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

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.

The Environmental Protection Agency (EPA) provides access to information on a variety of topics related to the environment and strives to inform citizens of health risks. The EPA also has an extensive library network that consists of 26 libraries throughout the United States, which provide access to a plethora of information to EPA employees, scientists, and researchers. The EPA implemented a reorganization project to digitize their materials so they would be more accessible to a wider range of users, but this plan was drastically accelerated when the EPA was threatened with a budget cut. It chose to close and reduce the hours and services of some of their libraries. As a result, the agency was accused of denying users the “right to know” by making information unavailable, not providing an adequate strategic plan, and discarding vital materials. This case study explores the background of the digitization project, the practices implemented, and the critiques of the project.

/pdf/the-environmental-protection-agency-3gx4opzrjm.pdf

The Environmental Protection Agency

▶ Addresses a wide range of timely environment, economic and energy topics ▶ A forum to review, analyze and stimulate the development, testing and implementation of mitigation and adaptation strategies at regional, national and global scales ▶ Contributes to real-time policy analysis and development as national and international policies and agreements are discussed and promulgated ▶ 94% of authors who answered a survey reported that they would definitely publish or probably publish in the journal again

Mitigation and Adaptation Strategies for Global Change

The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

The Analysis of Time Series: An Introduction

https://ira.lib.polyu.edu.hk/bitstream/10397/1314/3/JH8.pdf

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

17 18 Lin Qiu 19 Professor, Institute of environmental & municipal engineering, North China Institute of Water 20 Conservancy and Hydroelectric power, ZhengZhou, 450011, P.R.Ch 21 22 Abstract. Developing a hydrological forecasting model based on past records is crucial to 23

A comparison of performance of several artificial intelligence

Accurate time- and site-specific forecasts of streamflow and reservoir inflow are important in effective hydropower reservoir management and scheduling. Traditionally, autoregressive moving-average (ARMA) models have been used in modelling water resource time series as a standard representation of stochastic time series. Recently, artificial neural network (ANN) approaches have been proven to be efficient when applied to hydrological prediction. In this paper, the support vector machine (SVM) is presented as a promising method for hydrological prediction. Over-fitting and local optimal solution are unlikely to occur with SVM, which implements the structural risk minimization principle rather than the empirical risk minimization principle. In order to identify appropriate parameters of the SVM prediction model, a shuffled complex evolution algorithm is performed through exponential transformation. The SVM prediction model is tested using the long-term observations of discharges of monthly river fl...

https://www.tandfonline.com/doi/pdf/10.1623/hysj.51.4.599

Using support vector machines for long-term discharge prediction

An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction

Hydrological time series forecasting is one of the most important applications in modern hydrology, especially for effective reservoir management. In this research, the auto-regressive integrated moving average (ARIMA) model coupled with the ensemble empirical mode decomposition (EEMD) is presented for forecasting annual runoff time series. First, the original annual runoff time series is decomposed into a finite and often small number of intrinsic mode functions (IMFs) and one residual series using EEMD technique for a deep insight into the data characteristics. Then each IMF component and residue is forecasted, respectively, through an appropriate ARIMA model. Finally, the forecasted results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original annual runoff series. Three annual runoff series from Biuliuhe reservoir, Dahuofang reservoir and Mopanshan reservoir, in China, are investigated using developed model based on the four standard statistical performance evaluation measures (RMSE, MAPE, R and NSEC). The results obtained in this work indicate that EEMD can effectively enhance forecasting accuracy and that the proposed EEMD-ARIMA model can significantly improve ARIMA time series approaches for annual runoff time series forecasting.

Kwok Wing Chau

Papers

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

A comparison of performance of several artificial intelligence

Using support vector machines for long-term discharge prediction

An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction

Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition