Dimitri P. Solomatine

发表

ANN-based representation of parametric and residual uncertainty of models

Francesca Pianosi, Durga L. Shrestha, Dimitri P. Solomatine, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Uncertainty analysis of an inflow forecasting model: extension of the UNEEC machine learning-based method

Francesca Pianosi, Dimitri P. Solomatine, Durga Lal Shrestha, 2010 .

A Hybrid Approach in Combining Numerical and Data-driven Models in Modelling Fine Sediment Transport

Dimitri P. Solomatine, B. Bhattacharya, S Sewagudde, 2011 .

River flow forecasting using artificial neural networks

Dimitri P. Solomatine, Yonas B. Dibike, D. Solomatine, 2001 .

River cross-section extraction from the ASTER global DEM for flood modeling

Ioana Popescu, Dimitri P. Solomatine, Andreja Jonoski, 2012, Environ. Model. Softw..

Machine Learning Approach to Modeling Sediment Transport

Roland K. Price, Dimitri P. Solomatine, Biswa Bhattacharya, 2007 .

Architecture of a prototype cloud application for a hydro information system

Dimitri P. Solomatine, Blagoj Delipetrev, Andreja Jonoski, 2011 .

Computational intelligence in earth sciences and environmental applications: Issues and challenges

Julio J. Valdés, Dimitri P. Solomatine, Vladimir Cherkassky, 2006, Neural Networks.

Application of adaptive fuzzy rule-based models for reconstruction of missing precipitation events

Dimitri P. Solomatine, A. J. Abebe, R. G. W. Venneker, 2000 .

Multi-model ensemble forecasting in high dimensional chaotic system

Dimitri P. Solomatine, Michael Baskara L. A. Siek, D. Solomatine, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

On the encapsulation of numerical-hydraulic models in artificial neural network

Dimitri P. Solomatine, Yonas B. Dibike, Michael B. Abbott, 1999 .

Non-linear wave data assimilation with an ANN-type wind-wave model and Ensemble Kalman Filter (EnKF)

Dimitri P. Solomatine, Ahmadreza Zamani, Arnold W. Heemink, 2010 .

Optimal modularization of learning models in forecasting environmental variables

Dimitri P. Solomatine, D. Solomatine, 2006 .

Experiments with AdaBoost.RT, an Improved Boosting Scheme for Regression

Durga L. Shrestha, Dimitri P. Solomatine, D. Solomatine, 2006, Neural Computation.

Approach to robust multi-objective optimization and probabilistic analysis: the ROPAR algorithm

Dimitri P. Solomatine, Oscar O. Marquez-Calvo, D. Solomatine, 2019, Journal of Hydroinformatics.

Measuring domain decomposition effect in estuary model parallelization using high performance computer

Dimitri P. Solomatine, Santosa Sandy Putra, Gerald Agusto Corzo Perez, 2014, 2014 6th International Conference on Information Technology and Electrical Engineering (ICITEE).

Eager and Lazy Learning Methods in the Context of Hydrologic Forecasting

Durga L. Shrestha, Dimitri P. Solomatine, Mahesh Maskey, 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

IMPROVING PERFORMANCE OF CHLOROPHYL CONCENTRATION TIME SERIES SIMULATION WITH ARTIFICIAL NEURAL NETWORKS

Dimitri P. Solomatine, Henk van den Boogaard, Shen Yiyang, 1998 .

Development of a cloud application for supporting water resources modeling

Dimitri P. Solomatine, Blagoj Delipetrev, Andreja Jonoski, 2012 .

A novel approach to machine learning-based error correction for hydrological models

Juan Carlos, Leonardo Alfonso, Yi Xu, 2014 .

Adaptive cluster covering and evolutionary approach: comparison, differences and similarities

Dimitri P. Solomatine, D. Solomatine, 2005, 2005 IEEE Congress on Evolutionary Computation.

Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models

Dimitri P. Solomatine, Durga Lal Shrestha, Nagendra Kayastha, 2014 .

Cloud computing application for water resources based on open source software and open standards - a prototype

Dimitri P. Solomatine, Blagoj Delipetrev, D. Solomatine, 2015 .

A Novel Method to Estimate the Model Uncertainty Based on the Model Errors

Dimitri P. Solomatine, Durga Lal Shrestha, 2006 .

Data-driven modelling: some past experiences and new approaches

Avi Ostfeld, Dimitri P. Solomatine, D. Solomatine, 2008 .

A comprehensive review on the design and optimization of surface water quality monitoring networks

Guangtao Fu, Yi Zheng, Dimitri P. Solomatine, 2020, Environ. Model. Softw..

FOR AN

Dimitri P. Solomatine, Biswanath Bhattacharya, 2022 .

FLEXIBLE AND OPTIMAL M5 MODEL TREES WITH APPLICATIONS TO FLOW PREDICTIONS

Dimitri P. Solomatine, Michael Baskara L. A. Siek, D. Solomatine, 2004 .

Estimation of Model Parameter of Water Quality Simulation Model using Genetic Algorithm

Dimitri P. Solomatine, Hiroshi Matsushita, Masato Noguchi, 2004 .

Data-driven techniques for modelling the gross primary production of the páramo vegetation using climate data: Application in the Ecuadorian Andean region

Dimitri P. Solomatine, Arthur E. Mynett, Gerald Corzo, 2018, Ecol. Informatics.

Chaotic model with data assimilation using NARX network

Dimitri P. Solomatine, Michael Baskara L. A. Siek, 2009, 2009 International Joint Conference on Neural Networks.

Precipitation Sensor Network Optimal Design Using Time-Space Varying Correlation Structure

Leonardo Alfonso, Dimitri P. Solomatine, Juan Carlos Chacon-Hurtado, 2014 .

Baseflow separation techniques for modular artificial neural network modelling in flow forecasting

Dimitri P. Solomatine, Gerald Corzo, D. Solomatine, 2007 .

Committees Of Specialized Conceptual Hydrological Models: Comparative Study

Dimitri P. Solomatine, Nagendra Kayastha, 2014 .

Computational intelligence in earth and environmental sciences

Julio J. Valdés, Dimitri P. Solomatine, Vladimir Cherkassky, 2007, Neural Networks.

Optimizing Mixtures of Local Experts in Tree-Like Regression Models

Dimitri P. Solomatine, Michael Baskara L. A. Siek, D. Solomatine, 2005, Artificial Intelligence and Applications.

Experiments with encapsulation of Monte Carlo simulation results in machine learning models

Dimitri P. Solomatine, Durga Lal Shrestha, Nagendra Kayastha, 2010 .

Optimal reservoir operation policies using novel nested algorithms

Dimitri P. Solomatine, Blagoj Delipetrev, Andreja Jonoski, 2015 .

Neural networks and reinforcement learning in control of water systems

Dimitri P. Solomatine, B Bhattacharya, A H Lobbrecht, 2003 .

Application of global optimization to the design of pipe networks

Dimitri P. Solomatine, A. J. Abebe, D. Solomatine, 2000 .

Machine learning in real-time control of water systems

Dimitri P. Solomatine, Arnold H. Lobbrecht, 2002 .

Distributed environmental impact assessment using Internet

Dimitri P. Solomatine, Huang Yan, Michael B. Abbott, 1999 .

Data mining, knowledge discovery and data-driven modelling

Dimitri P. Solomatine, S. Velickov, B. Bhattacharya, 2003 .

Nonlinear multi-model ensemble prediction using dynamic Neural Network with incremental learning

Dimitri P. Solomatine, Michael Baskara L. A. Siek, D. Solomatine, 2011, The 2011 International Joint Conference on Neural Networks.

Monthly water balance modeling: Probabilistic, possibilistic and hybrid methods for model combination and ensemble simulation

Mohsen Nasseri, Newsha K. Ajami, Dimitri P. Solomatine, 2014 .

Pump Scheduling for a Large Water Distribution Network. Milan, Italy☆

Quan Pan, Dimitri P. Solomatine, Andreja Jonoski, 2017 .

Machine learning in sedimentation modelling

Dimitri P. Solomatine, Biswanath Bhattacharya, D. Solomatine, 2006, Neural Networks.

2006 Special issue: Earth Sciences and Environmental Applications of Computational IntelligenceIntroduction

Julio J. Valdés, Dimitri P. Solomatine, Vladimir Cherkassky, 2006, Neural Networks.

PREDICTING HYDROLOGICAL MODELS UNCERTAINTY: USE OF MACHINE LEARNING

Dimitri P. Solomatine, Durga Lal Shrestha, 2006 .

Intelligent Drought Tracking for its Use in Machine Learning: Implementation and First Results

Dimitri P. Solomatine, Vitali Diaz, Henny A. J. Van Lanen, 2018 .

Neural networks and fuzzy systems in model based control of the Overwaard polder

Dimitri P. Solomatine, Dimitri P. Solomatine, Arnold H. Lobbrecht, 2005 .

