A machine learning model to link ecological response and anthropogenic stressors: a tool for water management in the Tagus River Basin (Spain)

2020 ◽  
Author(s):  
Carlotta Valerio ◽  
Alberto Garrido ◽  
Gonzalo Martinez-Muñoz ◽  
Lucia De Stefano
Keyword(s):  
Machine Learning ◽  
Riparian Forest ◽  
Ecological Status ◽  
Freshwater Ecosystems ◽  
Water Bodies ◽  
Nutrient Concentrations ◽  
Machine Learning Model ◽  
Tagus River ◽  
Good Ecological Status

<p>Freshwater ecosystems are threatened by multiple anthropic pressures. Understanding the effect of pressures on the ecological status is essential for the design of effective policy measures but can be challenging from a methodological point of view. In this study we propose to capture these complex relations by means of a machine learning model that predicts the ecological response of surface water bodies to several anthropic stressors. The model was applied to the Spanish stretch of the Tagus River Basin. The performance of two machine learning algorithms -Random Forest (RF) and Boosted Regression Trees (BRT) - was compared. The response variables in the model were the biotic quality indices of macroinvertebrates (Iberian Biomonitoring Working Party) and diatoms (Indice de Polluosensibilité Spécifique). The stressors used as explanatory variables belong to the following categories: physicochemical water quality, land use, alteration of the hydrological regime and hydromorphological degradation. Variables describing the natural environmental variability were also included. According to the coefficient of determination, the root mean square error and the mean absolute error, the RF algorithm has the best explanatory power for both biotic indices. The categories of land cover in the upstream catchment area, the nutrient concentrations and the elevation of the water body are ranked as the main features at play in determining the quality of biological communities. Among the hydromorphological elements, the alteration of the riparian forest (expressed by the Riparian Forest Quality Index) is the most relevant feature, while the hydrological alteration does not seem to influence significantly the value of the biotic indices. Our model was used to identify potential policy measures aimed at improving the biological quality of surface water bodies in the most critical areas of the basin. Specifically, the biotic quality indices were modelled imposing the maximum concentration of nutrients that the Spanish legislation prescribes to ensure a good ecological status. According to our model, the nutrient thresholds set by the Spanish legislation are insufficient to ensure values of biological indicators consistent with the good ecological status in the entire basin. We tested several scenarios of more restrictive nutrient concentrations and values of hydromorphological quality to explore the conditions required to achieve the good ecological status. The predicted percentage of water bodies in good status increases when a high  Riparian Forest Quality Index is set, confirming the importance of combining physico-chemical and hydromorphological improvements in order to ameliorate the status of freshwater ecosystems. </p>

scholarly journals Influence of Anthropogenic Loads on Surface Water Status: A Case Study in Lithuania

2021 ◽  
Vol 13 (8) ◽  
pp. 4341
Author(s):  
Laima Česonienė ◽  
Daiva Šileikienė ◽  
Vitas Marozas ◽  
Laura Čiteikė
Keyword(s):  
Total Nitrogen ◽  
Water Status ◽  
Arable Land ◽  
Ecological Status ◽  
Water Bodies ◽  
Agricultural Pollution ◽  
Good Ecological Status ◽  
Water Catchment ◽  
Lake Basins

Twenty-six water bodies and 10 ponds were selected for this research. Anthropogenic loads were assessed according to pollution sources in individual water catchment basins. It was determined that 50% of the tested water bodies had Ntotal values that did not correspond to the good and very good ecological status classes, and 20% of the tested water bodies had Ptotal values that did not correspond to the good and very good ecological status classes. The lake basins and ponds received the largest amounts of pollution from agricultural sources with total nitrogen at 1554.13 t/year and phosphorus at 1.94 t/year, and from meadows and pastures with total nitrogen at 9.50 t/year and phosphorus at 0.20 t/year. The highest annual load of total nitrogen for lake basins on average per year was from agricultural pollution from arable land (98.85%), and the highest total phosphorus load was also from agricultural pollution from arable land (60%).

