Optimizing Rainwater Harvesting Systems for Non-potable Water Uses and Surface Runoff Mitigation

Palermo, Stefania Anna; Talarico, Valerio; Pirouz, Behrouz

doi:10.1007/978-3-030-39081-5_49

Cited by 21 publications

(14 citation statements)

References 23 publications

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“…Artificial Intelligence includes a wide range of methods and algorithms that work based on machine intelligence and has many applications in various fields of science [40], including fuzzy logic theory and application [41][42][43][44][45][46], artificial intelligence techniques and sociology [47][48][49], risk assessment and hazard identification [50,51], machine learning [52][53][54][55][56][57], and meta-heuristic algorithms and clustering techniques [58][59][60][61][62]. The group method of data handling (GMDH) type of neural network is one of these algorithms that was proposed by Ivakhnenko [63].…”

Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis

et al. 2020

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Nowadays, sustainable development is considered a key concept and solution in creating a promising and prosperous future for human societies. Nevertheless, there are some predicted and unpredicted problems that epidemic diseases are real and complex problems. Hence, in this research work, a serious challenge in the sustainable development process was investigated using the classification of confirmed cases of COVID-19 (new version of Coronavirus) as one of the epidemic diseases. Hence, binary classification modeling was used by the group method of data handling (GMDH) type of neural network as one of the artificial intelligence methods. For this purpose, the Hubei province in China was selected as a case study to construct the proposed model, and some important factors, namely maximum, minimum, and average daily temperature, the density of a city, relative humidity, and wind speed, were considered as the input dataset, and the number of confirmed cases was selected as the output dataset for 30 days. The proposed binary classification model provides higher performance capacity in predicting the confirmed cases. In addition, regression analysis has been done and the trend of confirmed cases compared with the fluctuations of daily weather parameters (wind, humidity, and average temperature). The results demonstrated that the relative humidity and maximum daily temperature had the highest impact on the confirmed cases. The relative humidity in the main case study, with an average of 77.9%, affected positively, and maximum daily temperature, with an average of 15.4 • C, affected negatively, the confirmed cases.

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Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis

et al. 2020

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“…In order to assess uncertain problems, soft computing techniques can be applied with a satisfactory performance for modeling some problems involved in engineering [39][40][41][42][43][44][45][46]. The ABC algorithm is one of the most widely used evolutionary methods in the area of soft computing.…”

Section: Methodsmentioning

confidence: 99%

Assessment of risks of tunneling project in Iran using artificial bee colony algorithm

et al. 2019

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The soft computing techniques have been widely applied to model and analyze the complex and uncertain problems. This paper aims to develop a novel model for the risk assessment of tunneling projects using artificial bee colony algorithm. To this end, the risk of the second part of the Emamzade Hashem tunnel was assessed and analyzed in seven sections after testing geotechnical characteristics. Five geotechnical and hydrological properties of study zone are considered for the clustering of geological units in front of tunneling project including length of tunnel, uniaxial compressive strength, rock mass rating, tunneling index Q, density and underground water condition. These sections were classified in two lowrisk and high-risk groups based on their geotechnical characteristics and using clustering technique. It was resulted that three sections with lithologies Durood Formation, Mobarak Formation, and Ruteh Formation are placed in the high risk group and the other sections with lithologies Baroot Formation, Elika Formation, Dacite tuff of Eocene, and Shear Tuff, and Lava Eocene are placed in the low risk group. In addition, the underground water condition and density with 0.722 and 1 Euclidean distances have the highest and lowest impacts in the high risk group, respectively. Therefore, comparing the obtained results of modelling and actual excavation data demonstrated that this technique can be applied as a powerful tool for modeling risks of tunnel and underground constructions.

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“…Mathematical modeling and statistical analysis are widely used in various fields, e.g., natural and human hazards [41][42][43][44][45], and for many phenomena, including hazards and optimization [46][47][48]. Thus, it is essential to express the problem in the form of an equation or a model [49][50][51][52][53][54][55].…”

Section: Mathematical Modeling and Statistical Analysismentioning

confidence: 99%

The Role of Artificial Intelligence, MLR and Statistical Analysis in Investigations about the Correlation of Swab Tests and Stress on Health Care Systems by COVID-19

2020

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The outbreak of the new Coronavirus (COVID-19) pandemic has prompted investigations on various aspects. This research aims to study the possible correlation between the numbers of swab tests and the trend of confirmed cases of infection, while paying particular attention to the sickness level. The study is carried out in relation to the Italian case, but the result is of more general importance, particularly for countries with limited ICU (intensive care units) availability. The statistical analysis showed that, by increasing the number of tests, the trend of home isolation cases was positive. However, the trend of mild cases admitted to hospitals, intensive case cases, and daily deaths were all negative. The result of the statistical analysis provided the basis for an AI study by ANN. In addition, the results were validated using a multivariate linear regression (MLR) approach. Our main result was to identify a significant statistical effect of a reduction of pressure on the health care system due to an increase in tests. The relevance of this result is not confined to the COVID-19 outbreak, because the high demand of hospitalizations and ICU treatments due to this pandemic has an indirect effect on the possibility of guaranteeing an adequate treatment for other high-fatality diseases, such as, e.g., cardiological and oncological ones. Our results show that swab testing may play a significant role in decreasing stress on the health system. Therefore, this case study is relevant, in particular, for plans to control the pandemic in countries with a limited capacity for admissions to ICU units.

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Optimizing Rainwater Harvesting Systems for Non-potable Water Uses and Surface Runoff Mitigation

Cited by 21 publications

References 23 publications

Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis

Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis

Assessment of risks of tunneling project in Iran using artificial bee colony algorithm

The Role of Artificial Intelligence, MLR and Statistical Analysis in Investigations about the Correlation of Swab Tests and Stress on Health Care Systems by COVID-19

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