scholarly journals Hydroinformatics and urban drainage: an agenda for the beginning of the 21st century

2000 ◽  
Vol 2 (2) ◽  
pp. 133-147 ◽  
Author(s):  
Roland K. Price
Keyword(s):  
21St Century ◽  
Network Performance ◽  
Data Driven ◽  
Urban Drainage ◽  
Drainage Network ◽  
Water Industry ◽  
Research And Practice ◽  
Physically Based ◽  
Complementary Nature ◽  
Urban Storm

The developing insights of hydroinformatics have much to offer the water industry, and particularly urban storm and wastewater drainage. This paper reviews aspects of data mining and knowledge discovery of large asset databases, the complementary nature of both physically based and data-driven modelling of drainage network performance, and the roles of decision support systems and knowledge management. It concludes with the presentation of ten agenda items that would benefit research and practice at the beginning of the 21st century.

Privacy after the Agile Turn

2017 ◽  
Author(s):  
Seda Gurses ◽  
Joris Vredy Jan van Hoboken
Keyword(s):  
Information Systems ◽  
Software Industry ◽  
Data Driven ◽  
Dynamic Nature ◽  
Privacy Regulation ◽  
Research And Practice ◽  
Production Processes ◽  
Regulatory Frameworks ◽  
Software And Hardware ◽  
New Challenges

Moving beyond algorithms and big data as starting points for discussions about privacy, the authors of Privacy After the Agile Turn focus our attention on the new modes of production of information systems. Specifically, they look at three shifts that have transformed most of the software industry: software is now delivered as services, software and hardware have moved into the cloud and software’s development is ever more agile. These shifts have altered the conditions for privacy governance, and rendered the typical mental models underlying regulatory frameworks for information systems out-of-date. After 'the agile turn', modularity in production processes creates new challenges for allocating regulatory responsibility. Privacy implications of software are harder to address due to the dynamic nature of services and feature development, which undercuts extant privacy regulation that assumes a clear beginning and end of production processes. And the data-driven nature of services, beyond the prospect of monetization, has become part of software development itself. With their focus on production, the authors manage to place known challenges to privacy in a new light and create new avenues for privacy research and practice.

scholarly journals A Data-Driven Method for the Temporal Estimation of Soil Water Potential and Its Application for Shallow Landslides Prediction

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1208
Author(s):  
Massimiliano Bordoni ◽  
Fabrizio Inzaghi ◽  
Valerio Vivaldi ◽  
Roberto Valentino ◽  
Marco Bittelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Potential ◽  
Soil Water ◽  
Temporal Trends ◽  
Soil Water Potential ◽  
Shallow Landslides ◽  
Water Dynamics ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Physically Based

Soil water potential is a key factor to study water dynamics in soil and for estimating the occurrence of natural hazards, as landslides. This parameter can be measured in field or estimated through physically-based models, limited by the availability of effective input soil properties and preliminary calibrations. Data-driven models, based on machine learning techniques, could overcome these gaps. The aim of this paper is then to develop an innovative machine learning methodology to assess soil water potential trends and to implement them in models to predict shallow landslides. Monitoring data since 2012 from test-sites slopes in Oltrepò Pavese (northern Italy) were used to build the models. Within the tested techniques, Random Forest models allowed an outstanding reconstruction of measured soil water potential temporal trends. Each model is sensitive to meteorological and hydrological characteristics according to soil depths and features. Reliability of the proposed models was confirmed by correct estimation of days when shallow landslides were triggered in the study areas in December 2020, after implementing the modeled trends on a slope stability model, and by the correct choice of physically-based rainfall thresholds. These results confirm the potential application of the developed methodology to estimate hydrological scenarios that could be used for decision-making purposes.

scholarly journals Data-driven versus self-similar parameterizations for Stochastic Advection by Lie Transport and Location Uncertainty

2019 ◽  
Author(s):  
Valentin Resseguier ◽  
Wei Pan ◽  
Baylor Fox-Kemper
Keyword(s):  
Fluid Dynamics ◽  
Heterogeneous Data ◽  
Data Driven ◽  
Accurate Data ◽  
High Quality ◽  
Ensemble Forecasts ◽  
Homogeneous Surface ◽  
Location Uncertainty ◽  
Physically Based ◽  
Self Similar

Abstract. Stochastic subgrid parameterizations enable ensemble forecasts of fluid dynamics systems and ultimately accurate data assimilation. Stochastic Advection by Lie Transport (SALT) and models under Location Uncertainty (LU) are recent and similar physically-based stochastic schemes. SALT dynamics conserve helicity whereas LU models conserve kinetic energy. After highlighting general similarities between LU and SALT frameworks, this paper focuses on their common challenge: the parameterization choice. We compare uncertainty quantification skills of a stationary heterogeneous data-driven parameterization and a non-stationary homogeneous self-similar parameterization. For stationary, homogeneous Surface Quasi-Geostrophic (SQG) turbulence, both parameterizations lead to high quality ensemble forecasts. This paper also discusses a heterogeneous adaptation of the homogeneous parameterization targeted at better simulation of strong straight buoyancy fronts.

Applications of Data-Driven Modelling and Machine Learning in Control of Water Resources

2011 ◽  
pp. 197-217 ◽  
Author(s):  
D. P. Solomatine
Keyword(s):  
Machine Learning ◽  
Water Resources ◽  
Computational Intelligence ◽  
Flood Control ◽  
Water Levels ◽  
Data Driven ◽  
Real Time Control ◽  
Modeling And Control ◽  
Physically Based ◽  
And Control

Traditionally, management and control of water resources is based on behavior-driven or physically based models based on equations describing the behavior of water bodies. Since recently models built on the basis of large amounts of collected data are gaining popularity. This modeling approach we will call data-driven modeling; it borrows methods from various areas related to computational intelligence—machine learning, data mining, soft computing, etc. The chapter gives an overview of successful applications of several data-driven techniques in the problems of water resources management and control. The list of such applications includes: using decision trees in classifying flood conditions and water levels in the coastal zone depending on the hydrometeorological data, using artificial neural networks (ANN) and fuzzy rule-based systems for building controllers for real-time control of water resources, using ANNs and M5 model trees in flood control, using chaos theory in predicting water levels for ship guidance, etc. Conclusions are drawn on the applicability of the mentioned methods and the future role of computational intelligence in modeling and control of water resources.

Sign in / Sign up

Export Citation Format

Share Document