Compared to wavelength switched optical networks (WSON), flexgrid optical networks provide higher spectrum efficiency and flexibility. To properly analyze, design, plan, and operate flexgrid networks, the routing and spectrum allocation (RSA) problem must be solved. The RSA problem involves two different constraints: the continuity constraint to ensure that the allocated spectral resources are the same along the links in the route and the contiguity constraint to guarantee that those resources are contiguous in the spectrum. As a consequence of its complexity, it is crucial that efficient methods are available to allow solving realistic problem instances in practical times. In this paper, we review different RSA-related optimization problems that arise within the life-cycle of flexgrid networks. Different methods to solve those optimization problems are reviewed along with the different requirements related to where those problems appear. Starting from its formulation, we analyze network life-cycle and indicate different solving methods for the kind of problems that arise at each network phase: from off-line to in-operation network planning. We tackle two representative use cases: i) a use case for off-line planning where a flexgrid network is designed and periodically upgraded, and ii) multilayer restoration as a use case for in-operation planning. Three solving methods are proposed for the off-line planning problem: mathematical programming, column generation and metaheuristics, whereas, as a result of its stringent required solving times, two heuristic methods are presented for the on-line problem.

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.

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.

The new flexgrid technology, in opposition to the fixed grid one traditionally used in wavelength switched optical networks (WSON), allows allocating the spectral bandwidth needed to convey heterogeneous client demand bitrates in a flexible manner so that the optical spectrum can be managed much more efficiently. In this paper we propose a new recovery scheme, called single-path provisioning multi-path recovery (SPP-MPR), specifically designed for flexgrid-based optical networks. It provisions single-paths to serve the bitrate requested by client demands and combines protection and restoration schemes to jointly recover, in part or totally, that bitrate in case of failure. We define the bitrate squeezed recovery optimization (BRASERO) problem to maximize the bitrate which is recovered in case of failure of any single fiber link. A mixed integer linear programming (MILP) formulation is provided. Exhaustive numerical experiments carried out over two network topologies and realistic traffic scenarios show that the efficiency of the proposed SPP-MPR scheme approaches that of restoration mechanisms while providing recovery times as short as protection schemes.