Hydroinformatics: computational intelligence and technological developments in water science applications—Editorial

See, Linda; Solomatine, Dimitri; Abrahart, Robert J.; Toth, Elena

doi:10.1623/hysj.52.3.391

Cited by 35 publications

(19 citation statements)

References 33 publications

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“…To begin with, data-driven modeling approaches offer an avenue towards the identification of process coupling as revealed in time series records (Abrahart et al, 2012;See et al, 2007;Young, 2013). The information theoretic approach of Ruddell and Kumar is one methodology that can extract signatures of coupling from time series records (Ruddell and Kumar, 2009a, b).…”

Section: Co-evolutionary Modelingmentioning

confidence: 99%

Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

Thompson

Sivapalan

Harman

et al. 2013

Hydrol. Earth Syst. Sci.

129

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Abstract.Globally, many different kinds of water resources management issues call for policy-and infrastructure-based responses. Yet responsible decision-making about water resources management raises a fundamental challenge for hydrologists: making predictions about water resources on decadal-to century-long timescales. Obtaining insight into hydrologic futures over 100 yr timescales forces researchers to address internal and exogenous changes in the properties of hydrologic systems. To do this, new hydrologic research must identify, describe and model feedbacks between water and other changing, coupled environmental subsystems. These models must be constrained to yield useful insights, despite the many likely sources of uncertainty in their predictions. Chief among these uncertainties are the impacts of the increasing role of human intervention in the global water cycle -a defining challenge for hydrology in the Anthropocene. Here we present a research agenda that proposes a suite of strategies to address these challenges from the perspectives of hydrologic science research. The research agenda focuses on the development of co-evolutionary hydrologic modeling to explore coupling across systems, and to address the implications of this coupling on the long-time behavior of the coupled systems. Three research directions support the development of these models: hydrologic reconstruction, comparative hydrology and model-data learning. These strategies focus on understanding hydrologic processes and feedbacks over long timescales, across many locations, and through strategic coupling of observational and model data in specific systems. We highlight the value of use-inspired and team-based science that is motivated by real-world hydrologic problems but targets improvements in fundamental understanding to support decision-making and management. Fully realizing the potential of this approach will ultimately require detailed integration of social science and physical science understanding of water systems, and is a priority for the developing field of sociohydrology.

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Section: Co-evolutionary Modelingmentioning

confidence: 99%

Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

Thompson

Sivapalan

Harman

et al. 2013

Hydrol. Earth Syst. Sci.

129

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“…Some other interesting work has been done in the area of solar radiation prediction using ARMA and Fourier analysis [37,67]. Furthermore, new approaches for predicting solar radiation series have been developed using ANN reported from different parts of the world, particularly from Turkey [74, 81,95,96], and authors from other places such as Negnevitsky and Le [70], Alawi and Hinai [4], Mohandes et al [65], Kemmoku et al [51] and Sfetsos and Coonick [82]. Mellit et al [63] made an ANFIS-based prediction for monthly clearness index and daily solar radiation.…”

Section: Data Driven Solar Radiation Modellingmentioning

confidence: 99%

Hydrological Data Driven Modelling

Remesan

Mathew

2015

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Aims and ScopeThe book series Earth Systems Data and Models publishes state-of-the-art research and technologies aimed at understanding processes and interactions in the earth system. A special emphasis is given to theory, methods, and tools used in earth, planetary and environmental sciences for: modeling, observation and analysis; data generation, assimilation and visualization; forecasting and simulation; and optimization. Topics in the series include but are not limited to: numerical, datadriven and agent-based modeling of the earth system; uncertainty analysis of models; geodynamic simulations, climate change, weather forecasting, hydroinformatics, and complex ecological models; model evaluation for decision-making processes and other earth science applications; and remote sensing and GIS technology.The series publishes monographs, edited volumes and selected conference proceedings addressing an interdisciplinary audience, which not only includes geologists, hydrologists, meteorologists, chemists, biologists and ecologists but also physicists, engineers and applied mathematicians, as well as policy makers who use model outputs as the basis of decision-making processes.More information about this series at

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“…A number of applications of datadriven and model-free approaches to solve environmental problems can be found in Cherkassky et al (2006), See et al (2007) and Kanevski (2008).…”

Section: Learning From Environmental Datamentioning

confidence: 99%

Data-driven topo-climatic mapping with machine learning methods

2009

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Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and nonlinear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

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Hydroinformatics: computational intelligence and technological developments in water science applications—Editorial

Cited by 35 publications

References 33 publications

Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

Hydrological Data Driven Modelling

Data-driven topo-climatic mapping with machine learning methods

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