Prediction Models for the Plant Coverage Percentage of a Vertical Green Wall System: Regression Models and Artificial Neural Network Models

Abstract

(1) Background: The expansion that most cities have been showing for more than half a century has also brought with it an increase in the density of buildings, most of the time at the expense of green areas. This has led to negative effects, such as overpopulation of cities, rising urban temperatures, pollution of water, air, soil, and others, affecting daily urban life. As a result, specialists from different fields form multidisciplinary teams are looking for solutions to counteract these effects. The subject of visible facades has registered an increased interest among researchers in recent years because they can represent a viable solution that can contribute to increasing the degree of urban comfort. However, for such a system to be effective, it is necessary that the plants used grow and develop harmoniously and ensure the best possible coverage of the facade. The aim of this research is to find an adequate mathematical model that can predict, with a high degree of accuracy, the percentage of plant coverage of a green wall system, which is positioned in the city of Iasi, northeastern Romania. (2) Methods: The models used for this purpose were a multiple linear regression model (MLR) and a model based on a feed-forward artificial neural network (ANN). Four independent variables (soil temperature, soil moisture, week of the year, and cardinal wall orientation) and the interaction between two variables (soil temperature and week of the year) were used for the multiple linear regression model. Artificial neural networks were also trained to estimate the percentage of plant coverage in the analyzed system, and the network with the best mean squared error performance was chosen in doing predictions. For both MLR and ANN models, we constructed confidence intervals for the degree of plant coverage of the system (PCP) for a set of observed values. In the case of the ANN model, the confidence interval was derived via the bootstrap method, which is a resampling with replacement technique used to generate new samples from the original dataset. To the best of our knowledge, the derivation of confidence intervals using a combination of neural networks with the bootstrap method has not been used before, at least for predictions in horticulture. (3) Results: The ANN employed here consisted of one input layer with four neurons, one hidden layer with five neurons, and one output layer with one neuron. The comparison showed that the confidence interval obtained using ANN has a shorter length (and thus it is more accurate) than that obtained by the multiple linear regression model. The choice of the experimental module façade had a significant influence (of magnitude 1.9073) on the plant coverage percentage. An increase of one unit in soil humidity will determine an increase of almost 5.1% in plant coverage percentage, and an increase of 1 °C in soil temperature will determine a decrease of almost 1.21% in plant coverage percentage. The choice of the experimental module façade had a significant influence (of magnitude 1.9073) on the plant coverage percentage. (4) Conclusions: Although both methods showed to be useful in making predictions, the ANN method showed better predictive capabilities, at least when the performance is measured by the mean squared error. This fact may be useful when predicting the percentage of plant coverage of a green wall system with a higher degree of accuracy, in the case of organizing outdoor exhibitions or other similar projects.

A Study on the Development of Two Ornamental Varieties of Ipomoea batatas Cultivated in Vertical Systems in the Northeastern Region of Europe

“…Reducing the negative impacts of excessive urbanisation has been a major concern for researchers from various fields of study in their attempts to provide an effective, sustainable solution to this currently challenging issue [1]. Both macro-level solutions on large land areas, such as urban and peri-urban forests [2], and micro-level solutions, which cover smaller surfaces (parks and squares) or unconventional surfaces such as green roofs and façades [3,4], aim to counteract such effects and contribute to greater urban comfort and life quality. The mitigation of urban heat islands, the reduction or elimination of air pollutants, a lowered level of noise, and improved physical and mental health, as well as the well-being of citizens, are some of the benefits provided by green areas [5,6].…”

Structure and diversity in a periurban forest of Bucharest, Romania

“…Some of the major problems resulting from rapid urbanization include poor air quality and higher air temperatures in urban environments (e.g., urban heat islands, UHI) worsening air pollution and affecting human health and citizens well-being (Cohen et al 2017). An effective strategy in developing and designing urban green spaces (parks, and urban and periurban forests) is to mitigate the negative effects (Marando et al 2016, Chiruță et al 2023. The concept of urban forestry has been introduced in Europe during the 1980s (Konijnendijk 2003), and multiple definitions, concepts and management plans were developed (Helms 2002, Randrup et al 2005.…”