scholarly journals Sewer Condition Prediction and Analysis of Explanatory Factors

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1239 ◽  
Author(s):  
Tuija Laakso ◽  
Teemu Kokkonen ◽  
Ilkka Mellin ◽  
Riku Vahala
Keyword(s):  
Random Forest ◽  
Predictor Variables ◽  
Network Data ◽  
Random Forest Algorithm ◽  
Poor Condition ◽  
Explanatory Factors ◽  
The Impact ◽  
Partial Dependence ◽  
Boruta Algorithm ◽  
Insight Into

Sewer condition is commonly assessed using closed-circuit television (CCTV) inspections. In this paper, we combine inspection results, pipe attributes, network data, and data on pipe environment to predict pipe condition and to discover which factors affect it. We apply the random forest algorithm to model pipe condition and assess the variable importance using the Boruta algorithm. We analyse the impact of predictor variables on poor condition using partial dependence plots, which are a valuable technique for this purpose. The results can be used in screening pipes for future inspections and provide insight into the dynamics between predictor variables and poor condition.

scholarly journals The Impact of Economic Crisis in Areas of Sprawl in Spanish Cities

Urban Science ◽  
2018 ◽  
Vol 2 (4) ◽  
pp. 113 ◽  
Author(s):  
Arlinda García-Coll ◽  
Cristina López-Villanueva
Keyword(s):  
Economic Crisis ◽  
Residential Mobility ◽  
Metropolitan Region ◽  
Life Stages ◽  
Middle Term ◽  
The Real Estate ◽  
Suburban Areas ◽  
Explanatory Factors ◽  
The Impact ◽  
Insight Into

The development of dispersed urbanism in Spain ran parallel to the real estate boom and consolidated a new model of city sprawl based on the expansion of suburban areas. This process, which started in the mid 1980s, came to a halt with the onset of the economic crisis in 2007. With it, construction stopped, mobility fell, and urban growth came to a standstill. The purpose of this article is, firstly, to analyse the recent evolution and chronology of the expansion of dispersed urbanism in the Barcelona Metropolitan Region (BMR) in order to gain an insight into some of its explanatory factors, and secondly, to look into the future middle-term prospects of dispersed urbanism in the BMR and Spain. To this end, we examine trends in the housing market and residential mobility and take stock of the impact of business cycles on them. The conclusion is that dispersed areas still retain their appeal for people in the life stages of the creation and expansion of households. For this reason, an effective economic recovery and a renewed rise in the price of housing in denser cities may contribute to an upturn in the popularity of the dispersed residential model, which nowadays could be considered to be in a ‘lethargic’ phase, waiting for certain factors to concur and reactivate its expansion.

Towards Food Security: the Prediction of Climatic Factors in Nigeria using Random Forest Approach

2021 ◽  
pp. 70-81
Author(s):  
Charity Ojochogwu Egbunu ◽  
Matthew Tunde Ogedengbe ◽  
Terungwa Simon Yange ◽  
Terlumun Gbaden ◽  
Malik Adeiza Rufai ◽  
...  
Keyword(s):  
Food Security ◽  
Random Forest ◽  
Food Insecurity ◽  
Food Production ◽  
Negative Impact ◽  
Climatic Factors ◽  
Climatic Conditions ◽  
Random Forest Algorithm ◽  
The Sustainable Development ◽  
The Impact

With the explosive growth in the world’s population which has little or no corresponding rise in the food production, food insecurity has become eminent, and hence, the need to seek for opportunities to increase food production in order to cater for this population is paramount. The second goal of the Sustainable Development Goals (SDGs) (i.e., ending hunger, achieving food security and improved nutrition, and promoting sustainable agriculture) set by the United Nations (UN) for the year 2030 clearly acknowledged this fact. Improving food production cannot be achieved using the obsolete conventional methods of agriculture by our farmers; hence, this study focuses on developing a model for predicting climatic conditions with a view to reducing their negative impact, and boosting the yield of crop. Temperature, wind, humidity and rainfall were considered as the effect of these factors is more devastating in Nigeria as compared to sun light which is always in abundance. We implemented random forest algorithm using Python programming language to predict the aforementioned climate parameters. The data used was gotten from the Nigerian Meteorological (NiMet) Agency, Lokoja, Kogi State between 1988 and 2018. The result shows that random forest algorithm is effective in climate prediction as the accuracy from the model based on the climatic factors considered was 94.64%. With this, farmers would be able to plan ahead to prevent the impact of the fluctuations in these climatic factors; thus, the yield of crops would be increased. This would dwarf the negative impact of food insecurity to the populace.

scholarly journals The Impact of Economic Crisis in Areas of Sprawl in Spanish Cities

2018 ◽  
Author(s):  
Arlinda García-Coll ◽  
Cristina López-Villanueva
Keyword(s):  
Economic Crisis ◽  
Residential Mobility ◽  
Metropolitan Region ◽  
Future Prospects ◽  
Middle Term ◽  
The Real Estate ◽  
Suburban Areas ◽  
Explanatory Factors ◽  
The Impact ◽  
Insight Into

The development of dispersed urbanism in Spain ran parallel to the real estate boom and consolidated a new model of city sprawl based on the expansion of suburban areas. This process, which started in the mid 1980s, came to a halt with the onset of the economic crisis in 2007. With it, construction stopped, mobility fell and urban growth came to a standstill. The purpose of this article is to carry out an analysis of the recent evolution and chronology of the expansion of dispersed urbanism in the Barcelona Metropolitan Region (BMR) in order to gain an insight into some of the explanatory factors of such expansion and to deal with the future prospects of middle-term development of dispersed urbanism in the BMR and in Spain. To do this, we examine the trends in the housing market, in residential mobility and we take stock of the impact of business cycles. The conclusion is that dispersed areas retain their appeal in the stages of creation and expansion of households. For this reason, an effective economic recovery and a renewed rise in the price of housing in denser cities may contribute to an upturn in the popularity of the dispersed residential model, which nowadays could be considered to be in a ‘lethargic’ stage, waiting for certain factors to coincide and re-activate its expansion.

Analyzing Opera Attendance: The Relative Impact of Managerial Vs. Environmental Variables

1986 ◽  
Vol 4 (2) ◽  
pp. 175-188 ◽  
Author(s):  
Susan L. Holak ◽  
William J. Havlena ◽  
Pamela K. Kennedy
Keyword(s):  
Predictive Validity ◽  
Environmental Variables ◽  
Predictor Variables ◽  
Forecasting Model ◽  
Relative Impact ◽  
Relative Importance ◽  
Environmental Characteristics ◽  
Using Data ◽  
The Impact ◽  
Insight Into

A forecasting model of opera attendance was used to assess the relative importance of two categories of predictor variables: performance attributes and environmental characteristics. Separate analyses for subscribers and nonsubscribers yielded insight into the differences between the two groups concerning the impact of repertory and scheduling on attendance. While subscribers were most heavily influenced by timing, nonsubscriber attendance was also influenced by the familiarity of the opera. The predictive validity of the model was evaluated using data from the most recent season's performances.

scholarly journals Open Application of Statistical and Machine Learning Models to Explore the Impact of Environmental Exposures on Health and Disease: An Asthma Use Case

2021 ◽  
Vol 18 (21) ◽  
pp. 11398
Author(s):  
Bo Lan ◽  
Perry Haaland ◽  
Ashok Krishnamurthy ◽  
David B. Peden ◽  
Patrick L. Schmitt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Environmental Exposures ◽  
Predictor Variables ◽  
Use Case ◽  
Airborne Particulate ◽  
Particulate Exposure ◽  
The Impact

