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.

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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.

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Climate Change 2007: Impacts, Adaptation and Vulnerability.

Citation Kundzewicz, Z. W. & Stakhiv, E. Z. (2010) Are climate models “ready for prime time” in water resources management applications, or is more research needed? Editorial. Hydrol. Sci. J. 55(7), 1085–1089.

https://www.tandfonline.com/doi/pdf/10.1080/02626667.2010.513211

Are climate models “ready for prime time” in water resources management applications, or is more research needed?

Uncertainty in climate change impacts on water resources

Les resultats d'une etude de detection de changements dans des series temporelles hydrologiques de debits fluviaux maximum annuels sont presentes. Un jeu de donnees mondial, constitue de 195 longues series de debits moyens journaliers, a ete selectionne parmi plus de mille series temporelles longues fournies par le Centre Mondial de Donnees sur l'Ecoulement (GRDC) de Coblence en Allemagne. La selection verifie d'une part certains criteres comme la longueur de la serie, l'actualite, l'absence de lacunes durables et ponctuelles et une distribution geographique adequate, et d'autre part une priorite aux plus petits bassins versants. L'analyse des debits maximum annuels ne verifie pas l'hypothese d'une augmentation generalisee des forts debits. Bien que 27 cas d'augmentation forte et statistiquement significative aient ete identifies grâce au test de Mann-Kendall, 31 cas de diminution ont egalement ete identifies, tandis que la plupart (137) des series temporelles ne presente aucun changement significatif (au niveau 10%). La prudence est preconisee dans l'interpretation de ces resultats, dans la mesure ou la crue est un phenomene complexe, cause par de nombreux facteurs qui relevent de processus climatiques locaux, regionaux et hemispheriques. De plus, le debit fluvial a une forte variabilite naturelle et presente une persistance a long terme qui peuvent perturber les resultats des tests de tendance et de signification.

Trend detection in river flow series: 1. Annual maximum flow

In this article, flood protection is considered in the context of sustainability. On the one hand, floods destroy human heritage and jeopardize sustainable development, which can be defined as “non-decreasing quality of life.” On the other hand, following the most common interpretation of sustainable development, one should not choose flood protection policies that could be rated by future generations as inappropriate options of flood defense. This is how several large structural flood defenses are often viewed. Non-structural measures are in better agreement with the spirit of sustainable development, being more reversible, commonly acceptable, and environment-friendly. Among such measures are source control (watershed/landscape structure management), laws and regulations (including zoning), economic instruments, an efficient flood forecast-warning system, a system of flood risk assessment, awareness raising, flood-related data bases, etc. As flood safety cannot be reached in most vulnerable are...

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Non-structural Flood Protection and Sustainability

Several episodes of extreme precipitation or extreme lack of precipitation (and high temperature) leading to dramatic and high-impact floods and droughts have occurred in Europe in recent years. Climate scenarios suggest that problems of too little or too much water may become more severe in the future. Using data from the Hadley Centre’s HadRM3 model, this paper analyzes future changes in the characteristics of intense precipitation (mean daily precipitation amounts and number of days with intense precipitation in a year) and the duration of dry (also dry and hot) spells over the European continent, comparing the time periods of 1961–1990 and 2070–2099. The potential for intense precipitation is likely to increase in the warmer climate of the future, contributing to the growth of flood hazard in areas where inundations are typically triggered by heavy rain. The projected number of days with intense precipitation and the maximum daily precipitation are likely to increase over much of Europe, especially in the central and northern parts. According to projections, ‘dry and hot’ extremes may become more severe for most of Europe. The areas already affected by water stress in the present climate (e.g. southern Europe) are expected to experience even more severe conditions.

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Zbigniew W. Kundzewicz

Papers

Are climate models “ready for prime time” in water resources management applications, or is more research needed?

Uncertainty in climate change impacts on water resources

Trend detection in river flow series: 1. Annual maximum flow

Non-structural Flood Protection and Sustainability

Precipitation extremes in the changing climate of Europe