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Computational Intelligence in data-driven and hybrid Models and Data Analysis
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Full-Text Articles in Physical Sciences and Mathematics
Uncertainty Propagation In A Hydro-Meteorological Approach: From The Cloud To The Flood Map., Juan Pablo Rodríguez-Rincón, José Agustín Breña-Naranjo, Adrián Pedrozo-Acuña
Uncertainty Propagation In A Hydro-Meteorological Approach: From The Cloud To The Flood Map., Juan Pablo Rodríguez-Rincón, José Agustín Breña-Naranjo, Adrián Pedrozo-Acuña
International Conference on Hydroinformatics
Globally, it is widely known that floods remain the most frequent and devastating natural hazards. Likewise, there is recent evidence showing an increase in the number of extreme flood events observed around the world. Therefore, it is imperative to develop an integrated flood assessment framework that enables a better understanding of both, the generation of these events and the interaction of key variables within the hydro-meteorological system. The aim of this investigation is to study the propagation of meteorological uncertainty to a numerically estimated flood map. For such purpose, we utilise a cascade modelling approach comprised by a Numerical Weather …
Ensemble Simulation From Multiple Data Sources In A Spatially Distributed Hydrological Model Of The Rijnland Water System In The Netherlands, Isnaeni Murdi Hartanto, Schalk-Jan Van Andel, Thomas K. Alexandridis, Dimitri P. Solomatine
Ensemble Simulation From Multiple Data Sources In A Spatially Distributed Hydrological Model Of The Rijnland Water System In The Netherlands, Isnaeni Murdi Hartanto, Schalk-Jan Van Andel, Thomas K. Alexandridis, Dimitri P. Solomatine
International Conference on Hydroinformatics
Data for water management is increasingly easy to access, it has finer spatial and temporal resolution, and it is available from various sources. Precipitation data can be obtained from meteorological stations, radar, satellites and weather models. Land use data is also available from different satellite products and different providers. The various sources of data may confirm each other or give very different values in space and time. However, from these various data sources, it can often not be judged beforehand that one data is correct and others are wrong. Each source has its own value for a particular purpose. The …
Fast Neural Network Surrogates For Complex Groundwater Flow Models, Niels Schütze, Tirthankar Roy
Fast Neural Network Surrogates For Complex Groundwater Flow Models, Niels Schütze, Tirthankar Roy
International Conference on Hydroinformatics
Surrogate modeling approach has been adopted in the study to replace computationally expensive physical-based numerical flow and transport model. Two approximate surrogate models namely, Artificial Neural Network (ANN) and Gaussian Process Model (GPM) are developed individually using a scenario database generated from the density dependent numerical flow and transport model OpenGeoSys (OGS). The state-space surrogates have the flexibility to move freely from one point to another within a time frame of decades and also to allow for moderate extrapolation in the case of extreme abstractions. The performance of the GPM was better in many cases with a little compromise on …
Causal Graph Discovery For Hydrological Time Series Knowledge Discovery, Piraporn Jangyodsuk, Dong-Jun Seo, Jean Gao
Causal Graph Discovery For Hydrological Time Series Knowledge Discovery, Piraporn Jangyodsuk, Dong-Jun Seo, Jean Gao
International Conference on Hydroinformatics
Causal inference or causal relationship discovery is an important task in hydrological study to explore the causes of abnormal hydrology phenomena such as drought and flood, which will help improving our prediction and response ability to natural disasters. Different from generic causality study where causalrelation discovery is sufficient, for extreme hydrological situation prediction and modeling, we need not only to construct a causal graph to reveal the contributing factors, but also to provide the lead time of each cause to its effect. Lead time is the time difference between the occurrence of lead and effect. Though causal inference or causal …
A Novel Nested Dynamic Programming (Ndp) Algorithm For Multipurpose Reservoir Optimization, Blagoj Delipetrev, Andreja Jonoski, Dimitri P. Solomatine
