Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda

Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

Leakage detection in water pipelines using supervised classification of acceleration signals

Smartening sustainable development in cities: strengthening the theoretical linkage between smart cities and SDGs

The last decade has seen the development of complex IT systems to support city management, i.e., the creation of so-called intelligent cities. These systems include modules dedicated to particular branches of municipal economy, such as urban transport, heating systems, energy systems, telecommunications, and finally water and sewage management. In turn, with regard to the latter branch, IT systems supporting the management of water supply and sewage networks and sewage treatment plants are being developed. This paper deals with the system concerning the urban water supply network, and in particular, with the subsystem for detecting and locating leakages on the water supply network, including so-called hidden leakages. These leaks cause the greatest water losses in networks, especially in old ones, with a very diverse age and material structure. In the proposed concept of the subsystem consisting of a GIS (Geographical Information System), SCADA (Supervisory Control and Data Acquisition) system and hydraulic model of the water supply network, an algorithm of leak detection and location based on the neural networks’ MLP (multi-layer perceptron) and Kohonen was developed. The algorithm has been tested on the hydraulic models of several municipal water supply networks.

https://www.mdpi.com/2071-1050/11/2/518/pdf/1

Detection and Localization of Water Leaks in Water Nets Supported by an ICT System with Artificial Intelligence Methods as a Way Forward for Smart Cities

http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.baztech-d4e78b27-a393-45e5-b9db-9d2388f8d881/c/Rojek_eng.pdf

Comparison of different types of neuronal nets for failures location within water-supply networks

. One of the key parameters constituting the basis for the
operational assessment of stormwater systems is the annual number of storm
overflows. Since uncontrolled overflows are a source of pollution washed
away from the surface of the catchment area, which leads to imbalanced
receiving waters, there is a need for their prognosis and potential
reduction. The paper presents a probabilistic model for simulating the
annual number of storm overflows. In this model, an innovative solution is
to use the logistic regression method to analyze the impact of rainfall
genesis on the functioning of a storm overflow (OV) in the example of a catchment
located in the city of Kielce (central Poland). The developed model consists of two independent elements. The first element
of the model is a synthetic precipitation generator, in which the simulation
of rainfall takes into account its genesis resulting from various processes
and phenomena occurring in the troposphere. This approach makes it possible
to account for the stochastic nature of rainfall in relation to the annual
number of events. The second element is the model of logistic regression,
which can be used to model the storm overflow resulting from the occurrence
of a single rainfall event. The paper confirmed that storm overflow can be
modeled based on data on the total rainfall and its duration. An
alternative approach was also proposed, providing the possibility of
predicting storm overflow only based on the average rainfall intensity.
Substantial simplification in the simulation of the phenomenon under study
was achieved compared with the works published in this area to date. It is
worth noting that the coefficients determined in the logit models have a
physical interpretation, and the universal character of these models
facilitates their easy adaptation to other examined catchment areas. The calculations made in the paper using the example of the examined
catchment allowed for an assessment of the influence of rainfall characteristics
(depth, intensity, and duration) of different genesis on the probability of
storm overflow. Based on the obtained results, the range of the variability
of the average rainfall intensity, which determines the storm overflow, and
the annual number of overflows resulting from the occurrence of rain of
different genesis were defined. The results are suited for the
implementation in the assessment of storm overflows only based on the
genetic type of rainfall. The results may be used to develop warning systems
in which information about the predicted rainfall genesis is an element of
the assessment of the rainwater system and its facilities. This approach is
an original solution that has not yet been considered by other researchers.
On the other hand, it represents an important simplification and an
opportunity to reduce the amount of data to be measured.

/pdf/application-of-logistic-regression-to-simulate-the-influence-12z9e6cx9d.pdf

Application of logistic regression to simulate the influence of rainfall genesis on storm overflow operations: a probabilistic approach

The processes that affect sediment quality in drainage systems show high dynamics and complexity. However, relatively little information is available on the influence of both catchment characteristics and meteorological conditions on sediment chemical properties, as those issues have not been widely explored in research studies. This paper reports the results of investigations into the content of selected heavy metals (Ni, Mn, Co, Zn, Cu, Pb, and Fe) and polycyclic aromatic hydrocarbons (PAHs) in sediments from the stormwater drainage systems of four catchments located in the city of Kielce, Poland. The influence of selected physico-geographical catchment characteristics and atmospheric conditions on pollutant concentrations in the sediments was also analyzed. Based on the results obtained, statistical models for forecasting the quality of stormwater sediments were developed using artificial neural networks (multilayer perceptron neural networks). The analyses showed varied impacts of catchment characteristics and atmospheric conditions on the chemical composition of sediments. The concentration of heavy metals in sediments was far more affected by catchment characteristics (land use, length of the drainage system) than atmospheric conditions. Conversely, the content of PAHs in sediments was predominantly affected by atmospheric conditions prevailing in the catchment. The multilayer perceptron models developed for this study had satisfactory predictive abilities; the mean absolute error of the forecast (Ni, Mn, Zn, Cu, and Pb) did not exceed 21%. Hence, the models show great potential, as they could be applied to, for example, spatial planning for which environmental aspects (i.e., sediment quality in the stormwater drainage systems) are accounted.

https://www.mdpi.com/2073-4441/11/3/626/pdf

Modeling of Heavy Metal (Ni, Mn, Co, Zn, Cu, Pb, and Fe) and PAH Content in Stormwater Sediments Based on Weather and Physico-Geographical Characteristics of the Catchment-Data-Mining Approach

/pdf/environmental-informatics-and-systems-research-3ovz95ped9.pdf

Jan Studziński

Papers

Detection and Localization of Water Leaks in Water Nets Supported by an ICT System with Artificial Intelligence Methods as a Way Forward for Smart Cities

Comparison of different types of neuronal nets for failures location within water-supply networks

Application of logistic regression to simulate the influence of rainfall genesis on storm overflow operations: a probabilistic approach

Modeling of Heavy Metal (Ni, Mn, Co, Zn, Cu, Pb, and Fe) and PAH Content in Stormwater Sediments Based on Weather and Physico-Geographical Characteristics of the Catchment-Data-Mining Approach

Environmental Informatics and Systems Research