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Applicability of Chemometric Methods for Estimating the Influence of Environmental Factors on the Water Quality on the Example of Some Lakes in Wolin National Park

100%

Bucior-Kwaczyńska A. , Miller T.

Rocznik Ochrona Środowiska

2019

tom Tom 21, cz. 1

421--438

Selected water quality indices collected form Czajcze and Domysłowsie Lakes in Wolin National Park in years 2000-2013 were studied. With the chemometric procedures an attempt was made to assess the impact of selected environmental factors in particular water level of lakes and monthly sum of precipitation on hydrochemistry of the lakes. It has been demonstrated that the level of lake waters can significantly shape the quality of the examined waters on the runoff of a river through lakes.

Oznaczano wybrane wskaźniki jakości wody zebrane z jezior Czajcze i Domysłowskie w Wolińskim Parku Narodowym w latach 2000-2013. Przy użyciu procedur chemometrycznych podjęto próbę oceny wpływu wybranych czynników środowiskowych, w szczególności poziomu wody w jeziorach, jak i miesięcznej sumy opadów, na hydrochemię jezior. Wykazano, że poziom wód jeziornych może znacząco wpłynąć na jakość badanych wód podczas spływu rzeki przez jeziora.

Utilizing recurrent neural networks in sustainable urban transport and logistics

70%

Durlik I. , Miller T. , Kostecka E. , Pusty T. , Łobodzińska A.

tom nr 78 (150)

131--149

Urban centers, replete with diverse amenities and opportunities, simultaneously grapple with the challenges brought on by rapid urbanization, notably in the realms of transport and logistics. A pivotal move towards energy-efficient and sustainable systems is essential to mitigate these challenges. In this landscape, machine learning (ML), and particularly recurrent neural networks (RNNs), emerge as powerful tools for effectively addressing these urban complexities. This comprehensive review zeroes in on the deployment of RNNs within sustainable urban transportation and logistics, highlighting their adeptness in processing sequential data, a critical component in various forecasting and optimization tasks. We commence with a foundational understanding of RNNs, segueing into their successful applications in urban transport and logistics. This review also critically examines the constraints of current methodologies and potential avenues for enhancement. We scrutinize the application of RNNs across several areas, encompassing the energy shift in both passenger and freight transport, logistics management, integration of low- and zero-emission vehicles, and the energy dynamics of transport and logistics. Additionally, the role of RNNs in traffic and infrastructure planning is explored, particularly in forecasting traffic flow, congestion patterns, and optimizing energy usage. The crux of this review is to amalgamate and present the existing knowledge on the instrumental role of RNNs in facilitating the transition to energy-efficient urban transportation and logistics. Our goal is to highlight effective strategies, pinpoint challenges, and map out avenues for future research in this domain.