Improving Empirical Models with Machine Learning

Dimitri P. Solomatine, Biswanath Bhattacharya, D. Solomatine, 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

Modular learning models in forecasting natural phenomena

Dimitri P. Solomatine, Michael Baskara L. A. Siek, D. Solomatine, 2006, Neural Networks.

Machine learning in soil classification

Dimitri P. Solomatine, B. Bhattacharya, 2005 .

Automatic Calibration of Water Quality Simulation Model Using Global Optimization Technique

Dimitri P. Solomatine, Masato Noguchi, Wataru Nishida, 2003 .

Multiobjective Direct Policy Search Using Physically Based Operating Rules in Multireservoir Systems

Dimitri P. Solomatine, Gerald Corzo, Josias Ritter, 2020, 2001.09926.

Model Induction with Support Vector Machines: Introduction and Applications

Dimitri P. Solomatine, Yonas B. Dibike, Slavco Velickov, 2001 .

Machine learning approaches for estimation of prediction interval for the model output

Durga L. Shrestha, Dimitri P. Solomatine, D. Solomatine, 2006, Neural Networks.

Multiobjective Optimization of Operational Responses for Contaminant Flushing in Water Distribution Networks

Leonardo Alfonso, Dimitri P. Solomatine, Andreja Jonoski, 2010 .

M5 Model Trees and Neural Networks: Application to Flood Forecasting in the Upper Reach of the Huai River in China

Dimitri P. Solomatine, Yunpeng Xue, D. Solomatine, 2004 .

Citizen observations contributing to flood modelling: opportunities and challenges

Ioana Popescu, Dimitri P. Solomatine, Andreja Jonoski, 2017 .

Learning Errors of Environmental Mathematical Models

Durga L. Shrestha, Dimitri P. Solomatine, Vadim Kuzmin, 2013, EANN.

Model Induction with Support Vector Machines: Introduction and Applications

Dimitri P. Solomatine, Yonas B. Dibike, Slavco Velickov, 2001 .

Mixtures of Simple Models vs ANNs in Hydrological Modeling

Dimitri P. Solomatine, D. Solomatine, 2003, HIS.

An Algorithm for Clustering and Classification of Series Data with Constraint of Contiguity

Dimitri P. Solomatine, Biswanath Bhattacharya, D. Solomatine, 2003, HIS.

Development of a web application for water resources based on open source software

Dimitri P. Solomatine, Blagoj Delipetrev, Andreja Jonoski, 2014, Comput. Geosci..

Multivariate chaotic models vs neural networks in predicting storm surge dynamics

Dimitri P. Solomatine, Michael Baskara L. A. Siek, Slavco Velickov, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

A Graph Theoretical Sectorization Approach for Energy Reduction in Water Distribution Networks

Quan Pan, Dimitri P. Solomatine, Andreja Jonoski, 2016 .

Data-Driven Modelling: Concepts, Approaches and Experiences

Dimitri P. Solomatine, D. Solomatine, 2009 .

Machine learning in soil classification

Dimitri P. Solomatine, Biswanath Bhattacharya, D. Solomatine, 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

Application of methods predicting model uncertainty in flood forecasting

Dimitri P. Solomatine, Durga Lal Shrestha, N Kayastha, 2012 .

Learning hydrologic flow separation algorithm and local ANN committee modeling

Dimitri P. Solomatine, Gerald Corzo, D. Solomatine, 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

Comparing machine learning methods in estimation of model uncertainty

Durga L. Shrestha, Dimitri P. Solomatine, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

Knowledge-based modularization and global optimization of artificial neural network models in hydrological forecasting

Dimitri P. Solomatine, Gerald Corzo, D. Solomatine, 2007, Neural Networks.

Data‐Driven Modeling and Computational Intelligence Methods in Hydrology

Dimitri P. Solomatine, 2006 .

Experiences in using evolutionary and non-evolutionary optimization methods in models calibration

Dimitri P. Solomatine, 2006 .

Neural networks and M5 model trees in modelling water level-discharge relationship

Dimitri P. Solomatine, Biswanath Bhattacharya, D. Solomatine, 2005, Neurocomputing.

Real-time assimilation of streamflow observations into a hydrological routing model: effects of model structures and updating methods

Leonardo Alfonso, Yuqiong Liu, Dong-Jun Seo, 2018 .

How Well Can Machine Learning Models Perform without Hydrologists? Application of Rational Feature Selection to Improve Hydrological Forecasting

Dimitri P. Solomatine, Vsevolod Moreido, Boris Gartsman, 2021, Water.