D. Solomatine

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

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

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

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

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

Application of methods predicting model uncertainty in flood forecasting

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

WEB-BASED FLOOD INFORMATION SYSTEM: CASE STUDY OF SOMESUL MARE, ROMANIA

Dimitri P. Solomatine, Andreja Jonoski, Adrian Almoradie, 2013 .

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..

A DATA MINING APPROACH TO MODELLING SEDIMENT TRANSPORT

Roland K. Price, Dimitri P. Solomatine, Biswa Bhattacharya, 2004 .

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

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

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

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

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

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

Computational intelligence in earth and environmental sciences

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

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

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

Sensitivity analysis of data-driven groundwater forecasts to hydroclimatic controls in irrigated croplands

F. Pianosi, D. Solomatine, G. Corzo, 2020, Journal of Hydrology.

Hydroclimatic Controls in Irrigated Croplands

F. Pianosi, D. Solomatine, G. Corzo, 2020 .

Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology

Dimitri Solomatine, Gerald Corzo, Amin Elshorbagy, 2009 .

Rainfall and streamflow sensor network design: a review of applications, classification, and a proposed framework

Leonardo Alfonso, Dimitri Solomatine, Juan Carlos Chacon-Hurtado, 2016 .

Spatiotemporal Analysis of Extreme Rainfall Events Using an Object-Based Approach

D. Solomatine, G. Corzo, R. Uijlenhoet, 2019, Spatiotemporal Analysis of Extreme Hydrological Events.

Analysis and simulation of the influencing factors on regional water use based on information entropy

Dimitri Solomatine, Lihua Ma, Shaozhong Kang, 2012 .

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Avi Ostfeld, Andrea Castelletti, Joseph R. Kasprzyk, 2014, Environ. Model. Softw..

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

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

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

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

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

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

Two Strategies of Adaptive Cluster Covering with Descent and Their Comparison to Other Algorithms

D. P. Solomatine, D. Solomatine, 1999, J. Glob. Optim..

Neural networks and reinforcement learning in control of water systems

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

A novel approach to parameter uncertainty analysis of hydrological models using neural networks

Dimitri Solomatine, Durga Lal Shrestha, N. Kayastha, 2009 .

Instance‐based learning compared to other data‐driven methods in hydrological forecasting

Durga Lal Shrestha, Dimitri P. Solomatine, Mahesh L. Maskey, 2008 .

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.

FLEXIBLE AND OPTIMAL M5 MODEL TREES WITH APPLICATIONS TO FLOW PREDICTIONS

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

ST-CORAbico: A Spatiotemporal Object-Based Bias Correction Method for Storm Prediction Detected by Satellite

David Saah, Remko Uijlenhoet, Ate Poortinga, 2020, Remote. Sens..

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.

A Comparison of Spatial–Temporal Scale Between Multiscalar Drought Indices in the South Central Region of Vietnam

D. Solomatine, G. Corzo, V. Medina, 2019, Spatiotemporal Analysis of Extreme Hydrological Events.

Water Quality Modelling for Nitrate Nitrogen Control Using HEC-RAS: Case Study of Nakdong River in South Korea

D. Solomatine, A. Jonoski, P. Goethals, 2023, Water.

Practical experience and framework for sensitivity analysis of hydrological models: six methods, three models, three criteria

Dimitri Solomatine, Anqi Wang, Dimitri Solomatine, 2018 .

Application of model trees and other machine learning techniques for algal growth prediction in Yongdam reservoir, Republic of Korea

Roland K. Price, Dimitri Solomatine, Ioana Popescu, 2010 .

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

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

Modular learning models in forecasting natural phenomena

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

Optimal modularization of learning models in forecasting environmental variables

Dimitri P. Solomatine, D. Solomatine, 2006 .

Local and hybrid learning models in forecasting natural phenomena

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

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.

Semi-optimal hierarchical regression models and ANNs

D.P. Solomatine, M.B.L.A. Siek, D. Solomatine, 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

INNOVATIVE APPROACHES TO FLOOD FORECASTING USING DATA DRIVEN AND HYBRID MODELLING

Roland K. Price, Dimitri P. Solomatine, D. Solomatine, 2004 .

PRO O F CO PY [ HE / 2002 / 022579 ] 001406 Q HE M 5 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 .

