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

Over the past 15 years, artificial neural networks (ANNs) have been used increasingly for prediction and forecasting in water resources and environmental engineering. However, despite this high level of research activity, methods for developing ANN models are not yet well established. In this paper, the steps in the development of ANN models are outlined and taxonomies of approaches are introduced for each of these steps. In order to obtain a snapshot of current practice, ANN development methods are assessed based on these taxonomies for 210 journal papers that were published from 1999 to 2007 and focus on the prediction of water resource variables in river systems. The results obtained indicate that the vast majority of studies focus on flow prediction, with very few applications to water quality. Methods used for determining model inputs, appropriate data subsets and the best model structure are generally obtained in an ad-hoc fashion and require further attention. Although multilayer perceptrons are still the most popular model architecture, other model architectures are also used extensively. In relation to model calibration, gradient based methods are used almost exclusively. In conclusion, despite a significant amount of research activity on the use of ANNs for prediction and forecasting of water resources variables in river systems, little of this is focused on methodological issues. Consequently, there is still a need for the development of robust ANN model development approaches.

Review: Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions

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

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

Machine learning methods, a family of statistical techniques with origins in the field of artificial intelligence, are recognized as holding great promise for the advancement of understanding and prediction about ecological phenomena. These modeling techniques are flexible enough to handle complex problems with multiple interacting elements and typically outcompete traditional approaches (e.g., generalized linear models), making them ideal for modeling ecological systems. Despite their inherent advantages, a review of the literature reveals only a modest use of these approaches in ecology as compared to other disciplines. One potential explanation for this lack of interest is that machine learning techniques do not fall neatly into the class of statistical modeling approaches with which most ecologists are familiar. In this paper, we provide an introduction to three machine learning approaches that can be broadly used by ecologists: classification and regression trees, artificial neural networks,...

http://depts.washington.edu/oldenlab/wordpress/wp-content/uploads/2013/03/QuarterReviewBiol_2008.pdf

Machine Learning Methods Without Tears: A Primer for Ecologists

In the recent past, machine learning (ML) techniques such as artificial neural networks (ANN) have been increasingly used to model algal bloom dynamics. In the present paper, along with ANN, we select genetic programming (GP) for modelling and prediction of algal blooms in Tolo Harbour, Hong Kong. The study of the weights of the trained ANN and also the GP-evolved equations shows that they correctly identify the ecologically significant variables. Analysis of various ANN and GP scenarios indicates that good predictions of long-term trends in algal biomass can be obtained using only chlorophyll-a as input. The results indicate that the use of biweekly data can simulate long-term trends of algal biomass reasonably well, but it is not ideally suited to give short-term algal bloom predictions.

/pdf/neural-network-and-genetic-programming-for-modelling-coastal-58ct9wk3pg.pdf

Neural network and genetic programming for modelling coastal algal blooms

The use of water quality indices (WQIs) as a tool to evaluate the status of water quality in rivers has been introduced since the 1960s. The WQI transforms selected water quality parameters into a dimensionless number so that changes in river water quality at any particular location and time could be presented in a simple and easily understandable manner. Although many WQIs have been developed, there is no worldwide accepted method for implementing the steps used for developing a WQI. Thus, there is a continuing interest to develop accurate WQIs that suit a local or regional area. This paper aimed to provide significant contribution to the development of future river WQIs through a review of 30 existing WQIs based on the four steps needed to develop a WQI. These steps are the selection of parameters, the generation of sub-indices, the generation of parameter weights and the aggregation process to compute the final index value. From the 30 reviewed WQIs, 7 were identified as most important based on their wider use and they were discussed in detail. It was observed that a major factor that influences wider use of a WQI is the support provided by the government and authorities to implement a WQI as the main tool to evaluate the status of rivers. Since there is a lot of subjectivity and uncertainty involved in the steps for developing and applying a WQI, it is recommended that the opinion of local water quality experts is taken, especially in the first three steps (through techniques like Delphi method). It was also observed that uncertainty and sensitivity analysis was rarely undertaken to reduce uncertainty, and hence such an analysis is recommended for future studies.

/pdf/development-of-river-water-quality-indices-a-review-21n1hpr2bw.pdf

Development of river water quality indices-a review

Genetic programming (GP), a relatively new evolutionary technique, is demonstrated in this study to evolve codes for the solution of problems. First, a simple example in the area of symbolic regression is considered. GP is then applied to real-time runoff forecasting for the Orgeval catchment in France. In this study, GP functions as an error updating scheme to complement a rainfall-runoff model, MIKE11/NAM. Hourly runoff forecasts of different updating intervals are performed for forecast horizons of up to nine hours. The results show that the proposed updating scheme is able to predict the runoff quite accurately for all updating intervals considered and particularly for updating intervals not exceeding the time of concentration of the catchment. The results are also compared with those of an earlier study, by the World Meteorological Organization, in which autoregression and Kalman filter were used as the updating methods. Comparisons show that GP is a better updating tool for real-time flow forecasting. Another important finding from this study is that nondimensionalizing the variables enhances the symbolic regression process significantly.

Genetic programming and its application in real-time runoff forecasting

https://aquadoc.typepad.com/files/indicator-based-water-sustainability-assessment---review.pdf

Indicator-based water sustainability assessment — A review

https://ira.lib.polyu.edu.hk/bitstream/10397/1008/1/EAAI2.pdf

Nitin Muttil

Papers

Neural network and genetic programming for modelling coastal algal blooms

Development of river water quality indices-a review

Genetic programming and its application in real-time runoff forecasting

Indicator-based water sustainability assessment — A review

Machine-learning paradigms for selecting ecologically significant input variables