A Novel Nested Dynamic Programming (Ndp) Algorithm For Multipurpose Reservoir Optimization, Blagoj Delipetrev, Andreja Jonoski, Dimitri P. Solomatine
International Conference on Hydroinformatics
We present a novel nDP (nested dynamic programming) algorithm for multipurpose reservoir optimization. The nDP algorithm is built from two algorithms: 1) dynamic programming (DP) and 2) nested optimal water allocation algorithm implemented with Simplex and quadratic Knapsack. The main objective function is to minimize the sum of weighted square deviations of each objective over the whole time horizon. Each objective is described with its water demand or target and its weights at each time step. The nDP algorithm begins with the DP optimization algorithm that at each DP transition executes the nested optimal water allocation algorithm. The optimal water …
Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models, Nagendra Kayastha, Dimitri P. Solomatine, Durga Lal Shrestha
Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models, Nagendra Kayastha, Dimitri P. Solomatine, Durga Lal Shrestha
International Conference on Hydroinformatics
This study presents an approach to combine uncertainties of the hydrological model outputs predicted from a number of machine learning models. The machine learning based uncertainty prediction approach is very useful for estimation of hydrological models' uncertainty in particular hydro-metrological situation in real-time application [1]. In this approach the hydrological model realizations from Monte Carlo simulations are used to build different machine learning uncertainty models to predict uncertainty (quantiles of pdf) of the a deterministic output from hydrological model . Uncertainty models are trained using antecedent precipitation and streamflows as inputs. The trained models are then employed to predict the …
An Ensemble Approach For Typhoon Runoff Simulation With Perturbed Rainfall Forecasts In Taiwan, Lan Yu, Lloyd Hock Chye Chua, Dong-Sin Shih
An Ensemble Approach For Typhoon Runoff Simulation With Perturbed Rainfall Forecasts In Taiwan, Lan Yu, Lloyd Hock Chye Chua, Dong-Sin Shih
International Conference on Hydroinformatics
Under the background of demand for accurate and reliable flood forecasting, various methodologies are used to model floods. However, not all the phases of the hydrograph can be predicted by any models, even though the global optimum may be reached. In order to exploit the distinct information provided by different models, an ensemble approach is proposed to improve the forecasting accuracy and reliability. The ensemble precipitation estimates from a Weather Research Forecasting (WRF) model were used to as inputs to model the rainfall-runoff process in Taiwan. A Dynamic Evolving Neural-Fuzzy Inference System was applied to combining the predictions of the …
Integration Of Trmm Rainfall In Numerical Model For Pesticide Prediction In Subtropical Climate, Manika Gupta, N.K. Garg, Prashant K. Srivastava, Tanvir Islam
Integration Of Trmm Rainfall In Numerical Model For Pesticide Prediction In Subtropical Climate, Manika Gupta, N.K. Garg, Prashant K. Srivastava, Tanvir Islam
International Conference on Hydroinformatics
Rain gauge data in developing countries are usually very limited, which constrains most of the hydrological modelling applications. The satellite based rainfall estimates could be a promising choice and hence can be used as a surrogate to ground-based rainfall. However, the usefulness of these products needs to be evaluated for hydrological application such as for pesticide predictions. The present study compares the contaminant transport simulation with the utilization of Tropical Rainfall Measuring Mission (TRMM) rainfall compared with rain gauge data from the field site. Through this study, transport trends of the pesticide, Thiram, a dithiocarbamate, at different time and depth …
River Flows Prediction By Ensemble Model, Milan Cisty, Celar Lubomir
River Flows Prediction By Ensemble Model, Milan Cisty, Celar Lubomir
International Conference on Hydroinformatics
This review paper will deal with the possibilities of applying the R programming language in water resources and hydrologic applications in education and research. The objective of this paper is to present some features and packages that make R a powerful environment for analyzing data from the hydrology and water resources management fields, hydrological modelling, the post-processing of the results of such modelling, and other tasks.
