scholarly journals A Waterbody Typology Derived from Catchment Controls Using Self-Organising Maps

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 78
Author(s):  
Eleanore L. Heasley ◽  
James D. A. Millington ◽  
Nicholas J. Clifford ◽  
Michael A. Chadwick
Keyword(s):  
Rapid Assessment ◽  
Regulatory Compliance ◽  
Physical Habitat ◽  
Machine Learning Technique ◽  
England And Wales ◽  
River Habitat ◽  
Sediment Size ◽  
Learning Technique ◽  
River Habitat Survey ◽  
Level Monitoring

Multiple catchment controls contribute to the geomorphic functioning of river systems at the reach-level, yet only a limited number are usually considered by river scientists and managers. This study uses multiple morphometric, geological, climatic and anthropogenic catchment characteristics to produce a single national typology of catchment controls in England and Wales. Self-organising maps, a machine learning technique, are used to reduce the complexity of the GIS-derived characteristics to classify 4485 Water Framework Directive waterbodies into seven types. The waterbody typology is mapped across England and Wales, primarily reflecting an upland to lowland gradient in catchment controls and secondarily reflecting the heterogeneity of the catchment landscape. The seven waterbody types are evaluated using reach-level physical habitat indices (including measures of sediment size, flow, channel modification and diversity) extracted from River Habitat Survey data. Significant differences are found between each of the waterbody types for most habitat indices suggesting that the GIS-derived typology has functional application for reach-level habitats. This waterbody typology derived from catchment controls is a valuable tool for understanding catchment influences on physical habitats. It should prove useful for rapid assessment of catchment controls for river management, especially where regulatory compliance is based on reach-level monitoring.

scholarly journals Integrating network topology metrics into studies of catchment-level effects on river characteristics

2019 ◽  
Vol 23 (5) ◽  
pp. 2305-2319 ◽  
Author(s):  
Eleanore L. Heasley ◽  
Nicholas J. Clifford ◽  
James D. A. Millington
Keyword(s):  
Network Topology ◽  
Network Structure ◽  
Spatial Arrangement ◽  
Regulatory Compliance ◽  
River Network ◽  
Network Density ◽  
Stream Order ◽  
Sediment Size ◽  
The Uk ◽  
River Habitat Survey

Abstract. The spatial arrangement of the river network is a fundamental characteristic of the catchment, acting as a conduit between catchment-level effects and reach morphology and ecology. Yet river network structure is often simplified to reflect an upstream-to-downstream gradient of river characteristics, commonly represented by stream order. The aim of this study is to quantify network topological structure using two network density metrics – one that represents network density over distance and the other over elevation – that can easily be extracted from digital elevation models and so may be applied to any catchment across the globe. These metrics should better account for the multi-dimensional nature of the catchment than stream order and be functionally applicable across geomorphological, hydrological and ecological attributes of the catchment. The functional utility of the metrics is assessed by appropriating monitoring data collected for regulatory compliance to explore patterns of river characteristics in relation to network topology. This method is applied to four comparatively low-energy, anthropogenically modified catchments in the UK using river characteristics derived from England's River Habitat Survey database. The patterns in river characteristics explained by network density metrics are compared to stream order as a standard measure of topology. The results indicate that the network density metrics offer a richer and functionally more relevant description of network topology than stream order, highlighting differences in the density and spatial arrangement of each catchment's internal network structure. Correlations between the network density metrics and river characteristics show that habitat quality score consistently increases with network density in all catchments as hypothesized. For other measures of river character – modification score, flow-type speed and sediment size – there are varying responses in different catchments to the two network density metrics. There are few significant correlations between stream order and the river characteristics, highlighting the limitations of stream order in accounting for network topology. Overall, the results suggest that network density metrics are more powerful measures which conceptually and functionally provide an improved method of accounting for the impacts of network topology on the fluvial system.

scholarly journals The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

Voice Pathology Detection Using Machine Learning Technique

2020 ◽  
Author(s):  
Fahad Taha AL-Dhief ◽  
Nurul Mu'azzah Abdul Latiff ◽  
Nik Noordini Nik Abd. Malik ◽  
Naseer Sabri ◽  
Marina Mat Baki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Voice Pathology Detection

scholarly journals Longitudinal national-level monitoring of on-farm broiler welfare identifies consistently poorly performing farms

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Siobhan Mullan ◽  
Bobby Stuijfzand ◽  
Andrew Butterworth
Keyword(s):  
Animal Welfare ◽  
Outcome Measures ◽  
National Level ◽  
Regulatory Compliance ◽  
On Farm ◽  
Level Monitoring ◽  
Poor Welfare ◽  
Check Compliance

AbstractA range of welfare outcome measures relating to on-farm welfare are monitored in UK slaughterhouses to check compliance with the European Broiler Directive. A national dataset from 438,155 batches of chickens between 2010 and 2014 and from 228,795 batches between 2016 and 2018 was analysed. The data contained information about 3.1 billion chickens. The highest mean proportion for a single condition was for ascites/oedema in 2016–2018 at 0.384%, affecting 3.9 million chickens/year sent to slaughter during that time, followed by abnormal colour/fevered at 0.324%, affecting 3.4 million chickens/year. Identifying farms most likely to have poor welfare is an important strategy for improving animal welfare overall, and for maximising the capacity for checking regulatory compliance when resources are limited. We found a greater proportion of broiler farms overall remained consistently in the best quartile (16.4%) rather than the worst quartile (6.6%). Farms that exceeded a Government ‘trigger’ threshold for poor welfare were significantly more likely to subsequently improve than ‘non-trigger’ farms, although they usually remained in the worst performing quartile of farms.

Evaluation of caridean ecological distribution in the Ubatuba region, southeastern Brazilian coast using unsupervised machine learning technique

2021 ◽  
Author(s):  
Alexandre Oliveira Marques ◽  
Aline Nonato Sousa ◽  
Veronica Pereira Bernardes ◽  
Camila Hipolito Bernardo ◽  
Danielle Monique Reis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brazilian Coast ◽  
Ecological Distribution ◽  
Machine Learning Technique ◽  
Unsupervised Machine Learning ◽  
Learning Technique

scholarly journals Election model classifications of problem-based learning using a machine learning technique

2021 ◽  
Vol 1088 (1) ◽  
pp. 012030
Author(s):  
Cep Lukman Rohmat ◽  
Saeful Anwar ◽  
Arif Rinaldi Dikananda ◽  
Irfan Ali ◽  
Ade Rinaldi Rizki
Keyword(s):  
Machine Learning ◽  
Problem Based Learning ◽  
Machine Learning Technique ◽  
Learning Technique
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