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Matti Kummu

Aalto University

P.O. Box 21210

Helsinki, 00101

Finland

SCHOLARLY PAPERS

4

DOWNLOADS

332

TOTAL CITATIONS

0

Scholarly Papers (4)

1.

Assessing the Global Potential for Transition from Single to Multiple Cropping

Number of pages: 34 Posted: 26 May 2025
affiliation not provided to SSRN, affiliation not provided to SSRN, University of Helsinki, affiliation not provided to SSRN, Aalto University - Water & Development Research Group, affiliation not provided to SSRN, affiliation not provided to SSRN and Aalto University
Downloads 119 (600,462)

Abstract:

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Multiple cropping, Sustainable intensification, machine learning, Climate variability, Agricultural inputs

2.

Wastewater network assets' lifespan prediction: can we modify the machine learning algorithms to fit the purpose?

Number of pages: 39 Posted: 02 Jul 2024
Aalto University - Water & Development Research Group, Aalto University, Aalto University, Norwegian Science and Technology University (NTNU) and Aalto University
Downloads 103 (674,591)

Abstract:

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Sewer pipes lifespan, sewer deterioration, inverse probability of censoring, adapting machine learning, CCTV inspection, proactive planning, asset management

3.

Improving Wind Power Integration in Vertical Farming with Modifying the Power Demand

Number of pages: 24 Posted: 17 Jul 2023
Aalto University, Natural Resources Institute Finland, Aalto University, Natural Resources Institute Finland, Aalto University and Aalto University
Downloads 70 (875,328)

Abstract:

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Vertical farming, Wind power, Optimal energy system, Demand side flexibility

4.

Scenario Projections of South Asian Migration Patterns Amidst Environmental and Socioeconomic Change

Number of pages: 26 Posted: 21 Mar 2024
VU University Amsterdam, affiliation not provided to SSRN, VU University Amsterdam - Institute for Environmental Studies (IVM), affiliation not provided to SSRN, affiliation not provided to SSRN, Aalto University and affiliation not provided to SSRN
Downloads 40 (1,183,237)

Abstract:

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Environmental change, climate change, migration, South Asia, machine-learning, scenario projections