Machine Learning-Based Subjective Quality Estimation for Video Streaming Over Wireless Networks

2019 ◽  
pp. 235-254
Author(s):  
Monalisa Ghosh ◽  
Chetna Singhal
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Video Streaming ◽  
Video Transmission ◽  
Video Quality ◽  
Subjective Quality ◽  
Perceptual Quality ◽  
Support Vector ◽  
Machine Learning Model

Video streaming services top the internet traffic surging forward a competitive environment to impart best quality of experience (QoE) to the users. The standard codecs utilized in video transmission systems eliminate the spatiotemporal redundancies in order to decrease the bandwidth requirement. This may adversely affect the perceptual quality of videos. To rate a video quality both subjective and objective parameters can be used. So, it is essential to construct frameworks which will measure integrity of video just like humans. This chapter focuses on application of machine learning to evaluate the QoE without requiring human efforts with higher accuracy of 86% and 91% employing the linear and support vector regression respectively. Machine learning model is developed to forecast the subjective quality of H.264 videos obtained after streaming through wireless networks from the subjective scores.

Freshwater nematodes as bioindicators in field studies - the NemaSPEAR[%]-index.

2021 ◽  
pp. 323-340
Author(s):  
Sebastian Höss ◽  
Walter Traunspurger
Keyword(s):  
Ecological Status ◽  
Nematode Species ◽  
Field Studies ◽  
Freshwater Ecosystems ◽  
Freshwater Nematodes ◽  
Chemically Induced ◽  
Freshwater Habitats ◽  
Induced Changes ◽  
Quality Assessments

Abstract This chapter, after a general introduction to quality assessments of freshwater habitats, reviews the use of freshwater nematodes as in situ bioindicators, including in monitoring the ecological quality of freshwater habitats. By drawing on studies of nematode communities in unpolluted and polluted habitats as examples, it highlights both the different methods used to assess the quality of freshwater ecosystems and their applications. A focus of the chapter is the development of a new index that uses freshwater nematodes to assess chemically induced changes in the ecological status of freshwater habitats, the NemaSPEAR[%]-index (Nematode SPEcies At Risk).

scholarly journals Combine Clustering and Machine Learning for Enhancing the Efficiency of Energy Baseline of Chiller System

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4368 ◽  
Author(s):  
Chun-Wei Chen ◽  
Chun-Chang Li ◽  
Chen-Yu Lin
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Learning Models ◽  
Important Method ◽  
Gap Statistic ◽  
Machine Learning Model ◽  
Key Variables

Energy baseline is an important method for measuring the energy-saving benefits of chiller system, and the benefits can be calculated by comparing prediction models and actual results. Currently, machine learning is often adopted as a prediction model for energy baselines. Common models include regression, ensemble learning, and deep learning models. In this study, we first reviewed several machine learning algorithms, which were used to establish prediction models. Then, the concept of clustering to preprocess chiller data was adopted. Data mining, K-means clustering, and gap statistic were used to successfully identify the critical variables to cluster chiller modes. Applying these key variables effectively enhanced the quality of the chiller data, and combining the clustering results and the machine learning model effectively improved the prediction accuracy of the model and the reliability of the energy baselines.

scholarly journals The Importance of Riparian Forest Cover to the Ecological Status of Agricultural Streams in a Nationwide Assessment

2021 ◽  
Author(s):  
Mikko Tolkkinen ◽  
Saku Vaarala ◽  
Jukka Aroviita
Keyword(s):  
Land Use ◽  
Forest Cover ◽  
Riparian Forest ◽  
Ecological Status ◽  
Freshwater Ecosystems ◽  
Agricultural Landscapes ◽  
Riparian Forests ◽  
Independent Effect ◽  
Medium Size ◽  
Positive Effect