Hydrodynamic And Water Quality Surrogate Modeling For Reservoir Operation, Juan Aguilar, Schalk-Jan Van Andel, Micha Werner, Dimitri P. Solomatine
Hydrodynamic And Water Quality Surrogate Modeling For Reservoir Operation, Juan Aguilar, Schalk-Jan Van Andel, Micha Werner, Dimitri P. Solomatine
International Conference on Hydroinformatics
A methodology is developed for reservoir release decisions considering forecasted downstream dissolved oxygen local conditions. River water quality management using reservoirs focuses mainly on how to develop a release schedule that may improve downstream conditions based on the seasonal change of the water quality within the reservoir. This improvement, however does not take into account the downstream local water quality state, which in certain cases might be more important, as the pollutant load downstream could be diluted with the upstream available volume released from the reservoir. Field sampling collected data suggest that the dissolved oxygen concentration decay produced by polluted …
Evaluating North Sea Water Level Monitoring Network Considering Uncertain Information Theory Quantities, Leonardo Alfonso, Elena Ridolfi, Sandra Gaytan, Francesco Napolitano, Fabio Russo
Evaluating North Sea Water Level Monitoring Network Considering Uncertain Information Theory Quantities, Leonardo Alfonso, Elena Ridolfi, Sandra Gaytan, Francesco Napolitano, Fabio Russo
International Conference on Hydroinformatics
Information-theory provides, among others, conceptual methods to quantify the amount of information contained in a random variable, as well as methods to quantify the amount of information contained and shared among two or more variables. Although these concepts have been successfully applied in Hydrology and other fields, the valuation of these quantities is sensible to different parameters used to estimate the probabilities that underline the entropy concept. Typical examples are the bin size of histograms used to compute probabilities and the Kendall correlation coefficient used to estimate copula entropy. The selection of these parameters has subsequent effects on other Information …
Rediscovering Manning’S Equation Using Genetic Programming, Carlos F. Gaitan
Rediscovering Manning’S Equation Using Genetic Programming, Carlos F. Gaitan
International Conference on Hydroinformatics
Open-channel hydraulics’ (OCH) research traditionally links empirical formulas to observational data. One of the most common equations in OCH is Manning’s formula for open channel flow (Q) driven by gravity (also known as the Gauckler-Manning-Strickler formula). The formula relates the cross-sectional average velocity (V=Q/A), the hydraulic radius (R), and the slope of the water surface (S) with a friction coefficient n, characteristic of the channel’s surface. Here we show a practical example where Genetic Programming (GP), a technique derived from Bioinformatics, can be used to derive an empirical relationship based on different synthetic datasets of the aforementioned parameters. Specifically, we …
Considering The Effect Of Uncertainty And Variability In The Synthetic Generation Of Influent Wastewater Time Series, Mansour Talebizadehsardari, Evangelia Belia, Peter A. Vanrolleghem
Considering The Effect Of Uncertainty And Variability In The Synthetic Generation Of Influent Wastewater Time Series, Mansour Talebizadehsardari, Evangelia Belia, Peter A. Vanrolleghem
International Conference on Hydroinformatics
The availability of influent wastewater time series is crucial for assessing the performance of a wastewater treatment plant (WWTP) under dynamic flow and loading conditions. Given the difficulty of collecting sufficient data, synthetic generation may be the only option. Usually, the main constituents of the influent time series (e.g. flow, COD, TSS, TKN) show periodic, auto-correlation, and cross-correlation structures in time. Therefore researchers have used statistical models (e.g. auto-regressive time series models) for random generation of the influent time series. However, these regular patterns in time could be significantly distorted during rain events (wet weather flow (WWF) conditions) in which …
Newly Automated System For Integrated Assessment Of The Conditions Of Underwater Gas Pipelines, Oleg Ilinich, Vadim Pryahin
Newly Automated System For Integrated Assessment Of The Conditions Of Underwater Gas Pipelines, Oleg Ilinich, Vadim Pryahin
International Conference on Hydroinformatics