Data mining, knowledge discovery and data-driven modelling

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

Chapter 2 Data-Driven Modelling : Concepts , Approaches and Experiences

Robert J. Abrahart, Linda M. See, Dimitri P. Solomatine, 2017 .

Data-Driven Modelling: Concepts, Approaches and Experiences

Dimitri P. Solomatine, D. Solomatine, 2009 .

Remote Sensed and/or Global Datasets for Distributed Hydrological Modelling: A Review

D. Solomatine, I. Popescu, A. Jonoski, 2023, Remote. Sens..

Integrating Qualitative Flow Observations in a Lumped Hydrologic Routing Model

Maurizio Mazzoleni, A. Amaranto, Dimitri P. Solomatine, 2019, Water Resources Research.

Machine Learning Approach to Modeling Sediment Transport

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

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

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

The use of a flood index to characterise flooding in the north-eastern region of Bangladesh

D. Solomatine, B. Bhattacharya, S. Masud, 2016 .

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.

Learning from data for wind–wave forecasting

Ahmadreza Zamani, Arnold W. Heemink, A. R. Azimian, 2008 .

Spatio-Temporal Hydrological Model Structure and Parametrization Analysis

Dimitri Solomatine, Gerald Corzo Perez, Mostafa Farrag, 2021, Journal of Marine Science and Engineering.

Practical Experience of Sensitivity Analysis: Comparing Six Methods, on Three Hydrological Models, with Three Performance Criteria

Anqi Wang, Dimitri Solomatine, D. Solomatine, 2019, Water.

Randomised Search Optimisation Algorithms and Their Application in the Rehabilitation of Urban Drainage Systems

D. P. Solomatine, Z. Vojinovic, D. Solomatine, 2009 .

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

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

Integrating the flow regime and water quality effects into a niche-based metacommunity dynamics model for river ecosystems.

D. Solomatine, Liping Li, Xiaojing Zhang, 2023, Journal of environmental management.

A novel method to estimate model uncertainty using machine learning techniques

Dimitri Solomatine, Durga Lal Shrestha, D. Solomatine, 2009 .

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

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

Groundwater Remediation Strategy Using Global Optimization Algorithms

Dimitri P. Solomatine, Andreja Jonoski, Shreedhar Maskey, 2002 .

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 .

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 .

Predictive Data Mining : Practical Examples

Dimitri Solomatine, Slavco Velickov, D. Solomatine, 2000 .

Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting.

Dimitri Solomatine, J. Beckers, Albrecht Weerts, 2017 .

Entropy-Randomized Forecasting of Stochastic Dynamic Regression Models

Dimitri Solomatine, Yuri S. Popkov, Alexey Y. Popkov, 2020, Mathematics.

Exploring the potential of SRTM topographic data for flood inundation modelling under uncertainty

Dimitri Solomatine, Giuliano Di Baldassarre, Kun Yan, 2013 .

Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning

Dimitri Solomatine, Dimitri Solomatine, Yi Zheng, 2020, Geophysical Research Letters.

Application of artificial neural network in stage-discharge relationship

B. Bhattacharya, D. P. Solomatine, D. Solomatine, 2000 .

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

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

A review of low‐cost space‐borne data for flood modelling: topography, flood extent and water level

Dimitri Solomatine, Guy Schumann, Giuliano Di Baldassarre, 2015 .

Identifying major drivers of daily streamflow from large-scale atmospheric circulation with machine learning

D. Solomatine, A. Sorteberg, D. Lawrence, 2021 .

Citizen observations contributing to flood modelling: opportunities and challenges

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

Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan

Holger R. Maier, Robert J. Abrahart, Nick J. Mount, 2016 .

Assimilating uncertain, dynamic and intermittent streamflow observations in hydrological models

Leonardo Alfonso, Dimitri Solomatine, Maurizio Mazzoleni, 2015 .

AdaBoost.RT: a boosting algorithm for regression problems

D.P. Solomatine, D.L. Shrestha, D. Solomatine, 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

Meteorological Drought Forecasting Based on Climate Signals Using Artificial Neural Network – A Case Study in Khanhhoa Province Vietnam

Dimitri P. Solomatine, Gerald Corzo Perez, D. Solomatine, 2016 .

