Aggregated outputs by linear models: An application on marine litter beaching prediction

The use of artificial intelligence algorithms to detect macroplastics in aquatic environments: A critical review

The presence of macroplastic (MP) is having serious consequences on natural ecosystems, directly affecting biota and human wellbeing. Given this scenario, estimating MPs' abundance is crucial for assessing the issue and formulating effective waste management strategies. In this context, the main objective of this critical review is to analyze the use of machine learning (ML) techniques, with a particular interest in deep learning (DL) approaches, to detect, classify and quantify MPs in aquatic environments, supported by datasets such as satellite or aerial images and video recordings taken by unmanned aerial vehicles. This article provides a concise overview of artificial intelligence concepts, followed by a bibliometric analysis and a critical review. The search methodology aimed to categorize the scientific contributions through temporal and spatial criteria for bibliometric analysis, whereas the critical review was based on generating homogeneous groups according to the complexity of ML and DL methods, as well as the type of dataset. In light of the review carried out, classical ML techniques, such as random forest or support vector machines, showed robustness in MPs detection. However, it seems that achieving optimal efficiencies in multiclass classification is a limitation for these methods. Consequently, more advanced techniques such as DL approaches are taking the lead for the detection and multiclass classification of MPs. A series of architectures based on convolutional neural networks, and the use of complex pre-trained models through the transfer learning, are currently being explored (e.g., VGG16 and YOLO models), although currently the computational expense is high due to the need for processing large volumes of data. Additionally, there seems to be a trend towards detecting smaller plastic, which need higher resolution images. Finally, it is important to stress that since 2020 there has been a significant increase in scientific research focusing on transformer-based architectures for object detection. Although this can be considered the current state of the art, no studies have been identified that utilize these architectures for MP detection.

Analysis and forecasting of national marine litter based on coastal data in South Korea from 2009 to 2021

In this study, statistical analysis and forecasting were performed using coastal litter data of Korea. The analysis indicated that rope and vinyl accounted for the highest proportion of coastal litter items. The statistical analysis of the national coastal litter trends revealed that the greatest concentration of litter was observed during summer months (June-August). To predict the amount of coastal litter per meter, recurrent neural network (RNN)-based models were used. Neural basis expansion analysis for interpretable time series forecasting (N-BEATS) and neural hierarchical interpolation for time series forecasting (N-HiTS), an improved model of N-BEATS recently announced, were used for comparison with RNN-based models. When predictive performance and trend followability were evaluated, overall N-BEATS and N-HiTS outperformed RNN-based models. Furthermore, we found that average of N-BEATS and N-HiTS models yielded better results than using one model.

A global gridded municipal water withdrawal estimation method using aggregated data and artificial neural network

Municipal water withdrawal (MWW) information is of great significance for water supply planning, including water supply pipeline networks planning, optimization and management. Currently most MWW data are reported as spatially aggregated over large-area survey regions or even lack of data, which is unable to meet the growing demand for spatially detailed data in many applications. In this paper, six different models are constructed and evaluated in estimating global MWW using aggregated MWW data and gridded raster covariates. Among the models, the artificial neural network-based indirect model (NN_M) shows the best accuracy with higher R² and lower NMAE and NRMSE in different spatial scales. The estimates achieved from the NNM model are consistent with census and survey data, and outperforms the existing global gridded MWW dataset. At last, the NNM model is applied to mapping global gridded MWW for the year 2015 at 0.1 × 0.1° resolution. The proposed method can be applied to a wider aggregated output learning problem and the high-resolution global gridded MWW data can be used in hydrological models and water resources management.

Sources, sinks and transformations of plastics in our oceans: Review, management strategies and modelling

Currently, 60-80 % of litter is plastic, and almost 10 % ends up in the ocean directly or indirectly. Plastics often suffer from photooxidation producing microplastics and these microplastics derived from the breakdown of larger plastics are called secondary microplastics. These compounds simply cannot be extracted from the oceans, and once mixed, they enter the food chain and may have toxic effects. This work reviews the current existing information on the topic in the scientific literature. Then, the current plastic management strategies in the marine environment are analysed, with the objective of identifying possible needs and improvements from a sustainable point of view, and to define new approaches. Simultaneously, a material flows analysis in different media of the marine environment is carried out using system dynamics. A preliminary model of plastics mobilization into the ocean to other media of the marine environment (like sediments and biota) is developed and validated with the existing data from the previous steps of the work. This work expands the current knowledge on the plastics management, their transformations and accumulation in the marine environment and the harmful effects on it. Likewise, preliminary dynamic model of mobilization of plastics in the ocean is implemented, run, and validated. The developed model can be used to predict trends in the distribution of the plastics in the ocean with time. In addition, the most important reservoirs of plastics in the ocean can be observed. Although plastics undergo transformations in the marine environment, it is not a means of disposal since most of them are non-biodegradable. Most plastics accumulate on the seabed. The proportion of microplastics found in sediments is higher than that of macroplastics.