ICEES (Integrated Clinical and Environmental Exposures Service) provides a disease-agnostic, regulatory-compliant approach for openly exposing and analyzing clinical data that have been integrated at the patient level with environmental exposures data. ICEES is equipped with basic features to support exploratory analysis using statistical approaches, such as bivariate chi-square tests. We recently developed a method for using ICEES to generate multivariate tables for subsequent application of machine learning and statistical models. The objective of the present study was to use this approach to identify predictors of asthma exacerbations through the application of three multivariate methods: conditional random forest, conditional tree, and generalized linear model. Among seven potential predictor variables, we found five to be of significant importance using both conditional random forest and conditional tree: prednisone, race, airborne particulate exposure, obesity, and sex. The conditional tree method additionally identified several significant two-way and three-way interactions among the same variables. When we applied a generalized linear model, we identified four significant predictor variables, namely prednisone, race, airborne particulate exposure, and obesity. When ranked in order by effect size, the results were in agreement with the results from the conditional random forest and conditional tree methods as well as the published literature. Our results suggest that the open multivariate analytic capabilities provided by ICEES are valid in the context of an asthma use case and likely will have broad value in advancing open research in environmental and public health.

The Random Forest Method in Research of Impact of Macroeconomic Indicators of Regional Development on Informal Employment Rate

2020 ◽  
Vol 27 (6) ◽  
pp. 37-55
Author(s):  
E. V. Zarova ◽  
E. I. Dubravskaya
Keyword(s):  
Random Forest ◽  
Regional Development ◽  
Russian Federation ◽  
Regional Level ◽  
Informal Employment ◽  
Macroeconomic Factors ◽  
Random Forest Method ◽  
Macroeconomic Indicators ◽  
The Russian Federation ◽  
The Impact

The topic of quantitative research on informal employment has a consistently high relevance both in the Russian Federation and in other countries due to its high dependence on cyclicality and crisis stages in economic dynamics of countries with any level of economic development. Developing effective government policy measures to overcome the negative impact of informal employment requires special attention in theoretical and applied research to assessing the factors and conditions of informal employment in the Russian Federation including at the regional level. Such effects of informal employment as a shortfall in taxes, potential losses in production efficiency, and negative social consequences are a concern for the authorities of the federal and regional levels. Development of quantitative indicators to determine the level of informal employment in the regions, taking into account their specifics in the general spatial and economic system of Russia are necessary to overcome these negative effects. The article proposes and tests methods for solving the problem of assessing the impact of hierarchical relationships on macroeconomic factors at the regional level of informal employment in constituent entities of the Russian Federation. Majority of the works on the study of informal employment are based on basic statistical methods of spatial-dynamic analysis, as well as on the now «traditional» methods of cluster and correlation-regression analysis. Without diminishing the merits of these methods, it should be noted that they are somewhat limited in identifying hidden structural connections and interdependencies in such a complex multidimensional phenomenon as informal employment. In order to substantiate the possibility of overcoming these limitations, the article proposes indicators of regional statistics that directly and indirectly characterize informal employment and also presents the possibilities of using the «random forest» method to identify groups of constituent entities of the Russian Federation that have similar macroeconomic factors of informal employment. The novelty of this method in terms of research objectives is that it allows one to assess the impact of macroeconomic indicators of regional development on the level of informal employment, taking into account the implicit, not predetermined by the initial hypotheses, hierarchical relationships of factor indicators. Based on the generalization of the studies presented in the literature, as well as the authors’ statistical calculations using Rosstat data, the authors came to the conclusion about the high importance of macroeconomic parameters of regional development and systemic relationships of macroeconomic indicators in substantiating the differentiation of the informal level across the constituent entities of the Russian Federation.

METHOD SUGGESTING CITY WALKING ROUTES FOR PEDESTRIANS USING AN EXAMPLE OF SAINT-PETERSBURG

2019 ◽  
pp. 71-76
Author(s):  
A.E. Semenov
Keyword(s):  
Random Forest ◽  
Develop Model ◽  
Random Forest Algorithm ◽  
Saint Petersburg ◽  
Pedestrian Navigation ◽  
Factors Influencing ◽  
The City

The method of pedestrian navigation in the cities illustrated by the example of Saint-Petersburg was investigated. The factors influencing people when they choose a route for their walk were determined. Based on acquired factors corresponding data was collected and used to develop model determining attractiveness of a street in the city using Random Forest algorithm. The results obtained shows that routes provided by the method are 14% more attractive and just 6% longer compared with the shortest ones.

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