A. Piotrowski

发表

River/stream water temperature forecasting using artificial intelligence models: a systematic review

Senlin Zhu, A. Piotrowski, 2020, Acta Geophysica.

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

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

Relationship Between Calibration Time and Final Performance of Conceptual Rainfall-Runoff Models

J. Napiorkowski, A. Piotrowski, M. Osuch, 2018, Water Resources Management.

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

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

Uncertainty study of data-based models of pollutant transport in rivers

A. Piotrowski, P. Rowiński, 2010 .

Evaluation of 1-D tracer concentration profile by ANN

S. G. Wallis, S. Wallis, J. Napiorkowski, 2007 .

Are Artificial Neural Network Techniques Relevant for the Estimation of Longitudinal Dispersion Coefficient in Rivers

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

DISPERSION COEFFICIENT ASSESSMENT BY MEANS OF DIFFERENT NEURAL NETWORKS

J. Napiorkowski, A. Piotrowski, 2005 .

PRODUCT UNIT NEURAL NETWORKS FOR ESTIMATIONS OF LONGITUDINAL DISPERSION COEFFICIENTS IN RIVERS

S. Wallis, J. Napiorkowski, A. Piotrowski, 2012 .

The grouping differential evolution algorithm for multi-dimensional optimization problems

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

River flow forecast by means of selected black box models

A. Piotrowski, 2004 .

Differential evolution and particle swarm optimization against COVID-19

Agnieszka E. Piotrowska, A. Piotrowski, 2021, Artif. Intell. Rev..

Calibration of conceptual rainfall-runoff models by selected differential evolution and particle swarm optimization variants

J. Napiorkowski, A. Piotrowski, T. Senbeta, 2022, Acta Geophysica.

Evaluation of 1-D tracer concentration profile in a small river by means of Multi-Layer Perceptron Neural Networks

S. Wallis, J. Napiorkowski, A. Piotrowski, 2007 .

Influence of the choice of stream temperature model on the projections of water temperature in rivers

J. Napiorkowski, A. Piotrowski, M. Osuch, 2021 .

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.

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

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

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 .

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

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

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..

Differential Evolution algorithms applied to Neural Network training suffer from stagnation

Adam P. Piotrowski, A. Piotrowski, 2014, Appl. Soft Comput..

Joint Optimization of Conceptual Rainfall-Runoff Model Parameters and Weights Attributed to Meteorological Stations

Adam P. Piotrowski, Marzena Osuch, Jarosław J. Napiorkowski, 2019, Water Resources Management.

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

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

L-SHADE optimization algorithms with population-wide inertia

Adam P. Piotrowski, A. Piotrowski, 2018, Inf. Sci..

Population size in Particle Swarm Optimization

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

Novel Air2water Model Variant for Lake Surface Temperature Modeling With Detailed Analysis of Calibration Methods

J. Napiorkowski, Senlin Zhu, A. Piotrowski, 2023, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

Swarm Intelligence and Evolutionary Algorithms: Performance versus speed

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

Review of Differential Evolution population size

Adam P. Piotrowski, A. Piotrowski, 2017, Swarm Evol. Comput..

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

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

How Much Do Swarm Intelligence and Evolutionary Algorithms Improve Over a Classical Heuristic From 1960?

Agnieszka E. Piotrowska, J. Napiorkowski, A. Piotrowski, 2023, IEEE Access.

Particle Swarm Optimization or Differential Evolution - A comparison

Agnieszka E. Piotrowska, J. Napiorkowski, A. Piotrowski, 2023, Eng. Appl. Artif. Intell..

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

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

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..

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 .

Regarding the rankings of optimization heuristics based on artificially-constructed benchmark functions

Adam P. Piotrowski, A. Piotrowski, 2015, Inf. Sci..

Adaptive Memetic Differential Evolution with Global and Local neighborhood-based mutation operators

Adam P. Piotrowski, A. Piotrowski, 2013, Inf. Sci..

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..

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 .

PREDICTION OF THE FATE OF POLLUTANTS IN RIVERS BY MEANS OF NONLINEAR VOLTERRA SERIES

J. Napiorkowski, A. Piotrowski, P. Rowiński, 2008 .

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

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

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

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

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.

Across Neighborhood Search algorithm: A comprehensive analysis

Adam P. Piotrowski, A. Piotrowski, 2018, Inf. Sci..

Some metaheuristics should be simplified

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

May the same numerical optimizer be used when searching either for the best or for the worst solution to a real-world problem?

Adam P. Piotrowski, Maciej J. Napiorkowski, A. Piotrowski, 2016, Inf. Sci..

Choice of benchmark optimization problems does matter

Agnieszka E. Piotrowska, J. Napiorkowski, A. Piotrowski, 2023, Swarm and Evolutionary Computation.

LARGE SCALE TRACER STUDY OF MIXING IN A NATURAL LOWLAND RIVER

Ian Guymer, Paweł M. Rowiński, Adam Piotrowski, 2007 .

How does the calibration method impact the performance of the air2water model for the forecasting of lake surface water temperatures?

J. Napiorkowski, Senlin Zhu, M. Ptak, 2021 .