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.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/WR023i010p02009

Comment on “Dilettantism in hydrology: Transition or destiny?” by V. Klemeš

/pdf/temperature-related-climate-extremes-in-the-potsdam-2pxf5yigf6.pdf

Temperature-related climate extremes in the Potsdam observation record

Abstract Glacierised river catchments are highly sensitive to climate change, while large populations may depend on their water resources. The irrigation agriculture and the communities along the Tarim River, NW China, strongly depend on the discharge from the glacierised catchments surrounding the Taklamakan Desert. While recent increasing discharge has been beneficial for the agricultural sector, future runoff under climate change is uncertain. We assess three climate change scenarios by forcing two glacio-hydrological models with output of eight general circulation models. The models have different glaciological modelling approaches but were both calibrated to discharge and glacier mass balance observations. Projected changes in climate, glacier cover and river discharge are examined over the twenty-first century and generally point to warmer and wetter conditions. The model ensemble projects median temperature and precipitation increases of + 1.9–5.3 °C and + 9–24%, respectively, until the end of the century compared to the 1971–2000 reference period. Glacier area is projected to shrink by 15–73% (model medians, range over scenarios), depending on the catchment. River discharge is projected to first increase by about 20% in the Aksu River catchments with subsequent decreases of up to 20%. In contrast, discharge in the drier Hotan and Yarkant catchments is projected to increase by 15–60% towards the end of the century. The large uncertainties mainly relate to the climate model ensemble and the limited observations to constrain the glacio-hydrological models. Sustainable water resource management will be key to avert the risks associated with the projected changes and their uncertainties. 

/pdf/projected-climate-change-and-its-impacts-on-glaciers-and-3vaxl4gw.pdf

Projected climate change and its impacts on glaciers and water resources in the headwaters of the Tarim River, NW China/Kyrgyzstan

Impact of Climate and Land-Use Changes on Natural Resources in the Agricultural Landscape

A considerable increase in flood damage has been observed worldwide, with mean annual values reaching tens of billions of US$ and still considerable number of fatalities. Most dramatic floods occur overseas (in particular in Asia China, India, and Bangladesh), but in the last decade, floods have severely hit large parts of the European continent. For example, Poland was visited by dramatic summer floods in 1997, 1998, and 2001. The flood hitting the drainage basins of the rivers Odra and the Vistula in summer 1997 has been labeled by the media (in a somewhat exaggerated way, as far as the number of fatalities are concerned) as the greatest natural disaster in the 1000-year history of Poland, being definitely costliest ever. The Odra (Oder) flood in the summer of 1997 had an international dimension. It hit three countries in Central Europe – Czech Republic, Poland, and Germany, causing 110 fatalities in the first two countries and large material losses in all three. The present contribution puts the destructive 1997 Odra flood in perspective. Information on this dramatic flood event of unprecedented severity is provided, including meteorological, hydrological, socio-economic and disaster management aspects. From the hydrological point of view, this flood was a very rare event; with return period in some river cross-sections of the order of several hundred years and more. It was associated with the Vb type of weather system. The context of the flood is discussed, in the sense of both past-topresent records and projections for the future. Flood management options are reviewed. In Poland, as well as in much of Central Europe, river flooding has been recently recognized as a major hazard. Having observed that flood risk and vulnerability are likely to have grown in many areas, one is curious to understand the reasons for growth. These can be sought in the O.F. Vasiliev et al. (eds.), Extreme Hydrological Events: New Concepts for Security, 97–110.

Zbigniew W. Kundzewicz

Papers

Comment on “Dilettantism in hydrology: Transition or destiny?” by V. Klemeš

Temperature-related climate extremes in the Potsdam observation record

Projected climate change and its impacts on glaciers and water resources in the headwaters of the Tarim River, NW China/Kyrgyzstan

Impact of Climate and Land-Use Changes on Natural Resources in the Agricultural Landscape

Summer 1997 flood in poland in perspective