AbstractForested riparian corridors are a key management solution for halting the global trend of declining ecological status of freshwater ecosystems. There is an increasing body of evidence related to the efficacy of these corridors at the local scale, but knowledge is inadequate concerning the effectiveness of riparian forests in terms of protecting streams from harmful impacts across larger scales. In this study, nationwide assessment results comprising more than 900 river water bodies in Finland were used to examine the importance of adjacent land use to river ecological status estimates. Random forest models and partial dependence functions were used to quantify the independent effect of adjacent land use on river ecological status after accounting for the effects of other factors. The proportion of adjacent forested land along a river had the strongest independent positive effect on ecological status for small to medium size rivers that were in agricultural landscapes. Ecological quality increased by almost one status class when the adjacent forest cover increased from 10 to 60%. In contrast, for large rivers, adjacent forested land did not show an independent positive effect on ecological status. This study has major implications for managing river basins to achieve the EU Water Framework Directive (WFD) goal of obtaining good ecological status of rivers. The results from the nationwide assessment demonstrate that forested riparian zones can have an independent positive effect on the ecological status of rivers, indicating the importance of riparian forests in mitigating the impacts of catchment-level stressors. Therefore, forested buffer zones should be more strongly considered as part of river basin management.

scholarly journals El estado ecológico de las pequeñas cuencas de cabecera en las serranías béticas húmedas (parque natural Los Alcornocales, sur de España) según la Directiva Marco del Agua: ¿su aplicación garantiza la conservación?

2015 ◽  
Vol 38 (1) ◽  
pp. 59-69
Author(s):  
A. Ruiz–García ◽  
◽  
M. Ferreras-Romero ◽  
Keyword(s):  
Reference Conditions ◽  
Ecological Status ◽  
Quality Criteria ◽  
Management Tool ◽  
Mountain Rivers ◽  
Natural Park ◽  
Environmental Status ◽  
Spanish Atlantic ◽  
Good Ecological Status

In compliance with the European Water Framework Directive, member states have had to develop a method to assess the quality of aquatic ecosystems by comparing the current situation regarding near–natural reference conditions for each river type. In 2008, the Spanish Ministry of Environment approved the Order of Water Planning Statement. This statement sets out reference conditions and ecological status class change limits for the different types of rivers in Spain for which sufficient data are available. In the presentstudy, we established reference conditions and quality class thresholds for streams classified as wet Betic mountain rivers from 24 reaches of streams located in the Los Alcornocales natural park, using two qualitative indices based on macroinvertebrates (IBMWP and IMMi–L). The results for the IBMWP index indicate that from the standpoint of management of the ecological state, the watercourses studied show more affinity with the types of the Spanish Atlantic siliceous slope than with those of the Mediterranean siliceous slope when we consider EQR values. Considering the threshold values, the index resembles siliceous low Mediterranean mountain rivers (type 8). However, the EQR values do not match those calculated in this study. These results suggest that it is necessary to use an index adapted to the characteristics of these watercourses. Application of the quality criteria contained in the Guadalete–Barbate and Mediterranean–Andalusian Basin Plans to the management of these waterways is discussed, because it is unlikely that they ensure the maintenance of good ecological status. We thus propose a new calibration of the IBMWP index that ensures the maintenance of good environmental status of watercourses in this natural area, and the use of the IMMi–L index as an effective management tool. However, as our study area represents only a part of the wet headwaters in the southern Iberian peninsula, analysis of other basin types is necessary to complete such information.

Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening

2020 ◽  
Vol 25 (6) ◽  
pp. 655-664
Author(s):  
Wienand A. Omta ◽  
Roy G. van Heesbeen ◽  
Ian Shen ◽  
Jacob de Nobel ◽  
Desmond Robers ◽  
...  
Keyword(s):  
Machine Learning ◽  
Training Set ◽  
Data Set ◽  
Genome Wide ◽  
Machine Learning Model ◽  
Exploratory Data ◽  
Interfering Rna ◽  
Insight Into ◽  
Training Sets

There has been an increase in the use of machine learning and artificial intelligence (AI) for the analysis of image-based cellular screens. The accuracy of these analyses, however, is greatly dependent on the quality of the training sets used for building the machine learning models. We propose that unsupervised exploratory methods should first be applied to the data set to gain a better insight into the quality of the data. This improves the selection and labeling of data for creating training sets before the application of machine learning. We demonstrate this using a high-content genome-wide small interfering RNA screen. We perform an unsupervised exploratory data analysis to facilitate the identification of four robust phenotypes, which we subsequently use as a training set for building a high-quality random forest machine learning model to differentiate four phenotypes with an accuracy of 91.1% and a kappa of 0.85. Our approach enhanced our ability to extract new knowledge from the screen when compared with the use of unsupervised methods alone.

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