Jaroslaw J. Napiorkowski

发表

Product-Units neural networks for catchment runoff forecasting

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, 2012 .

Population size in Particle Swarm Optimization

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Agnieszka Piotrowska, 2020, Swarm Evol. Comput..

Comparison of evolutionary computation techniques for noise injected neural network training to estimate longitudinal dispersion coefficients in rivers

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Pawel M. Rowinski, 2012, Expert Syst. Appl..

The Volterra series as special case of artificial neural network model

Jaroslaw J. Napiorkowski, J. P. O Kane, 2003 .

Optimizing neural networks for river flow forecasting – Evolutionary Computation methods versus the Levenberg–Marquardt approach

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, J. Napiorkowski, 2011 .

Some metaheuristics should be simplified

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, J. Napiorkowski, 2018, Inf. Sci..

Global Optimisation Method for Determination of Reservoir Decision Rules During Flood

Jaroslaw J. Napiorkowski, Tomasz Dysarz, 2002 .

A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modelling

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, J. Napiorkowski, 2013 .

Determination of reservoir decision rules during flood

Jaroslaw J. Napiorkowski, Tomasz Dysarz, 2002 .

A Study of Flow Conditions Aimed at Preserving Valuable Wetland Areas in the Upper Narew Valley Using GSA-GLUE Methodology

Adam Kiczko, Jaroslaw J. Napiorkowski, Renata J. Romanowicz, 2007, EnviroInfo.

Step-by-step improvement of JADE and SHADE-based algorithms: Success or failure?

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, J. Napiorkowski, 2018, Swarm Evol. Comput..

Forty Years of Water Research at the Institute of Geophysics, Polish Academy of Sciences

Jaroslaw J. Napiorkowski, Witold G. Strupczewski, Robert Józef Bialik, 2014 .

One-Dimensional Modeling of Flows in Open Channels

Romuald Szymkiewicz, Jaroslaw J. Napiorkowski, Dariusz Gąsiorowski, 2015 .

Application of deterministic chaos and neural networks in water reservoir management

Jaroslaw J. Napiorkowski, Tomasz Terlikowski, 2001 .

Impact of deep learning-based dropout on shallow neural networks applied to stream temperature modelling

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Agnieszka Piotrowska, 2020 .

Searching for structural bias in particle swarm optimization and differential evolution algorithms

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, J. Napiorkowski, 2016, Swarm Intelligence.

Flash-flood forecasting by means of neural networks and nearest neighbour approach – a comparative study

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Paweł M. Rowiński, 2006 .

Dispersion Processes in Wetlands - Application of Data Based Mechanistic and Transient Storage Models

Jaroslaw J. Napiorkowski, Renata J. Romanowicz, Marzena Osuch, 2007, EnviroInfo.

How novel is the "novel" black hole optimization approach?

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Pawel M. Rowinski, 2014, Inf. Sci..

Comparing large number of metaheuristics for artificial neural networks training to predict water temperature in a natural river

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Pawel M. Rowinski, 2014, Comput. Geosci..

Are modern metaheuristics successful in calibrating simple conceptual rainfall–runoff models?

Zbigniew W. Kundzewicz, Adam P. Piotrowski, Jaroslaw J. Napiorkowski, 2017 .

Artificial neural networks as an alternative to the Volterra series in rainfall-runoff modelling

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, J. Napiorkowski, 2005 .

Stream temperature forecasting by means of ensemble of neural networks: Importance of input variables and ensemble size

Jaroslaw J. Napiorkowski, Maciej J. Napiorkowski, Adam T. Piotrowski, 2014 .

Swarm Intelligence and Evolutionary Algorithms: Performance versus speed

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Pawel M. Rowinski, 2017, Inf. Sci..

On the importance of training methods and ensemble aggregation for runoff prediction by means of artificial neural networks

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Marzena Osuch, 2016 .

On-line updating of a distributed flow routing model - River Vistula case study

Jaroslaw J. Napiorkowski, Renata J. Romanowicz, Emilia Karamuz, 2015 .

Are Evolutionary Algorithms Effective in Calibrating Different Artificial Neural Network Types for Streamwater Temperature Prediction?

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Marzena Osuch, 2016, Water Resources Management.

Performance of the air2stream model that relates air and stream water temperatures depends on the calibration method

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, J. Napiorkowski, 2018, Journal of Hydrology.

Corrigendum to: "Differential evolution algorithm with separated groups for multi-dimensional optimization problems" [Eur. J. Oper. Res. 216 (2012) 33-46]

Adam Kiczko, Adam P. Piotrowski, Jaroslaw J. Napiorkowski, 2012, Eur. J. Oper. Res..

Stochastic transfer function model applied to combined reservoir management and flow routing

Adam Kiczko, Jaroslaw J. Napiorkowski, Renata J. Romanowicz, 2010 .

Differential Evolution algorithm with Separated Groups for multi-dimensional optimization problems

Adam Kiczko, Adam P. Piotrowski, Jaroslaw J. Napiorkowski, 2012, Eur. J. Oper. Res..

Input dropout in product unit neural networks for stream water temperature modelling

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Agnieszka E. Piotrowska, 2021, Journal of Hydrology.

Monthly runoff generation by non-linear model using multispectral and multitemporal satellite imagery

Gert A. Schultz, Jaroslaw J. Napiorkowski, I. Papadakis, 1993 .

Applicability of diffusion analogy in flood routing

James C. I. Dooge, Jaroslaw J. Napiorkowski, 1988 .

Integration of Reservoir Management and Flow Routing Model Upper Narew Case Study

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Renata J. Romanowicz, 2008 .

Evaluation of temporal concentration profiles for ungauged rivers following pollution incidents

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Paweł M. Rowiński, 2011 .

The grouping differential evolution algorithm for multi-dimensional optimization problems

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, J. Napiorkowski, 2010 .

Hydrodynamic derivation of storage parameters of the Muskingum model

Jaroslaw J. Napiorkowski, Witold G. Strupczewski, James C. I. Dooge, 1982 .

Comparing various artificial neural network types for water temperature prediction in rivers

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, Marzena Osuch, 2015 .

Run-off modelling in an Arctic unglaciated catchment (Fuglebekken, Spitsbergen)

Jaroslaw J. Napiorkowski, Marzena Osuch, Tomasz Wawrzyniak, 2017, Annals of Glaciology.

THE EFFECT OF THE DOWNSTREAM BOUNDARY CONDITIONS IN THE LINEARIZED ST VENANT EQUATIONS

Jaroslaw J. Napiorkowski, J. Napiorkowski, J. Dooge, 1987 .

Simple modifications of the nonlinear regression stream temperature model for daily data

Adam P. Piotrowski, Jaroslaw J. Napiorkowski, J. Napiorkowski, 2019, Journal of Hydrology.

A Discrete Conceptualization of a Volterra Series Model for Surface Runoff

Zbigniew W. Kundzewicz, Jaroslaw J. Napiorkowski, 1986 .

Are artificial neural network techniques relevant for the estimation of longitudinal dispersion coefficient in rivers? / Les techniques de réseaux de neurones artificiels sont-elles pertinentes pour estimer le coefficient de dispersion longitudinale en rivières?

Jaroslaw J. Napiorkowski, J. Napiorkowski, P. Rowiński, 2005 .

Modelling of the thermal regime of permafrost during 1990–2014 in Hornsund, Svalbard

Jaroslaw J. Napiorkowski, Marzena Osuch, Sebastian Westermann, 2016 .