Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Drafting Authors: Neil Adger, Pramod Aggarwal, Shardul Agrawala, Joseph Alcamo, Abdelkader Allali, Oleg Anisimov, Nigel Arnell, Michel Boko, Osvaldo Canziani, Timothy Carter, Gino Casassa, Ulisses Confalonieri, Rex Victor Cruz, Edmundo de Alba Alcaraz, William Easterling, Christopher Field, Andreas Fischlin, Blair Fitzharris, Carlos Gay García, Clair Hanson, Hideo Harasawa, Kevin Hennessy, Saleemul Huq, Roger Jones, Lucka Kajfež Bogataj, David Karoly, Richard Klein, Zbigniew Kundzewicz, Murari Lal, Rodel Lasco, Geoff Love, Xianfu Lu, Graciela Magrín, Luis José Mata, Roger McLean, Bettina Menne, Guy Midgley, Nobuo Mimura, Monirul Qader Mirza, José Moreno, Linda Mortsch, Isabelle Niang-Diop, Robert Nicholls, Béla Nováky, Leonard Nurse, Anthony Nyong, Michael Oppenheimer, Jean Palutikof, Martin Parry, Anand Patwardhan, Patricia Romero Lankao, Cynthia Rosenzweig, Stephen Schneider, Serguei Semenov, Joel Smith, John Stone, Jean-Pascal van Ypersele, David Vaughan, Coleen Vogel, Thomas Wilbanks, Poh Poh Wong, Shaohong Wu, Gary Yohe

Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change

Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems

Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

/pdf/climate-change-2007-the-physical-science-basis-1x93ttz9jt.pdf

Climate Change 2007: The Physical Science Basis.

Impatti, adattamento e vulnerabilita Le cause e le responsabilita dei cambiamenti climatici sono state trattate sul numero di ottobre della rivista Cda. Approfondiamo l’argomento presentando il documento: “Cambiamenti climatici 2007: impatti, adattamento e vulnerabilita” votato ad aprile 2007 dal secondo gruppo di lavoro del Comitato Intergovernativo sui Cambiamenti Climatici (Intergovernmental Panel on Climate Change). Si tratta del secondo di tre documenti che compongono il quarto rapporto sui cambiamenti climatici.

/pdf/climate-change-2007-impacts-adaptation-and-vulnerability-31xc1cf4d3.pdf

Climate Change 2007: Impacts, Adaptation and Vulnerability.

There has been increasing scientific evidence related to climate change and its attribution, impacts, and possibilities of mitigation. Yet, climate contrarianism still persists. This paper ...

Inconvenience versus Rationality: Reflections on Different Faces of Climate Contrarianism in Poland and Norway

The notion of sustainable development exploits the concept of inter-generational and intra-generational equity. Availability of freshwater resources in adequate quantity and quality is an indispensable element of the life support system, essential to attain the sustainable development and the particular Millennium Development Goals. In many regions, increasing pressures, such as population growth with consequences for settlements and production of food and fiber, and human aspirations to better living standards and consumptive life style, are likely to render the road to sustainable development even more difficult. The water shortage problems are likely to grow in a number of areas, unveiling the need for curbing water demands and for improving efficiency of water use. Sustainable development should have a mechanism of resilience against floods and droughts and an appropriate preparedness system. In order to solve increasingly complex needs a holistic and integrated approach is needed, benefiting of synergies with other disciplines.

Global freshwater resources for sustainable development

. Economic losses caused by hydrological extremes – floods and droughts – have been on the rise. Hydrological extremes jeopardize human security and impact on societal livelihood and welfare. Security can be generally understood as freedom from threat and the ability of societies to maintain their independent identity and their functional integrity against forces of change. Several dimensions of security are reviewed in the context of hydrological extremes. The traditional interpretation of security, focused on the state military capabilities, has been replaced by a wider understanding, including economic, societal and environmental aspects that get increasing attention. Floods and droughts pose a burden and serious challenges to the state that is responsible for sustaining economic development, and societal and environmental security. The latter can be regarded as the maintenance of ecosystem services, on which a society depends. An important part of it is water security, which can be defined as the availability of an adequate quantity and quality of water for health, livelihoods, ecosystems and production, coupled with an acceptable level of water-related risks to people, environments and economies. Security concerns arise because, over large areas, hydrological extremes − floods and droughts − are becoming more frequent and more severe. In terms of dealing with water-related risks, climate change can increase uncertainties, which makes the state’s task to deliver security more difficult and more expensive. However, changes in population size and development, and level of protection, drive exposure to hydrological hazards.

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Hydrological extremes and security

Z. W. KUNDZEWICZ & D. KOUTSOYIANNIS 1 Research Centre of Agricultural and Forest Environment, Polish Academy of Sciences, Bukowska 19, 60-809 Poznań, Poland zkundze@man.poznan.pl 2 Potsdam Institute for Climate Impact Research, D-14412 Potsdam, Germany 3 Department of Water Resources, Faculty of Civil Engineering, National Technical University of Athens, Heroon Polytechneiou 5, GR-157 80 Zographou, Greece dk@itia.ntua.gr

https://www.tandfonline.com/doi/pdf/10.1623/hysj.51.2.357

Zbigniew W. Kundzewicz

Papers

Inconvenience versus Rationality: Reflections on Different Faces of Climate Contrarianism in Poland and Norway

Global freshwater resources for sustainable development

Hydrological extremes and security

Reply to discussions of "Editorial—The peer review system: prospects and challenges"

Detection and Attribution of Changes in Water Resources