Hydrological and Erosion Modelling of the Brahmaputra Basin Using Global Datasets

Liton Chandra Mazumder, D. Solomatine, I. Masih, 2018 .

Development and evaluation of flood forecasting models for forecast-based financing using a novel model suitability matrix

Dimitri Solomatine, Albrecht Weerts, Patricia Trambauer, 2020, Progress in Disaster Science.

River flow forecasting using artificial neural networks

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

Model Induction with Support Vector Machines: Introduction and Applications

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

Data Assimilation in Hydrologic Routing: Impact of Model Error and Sensor Placement on Flood Forecasting

D. Solomatine, D. Seo, S. Noh, 2018, Journal of Hydrologic Engineering.

Exploring Assimilation of Crowdsourcing Observations into Flood Models

Leonardo Alfonso, Maurizio Mazzoleni, Dimitri P. Solomatine, 2019, The Handbook of Environmental Chemistry.

Random search methods in model calibration and pipe network design

D. P. Solomatine, D. Solomatine, 1999 .

Machine learning in sedimentation modelling

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

Spatio-temporal assessment of meteorological drought under the influence of varying record length: the case of Upper Blue Nile Basin, Ethiopia

Yared A. Bayissa, D. Solomatine, T. Tadesse, 2015 .

Comparison of the Performance of Six Drought Indices in Characterizing Historical Drought for the Upper Blue Nile Basin, Ethiopia

Tsegaye Tadesse, Dimitri Solomatine, Ann van Griensven, 2018 .

Data-driven modelling in the context of sediment transport

Roland K. Price, Dimitri P. Solomatine, Biswa Bhattacharya, 2005 .

Testing different cross-section spacing in 1D hydraulic modelling: a case study on Johor River, Malaysia

D. Solomatine, G. di Baldassarre, Anuar Md Ali, 2015 .

Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application

Dimitri Solomatine, Gerald Corzo, Amin Elshorbagy, 2009 .

Characterisation of the dynamics of past droughts.

D. Solomatine, H. V. Van Lanen, E. Varouchakis, 2019, The Science of the total environment.

Global re-analysis datasets to improve hydrological assessment and snow water equivalent estimation in a sub-Arctic watershed

D. Solomatine, A. Weerts, M. Werner, 2018, Hydrology and Earth System Sciences.

Improving Empirical Models with Machine Learning

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

Machine Learning and Committee Models for Improving ECMWF Subseasonal to Seasonal (S2S) Precipitation Forecast

D. Solomatine, G. Corzo, E. Varouchakis, 2023 .

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

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

Assessing the Impact of Precipitation Change on Design Annual Runoff in the Headwater Region of Yellow River, China

D. Solomatine, Z. Liang, H. M. Wang, 2019, Journal of Environmental Informatics.

Multivariate regression trees as an ‘ explainable machine learning ’ approach to exploring relationships between hydroclimatic characteristics and agricultural and hydrological drought severity

D. Solomatine, G. Corzo, Ana Paez-Trujilo, 2023 .

Citizen observatory of water as a data engine supporting the people-hydrology nexus: experience of the WeSenseIt project

D. Solomatine, F. Ciravegna, D. Norbiato, 2016 .

Impact of Dataset Size on the Signature-Based Calibration of a Hydrological Model

Safa A. Mohammed, D. Solomatine, M. Hrachowitz, 2021, Water.

A Stratified Sampling Approach for Improved Sampling from a Calibrated Ensemble Forecast Distribution

Dimitri Solomatine, Min Xu, Zhongmin Liang, 2016 .

Three-dimensional clustering in the characterization of spatiotemporal drought dynamics: cluster size filter and drought indicator threshold optimization

D. Solomatine, H. V. Van Lanen, G. C. Corzo Pérez, 2021 .

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

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

Development Of A Cloud Computing Application For Water Resources Modelling And Optimization Based On Open Source Software

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

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

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

Development of a cloud application for supporting water resources modeling

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

Cloud computing framework for a hydro information system

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

2.16 – Hydrological Modeling

D. P. Solomatine, D. Solomatine, 2011 .

Spatiotemporal Drought Analysis at Country Scale Through the Application of the STAND Toolbox

Dimitri Solomatine, Vitali Diaz, Gerald Corzo, 2019, Spatiotemporal Analysis of Extreme Hydrological Events.