An underwater gas pipeline is the portion of the pipeline that crosses a river beneath its bottom. Underwater gas pipelines are subject to increasing dangers as time goes by. An accident at an underwater gas pipeline can lead to technological and environmental disaster on the scale of an entire region. Therefore, timely troubleshooting of all underwater gas pipelines in order to prevent any potential accidents will remain a pressing task for the industry. The most important aspect of resolving this challenge is the quality of the automated system in question. Now the industry doesn't have any automated system that fully …
Decision Tree Analysis Of Processes Generating Water-Related Building Damage: A Case Study In Rotterdam, The Netherlands, Matthieu Spekkers, Francois Clemens, Marie-Claire Ten Veldhuis
Decision Tree Analysis Of Processes Generating Water-Related Building Damage: A Case Study In Rotterdam, The Netherlands, Matthieu Spekkers, Francois Clemens, Marie-Claire Ten Veldhuis
International Conference on Hydroinformatics
The objective of this study was to identify the main failure mechanisms behind water-related building damage and to investigate to what extent these processes are related to characteristics of buildings and rainfall events. Results are based on the mining of property level insurance damage data, for a case study in Rotterdam, the Netherlands. This study has found that most frequent causes of water-related damage relate to roof leakages (28%), bursts of household water supply pipes (19%) and blocked household wastewater systems (18%). Cases of sewer flooding or depression filling were less present (2.4% and 0.6%), but showed stronger correlations with …
Genetic Programming For Cellular Automata Urban Inundation Modelling, Mike J. Gibson, Edward C. Keedwell, Dragan A. Savić
Genetic Programming For Cellular Automata Urban Inundation Modelling, Mike J. Gibson, Edward C. Keedwell, Dragan A. Savić
International Conference on Hydroinformatics
Recent advances in Cellular Automata (CA) represent a new, computationally efficient method of simulating flooding in urban areas. A number of recent publications in this field have shown that CAs can be much more computationally efficient than methods than using standard shallow water equations (Saint Venant/Navier-Stokes equations). CAs operate using local state-transition rules that determine the progression of the flow from one cell in the grid to another cell, in many publications the Manning’s Formula is used as a simplified local state transition rule. Through the distributed interactions of the CA, computationally simplified urban flooding can be processed, although these …
Effective Data Management Enables Intelligent Utility Management, Gary L.S. Wong
Effective Data Management Enables Intelligent Utility Management, Gary L.S. Wong
International Conference on Hydroinformatics
Instrumentation and automation plays a vital role to managing the water industry. These systems generate vast amounts of data that must be effectively managed in order to enable intelligent decision making. Time series data management software, commonly known as data historians are used for collecting and managing real-time (time series) information. More advanced software solutions provide a data infrastructure or utility wide Operations Data Management System (ODMS) that stores, manages, calculates, displays, shares, and integrates data from multiple disparate automation and business systems that are used daily in water utilities. These ODMS solutions are proven and have the ability to …
Computationally Efficient Lumped Floodplain Modelling Using Data-Driven Methods, Vincent Wolfs, Patrick Willems
Computationally Efficient Lumped Floodplain Modelling Using Data-Driven Methods, Vincent Wolfs, Patrick Willems
International Conference on Hydroinformatics
Modelling floodplains adequately is crucial for numerous water management applications. Many of these applications require a large number of simulations or long term analyses, such as optimization problems at catchment scale, uncertainty analyses or real time control of hydraulic structures to prevent flooding. Therefore, models with a limited calculation time are necessary. Various computationally efficient models exist that describe the flow in rivers, but the modelling of floodplains is often overlooked. This study focuses on the computationally efficient lumped modelling of floodplains. Two different data-driven modelling approaches are proposed that predict the inundation level in the floodplain and the flow …
Effect Of Different Hydrological Model Structures On The Assimilation Of Distributed Uncertain Observations, Maurizio Mazzoleni, Leonardo Alfonso, Dimitri P. Solomatine
