scholarly journals Can Multiple Uses of Biomass Limit the Feedstock Availability for Future Biogas Production? An Overview of Biogas Feedstocks and Their Alternative Uses

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2747
Author(s):  
Dieu Linh Hoang ◽  
Chris Davis ◽  
Henri C. Moll ◽  
Sanderine Nonhebel
Keyword(s):  
Energy Use ◽  
Biogas Production ◽  
Large Share ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Potential Alternative ◽  
Multiple Uses ◽  
Feedstock Availability ◽  
Biomass Byproducts ◽  
Biobased Economy

Biogas is expected to contribute 10% of the total renewable energy use in Europe in 2030. This expectation largely depends on the use of several biomass byproducts and wastes as feedstocks. However, the current development of a biobased economy requires biomass sources for multiple purposes. If alternative applications also use biogas feedstocks, it becomes doubtful whether they will be available for biogas production. To explore this issue, this paper aims to provide an overview of potential alternative uses of different biogas feedstocks being researched in literature. We conducted a literature review using the machine learning technique “co-occurrence analysis of terms”. This technique reads thousands of abstracts from literature and records when pairs of biogas feedstock-application are co-mentioned. These pairs are assumed to represent the use of a feedstock for an application. We reviewed 109 biogas feedstocks and 217 biomass applications, revealing 1053 connections between them in nearly 55,000 scientific articles. Our results provide two insights. First, a large share of the biomass streams presently considered in the biogas estimates have many alternative uses, which likely limit their contribution to future biogas production. Second, there are streams not being considered in present estimates for biogas production although they have the proper characteristics.

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

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

scholarly journals Principled approach to the selection of the embedding dimension of networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Weiwei Gu ◽  
Aditya Tandon ◽  
Yong-Yeol Ahn ◽  
Filippo Radicchi
Keyword(s):  
Real World ◽  
Structural Information ◽  
General Purpose ◽  
Embedding Dimension ◽  
Network Embedding ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Low Dimensional ◽  
Large Corpus ◽  
Selection Of

AbstractNetwork embedding is a general-purpose machine learning technique that encodes network structure in vector spaces with tunable dimension. Choosing an appropriate embedding dimension – small enough to be efficient and large enough to be effective – is challenging but necessary to generate embeddings applicable to a multitude of tasks. Existing strategies for the selection of the embedding dimension rely on performance maximization in downstream tasks. Here, we propose a principled method such that all structural information of a network is parsimoniously encoded. The method is validated on various embedding algorithms and a large corpus of real-world networks. The embedding dimension selected by our method in real-world networks suggest that efficient encoding in low-dimensional spaces is usually possible.

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