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Ayalew Kassahun
Researcher at Wageningen University and Research Centre
Publications - 48
Citations - 1533
Ayalew Kassahun is an academic researcher from Wageningen University and Research Centre. The author has contributed to research in topics: Supply chain & Business process modeling. The author has an hindex of 11, co-authored 39 publications receiving 889 citations.
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Crop yield prediction using machine learning: A systematic literature review
TL;DR: This study performed a Systematic Literature Review to extract and synthesize the algorithms and features that have been used in crop yield prediction studies, and found Convolutional Neural Networks is the most widely used deep learning algorithm in these studies.
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Position paper: Environmental decision support systems (EDSS) development - Challenges and best practices
Brian S. McIntosh,J. C. Ascough,Mark J. Twery,J. Chew,Amgad Elmahdi,Dagmar Haase,Julien J. Harou,Daryl H. Hepting,Susan Cuddy,Anthony Jakeman,Serena Chen,Ayalew Kassahun,Sven Lautenbach,Keith Matthews,Wendy Merritt,Nigel W.T. Quinn,Ignasi Rodríguez-Roda,Stefan Sieber,M. Stavenga,Andrea Sulis,Jenifer Lyn Ticehurst,Martin Volk,M. Wrobel,H. van Delden,Sondoss Elsawah,Andrea Emilio Rizzoli,Alexey Voinov +26 more
TL;DR: The authors provide a set of best practice recommendations concerned with promoting design for ease of use, design for usefulness, establishing trust and credibility, promoting EDSS acceptance, and starting simple and small in functionality terms to help facilitate the achievement of desirable social and environmental outcomes.
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Quality assurance in model based water management - review of existing practice and outline of new approaches
Jens Christian Refsgaard,Hans Jørgen Henriksen,William G. Harrar,Huub Scholten,Ayalew Kassahun +4 more
TL;DR: The structure and key aspects of new generic guidelines and a set of electronically based supporting tools that are under development within the HarmoniQuA project are presented.
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A methodology to support multidisciplinary model-based water management
Huub Scholten,Ayalew Kassahun,Jens Christian Refsgaard,Theodore Kargas,Costas Gavardinas,A.J.M. Beulens +5 more
TL;DR: MoST facilitates co-operation in teams, modelling project audits and re-use of experiences of previous modelling projects by supporting the full modelling process by offering an ontological knowledge base and a Modelling Support Tool (MoST).
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Obstacles and features of Farm Management Information Systems: A systematic literature review
TL;DR: This study systematically ranked the identified features and obstacles of FMISs and described the key associated aspects, which include the agricultural domains, modeling approaches, delivery models, and identified stakeholders.