Effect Of Different Hydrological Model Structures On The Assimilation Of Distributed Uncertain Observations, Maurizio Mazzoleni, Leonardo Alfonso, Dimitri P. Solomatine
International Conference on Hydroinformatics
The reliable evaluation of the flood forecasting is a crucial problem for assessing flood risk and consequent damages. Different hydrological models (distributed, semi-distributed or lumped) have been proposed in order to deal with this issue. The choice of the proper model structure has been investigated by many authors and it is one of the main sources of uncertainty for a correct evaluation of the outflow hydrograph. In addition, the recent increasing of data availability makes possible to update hydrological models as response of real-time observations. For these reasons, the aim of this work it is to evaluate the effect of …
Committees Of Specialized Conceptual Hydrological Models: Comparative Study, Nagendra Kayastha, Dimitri P. Solomatine
Committees Of Specialized Conceptual Hydrological Models: Comparative Study, Nagendra Kayastha, Dimitri P. Solomatine
International Conference on Hydroinformatics
Committee modelling approach is skillful prediction in the domain of hydrological modelling that allows explicitly to derive predictive model outputs. In this approach, the different individual models are optimally combined. Generally if a single hydrological model or the model calibrated by the single aggregated objective function it is hard to capture all facets of a complex process and to present the best possible model outputs. This model could be either capable for high flows or for low flows or not for both cases hence more flexible modelling architectures are required. Here the possibilities is building several specialized models each of …
Accurate Prediction Of Ecological Quality Ratio With Product Unit Neural Networks, Arie De Niet, Erwin Meijers, Sebastiaan Schep
Accurate Prediction Of Ecological Quality Ratio With Product Unit Neural Networks, Arie De Niet, Erwin Meijers, Sebastiaan Schep
International Conference on Hydroinformatics
This paper shows how to use product unit neural networks (punn's) to derive a data-driven model for the prediction of ecological quality in surface water. In a comparison with other approaches the punns provide by far the best prediction. Moreover they reveal the underlying relations between characteristics and ecological quality. Encouraged by the European Water Framework Directive many measures are taken by Dutch government to improve water quality en ecological quality of surface water. Although it is well known which type of measures are most effective in what cases, it is uncertain what the total effect of a measure is. …
Comparison Of Ensemble Kalman Filtering And Particle Filtering On Short-Term Streamflow Forecasting Using A Distributed Hydrologic Model, Seong Jin Noh
International Conference on Hydroinformatics
Floods are the most common and widespread disasters in the world and are responsible for a greater number of damaging events than any other type of natural event. However, due to various uncertainties that originate from simulation models, observations, and forcing data, it is still insufficient to obtain accurate flood forecasting results with the required lead times. Recently, ensemble forecasting techniques based on data assimilation (DA) have become increasingly popular, due to their potential ability to explicitly handle the various sources of uncertainty in operational hydrological models. Difficulty lies in DA for flood forecasting because nonlinearity increases sharply during flood …
Application Of Data Mining For Reverse Osmosis Process In Seawater Desalination, Jaewuk Koo, Yonghyun Shin, Sangho Lee, Juneseok Choi
Application Of Data Mining For Reverse Osmosis Process In Seawater Desalination, Jaewuk Koo, Yonghyun Shin, Sangho Lee, Juneseok Choi
International Conference on Hydroinformatics
Reverse osmosis (RO) membrane process has been considered a promising technology for water treatment and desalination. However, it is difficult to predict the performance of pilot- or full-scale RO systems because numerous factors are involved in RO performance, including variations in feed water (quantity, quality, temperature, etc), membrane fouling, and time-dependent changes (deteriorations). Accordingly, this study intended to develop a practical approach for the analysis of operation data in pilot-scale reverse osmosis (RO) processes. Novel techniques such as artificial neural network (ANN) and genetic programming (GP) technique were applied to correlate key operating parameters and RO permeability statistically. The ANN …