Fuzzy-Committees of Conceptual distributed Models

D. Solomatine, G. C. Corzo Pérez, M. Farrag, 2021 .

Fuzzy-Committees of Conceptual distributed Model

D. Solomatine, G. C. Corzo Pérez, M. Farrag, 2021 .

Fuzzy committees of specialized rainfall-runoff models: Further enhancements and tests

Dimitri Solomatine, Fabrizio Fenicia, N. Kayastha, 2013 .

Encapsulation of parametric uncertainty statistics by various predictive machine learning models: MLUE method

Roland K. Price, Dimitri Solomatine, Nagendra Kayastha, 2014 .

Decision Support Framework for Optimal Reservoir Operation to Mitigate Cyanobacterial Blooms in Rivers

D. Solomatine, A. Jonoski, P. Goethals, 2023, Sustainability.

Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments

Dimitri P. Solomatine, Durga Lal Shrestha, Albrecht H. Weerts, 2014 .

Computational intelligence techniques in modeling water systems: some applications

D. P. Solomatine, D. Solomatine, 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

Combining semi-distributed process-based and data-driven models in flow simulation: A case study of the Meuse river basin

R. K. Price, Hidayat, Micha Werner, 2009 .

Combining Machine Learning and Domain Knowledge in Modular Modelling

D. P. Solomatine, D. Solomatine, 2009 .

2007 Special Issue: Knowledge-based modularization and global optimization of artificial neural network models in hydrological forecasting

D. Solomatine, G. Corzo, 2007 .

Combining multiple specialised models in flood context

Fabrizio Fenicia, Dimitri P. Solomatine, J. Ye, 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.

Space–time models for hydrological and environmental applications

Dionissios T. Hristopulos, Dimitri Solomatine, Emmanouil A. Varouchakis, 2020, Stochastic Environmental Research and Risk Assessment.

Studying the impact of infilling techniques on drought estimation — A case study in the South Central Region of Vietnam

D. Solomatine, V. Medina, G. C. Perez, 2017, 2017 Seventh International Conference on Information Science and Technology (ICIST).

A Novel Nested Dynamic Programming (nDP) Algorithm For Multipurpose Reservoir Optimization

Dimitri Solomatine, Blagoj Delipetrev, Andreja Jonoski, 2015 .

A novel nested stochastic dynamic programming (nSDP) and nested reinforcement learning (nRL) algorithm for multipurpose reservoir optimization

Dimitri Solomatine, Blagoj Delipetrev, Andreja Jonoski, 2017 .

Alternative configurations of quantile regression for estimating predictive uncertainty in water level forecasts for the upper Severn River: A comparison

Dimitri Solomatine, Albrecht Weerts, Jan Verkade, 2014 .

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

D. Solomatine, D. Shrestha, 2006, Neural Networks.

Web Based Access to Water Related Data Using OGC WaterML 2.0

Ioana Popescu, Andreja Jonoski, Adrian Almoradie, 2013 .

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

Robert J. Abrahart, Linda See, Elena Toth, 2007 .

Practical experience and framework for sensitivity analysis of hydrological models: six methods, three models, three criteria

D. Solomatine, Anqi Wang, 2018 .

Parametric uncertainty assessment of hydrological models: coupling UNEEC-P and a fuzzy general regression neural network

Mohsen Nasseri, Dimitri Solomatine, Arman Ahmadi, 2019, Hydrological Sciences Journal.

Data assimilation of satellite-based actual evapotranspiration in a distributed hydrological model of a controlled water system

Thomas Alexandridis, S. J. van Andel, I. M. Hartanto, 2017, Int. J. Appl. Earth Obs. Geoinformation.

Frequency analysis of Earth Observation and Hydrological model Evapotranspiration and Soil Moisture

I. M. Hartanto, D. Solomatine, T. Alexandridis, 2014 .

Application of global optimization to the design of pipe networks

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

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

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

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 in sedimentation modelling for the approach channel of the port area of Rotterdam

B. Bhattacharya, D. P. Solomatine, I. K. Deibel, 2007 .

Spatio-temporal Analysis of Hydrological Drought at Catchment Scale Using a Spatially-distributed Hydrological Model☆

D. Solomatine, H. V. Lanen, G. C. Perez, 2016 .

Mixtures of Simple Models vs ANNs in Hydrological Modeling

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

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 .