban Runoff. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 12252, 680 -692.
AMA StyleAngela Gorgoglione, Alberto Castro, Andrea Gioia, Vito Iacobellis. Application of the Self-organizing Map (SOM) to Characterize Nutrient Urban Runoff. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; 12252 ():680-692.
Chicago/Turabian StyleAngela Gorgoglione; Alberto Castro; Andrea Gioia; Vito Iacobellis. 2020. "Application of the Self-organizing Map (SOM) to Characterize Nutrient Urban Runoff." Transactions on Petri Nets and Other Models of Concurrency XV 12252, no. : 680-692.
The Data Scarcity problem is repeatedly encountered in environmental research. This may induce an inadequate representation of the response’s complexity in any environmental system to any input/change (natural and human-induced). In such a case, before getting engaged with new expensive studies to gather and analyze additional data, it is reasonable first to understand what enhancement in estimates of system performance would result if all the available data could be well exploited. The purpose of this Special Issue, “Overcoming Data Scarcity in Earth Science” in the Data journal, is to draw attention to the body of knowledge that leads at improving the capacity of exploiting the available data to better represent, understand, predict, and manage the behavior of environmental systems at meaningful space-time scales. This Special Issue contains six publications (three research articles, one review, and two data descriptors) covering a wide range of environmental fields: geophysics, meteorology/climatology, ecology, water quality, and hydrology.
Angela Gorgoglione; Alberto Castro; Christian Chreties; Lorena Etcheverry. Overcoming Data Scarcity in Earth Science. Data 2020, 5, 5 .
AMA StyleAngela Gorgoglione, Alberto Castro, Christian Chreties, Lorena Etcheverry. Overcoming Data Scarcity in Earth Science. Data. 2020; 5 (1):5.
Chicago/Turabian StyleAngela Gorgoglione; Alberto Castro; Christian Chreties; Lorena Etcheverry. 2020. "Overcoming Data Scarcity in Earth Science." Data 5, no. 1: 5.
Many carriers and service providers (SPs) use MPLS to achieve traffic engineering (TE) objectives, such as network resources optimization, support for end-to-end QoS guarantees services, aggregated traffic measurement and fast recovery against link/node/shared risk link group (SRLG) failures. The three main traffic engineering components of MPLS are the routing component, responsible for the discovery of the network topology, the path computation component, responsible for the assignment of resources to traffic demands, and the signaling component, responsible for the establishment of the label switched paths (LSPs) along the computed path. Path computation under QoS and administrative constraints is usually performed sub-optimally in network nodes, which are mainly dedicated to traffic forwarding. Off-load of this task to specialized network entities is a key aspect of the path computation element (PCE) architecture. This paper describes an ongoing implementation of such architecture seeking for cooperation of control and management plane components for overall network operation and management optimization. Early functional testing of the software components, performed in cooperation with a service provider, is presented.
E. Grampin; A. Castro; M. German; F. Rodríguez; G. Tejera; M. Sanguinetti. A PCE-based Connectivity Provisioning Management Framework. 2007 Latin American Network Operations and Management Symposium 2007, 76 -83.
AMA StyleE. Grampin, A. Castro, M. German, F. Rodríguez, G. Tejera, M. Sanguinetti. A PCE-based Connectivity Provisioning Management Framework. 2007 Latin American Network Operations and Management Symposium. 2007; ():76-83.
Chicago/Turabian StyleE. Grampin; A. Castro; M. German; F. Rodríguez; G. Tejera; M. Sanguinetti. 2007. "A PCE-based Connectivity Provisioning Management Framework." 2007 Latin American Network Operations and Management Symposium , no. : 76-83.