Urban Waterlogging Detection and Severity Prediction Using Artificial Neural Networks

Gupta, Apoorv; Bansal, Aman; Gupta, Rishab K.; Naryani, Deepika; Sood, Apoorvi

doi:10.1109/hpcc-smartcity-dss.2017.6

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…The works implementing ANN are characterized for its simple implementation to predict values since it is possible the generation of an effective model to solve different issues as these models have series of time organized including suitable variables. This is observed in studies carried out in [13,14,15,16], and [17], where the authors demonstrate that using ANN to predict floods is adequate as the Root of the Square Mean Error (RSME) oscillates between 0.0007540 and 0.93. These studies were focused on those characteristics that might transform an artificial neuronal network into a more effective one; for instance, the analysis undertaken by Johannet et al [18], concluded that the results were improved when having hidden layers in the network.…”

Section: Introductionmentioning

confidence: 84%

Use of computational intelligence techniques to predict flooding in places adjacent to the Magdalena River

Moreno

Sánchez

Espitia

2020

Heliyon

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Floods are one of the worst natural disasters in the world. Colombia is a country that has been greatly affected by this disaster. For example, in the years 2010 and 2011 there was a heavy rainy season, which caused floods that affected at least two million people and there were economic losses of 6.5 million dollars, which is equivalent to 5.7% of the country's Gross Domestic Product (GDP) at that time. The Magdalena River is the most important since 128 municipalities and 43 cities with a population of 6.3 million people, which is 13% of the total population of the country, are located in its basins. For this reason, the objective of the research is to design and implement a model that helps predict flooding over the Magdalena River by examining three techniques of artificial intelligence (Artificial Neuronal Networks, Adaptive Neuro Fuzzy Inference System, Support Vector Machine), and thus determining which of these techniques are the most effective according to the case study. The research was limited only to these three types, due to limitations of time, data, human and financial resources, and technological infrastructure. In the end, it is concluded that the Artificial Neural Networks technique is a suitable option to implement the predictive system as long as it is not very complex and does not require high processing machine. However, to establish a model based on rules to achieve a better interpretability of the floods, the ANFIS model can be used.

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Section: Introductionmentioning

confidence: 84%

Use of computational intelligence techniques to predict flooding in places adjacent to the Magdalena River

Moreno

Sánchez

Espitia

2020

Heliyon

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“…There are also some studies that have used hydrodynamic models such as SWMM and MIKE to simulate the process of urban waterlogging [28][29][30]. More and more scholars are establishing machine learning models such as SVM and ANN to study the susceptibility or impact mechanism of urban waterlogging [31][32][33]. For instance, Zhang et al [34] combined SCAM with HPA, discovering that extreme rainfall, impervious surface, and vegetation abundance are the main driving factors of urban waterlogging mechanism.…”

Section: Mechanisms and Characteristics Of Urban Communities Waterlog...mentioning

confidence: 99%

Predicting and Improving the Waterlogging Resilience of Urban Communities in China—A Case Study of Nanjing

Cui

Liu

et al. 2022

Buildings

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In recent years, urban communities in China have been continuously affected by extreme weather and emergencies, among which the rainstorm and waterlogging disasters pose a great threat to infrastructure and personnel safety. Chinese governments issue a series of waterlogging prevention and control policies, but the waterlogging prevention and mitigation of urban communities still needs to be optimized. The concept of “resilience” has unique advantages in the field of community disaster management, and building resilient communities can effectively make up for the limitations of the traditional top-down disaster management. Therefore, this paper focuses on the pre-disaster prevention and control of waterlogging in urban communities of China, following the idea of “concept analysis–influencing factor identification–evaluation indicators selection–impact mechanism analysis–resilience simulation prediction–empirical research–disaster adaptation strategy formulation”. The structural equation model and BP neural network are used by investigating the existing anti-waterlogging capitals of the target community to predict the future waterlogging resilience. Based on this simulation prediction model, and combined with the incentive and restraint mechanisms, suggestions on corrective measures can be put forward before the occurrence of waterlogging.

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“…The introduction of an acoustic rain gauge is, thus, justified, particularly, in contexts where it may be necessary to reduce the risks caused by sudden "showers" or "cloudbursts" with low operational investment, management and maintenance costs. Some application contexts concern smart cities for which is foreseen the integration of an audio sensor inside the luminaire of a street lamp, precision agriculture [15], with the advantage of being able to adapt the irrigation flows in a complementary way to different rain levels, as well as highway safety, by minimizing the risks of aquaplaning [16,17].…”

Section: Introductionmentioning

confidence: 99%

An Innovative Acoustic Rain Gauge Based on Convolutional Neural Networks

Avanzato

Beritelli

2020

Information

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An accurate estimate of rainfall levels is fundamental in numerous application scenarios: weather forecasting, climate models, design of hydraulic structures, precision agriculture, etc. An accurate estimate becomes essential to be able to warn of the imminent occurrence of a calamitous event and reduce the risk to human beings. Unfortunately, to date, traditional techniques for estimating rainfall levels present numerous critical issues. The algorithm applies the Convolution Neural Network (CNN) directly to the audio signal, using 3 s sliding windows with an offset of only 100 milliseconds. Therefore, by using low cost and low power hardware, the proposed algorithm allows implementing critical high rainfall event alerting mechanisms with short response times and low estimation errors. More specifically, this paper proposes a new approach to rainfall estimation based on the classification of different acoustic timbres that rain produces at different intensities and on CNN. The results obtained on seven classes ranging from “No rain” to “Cloudburst” indicate an average accuracy of 75%, which rises to 93% if the misclassifications of the adjacent classes are not considered. Some application contexts concern smart cities for which the integration of an audio sensor inside the luminaire of a street lamp is foreseen, precision agriculture, as well as highway safety, by minimizing the risks of aquaplaning.

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Urban Waterlogging Detection and Severity Prediction Using Artificial Neural Networks

Cited by 7 publications

References 8 publications

Use of computational intelligence techniques to predict flooding in places adjacent to the Magdalena River

Use of computational intelligence techniques to predict flooding in places adjacent to the Magdalena River

Predicting and Improving the Waterlogging Resilience of Urban Communities in China—A Case Study of Nanjing

An Innovative Acoustic Rain Gauge Based on Convolutional Neural Networks

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