Decision Trees for the prediction of environmental and agronomic effects of the use of Compost of Sewage Slugde (CSS)

Perez-Alonso, Daniel; Peña-Tejedor, Susana; Navarro, Milagros; Rad, Carlos; Arnaiz‐González, Álvar; Díez-Pastor, José-Francisco

doi:10.1016/j.spc.2017.07.001

Cited by 10 publications

(3 citation statements)

References 38 publications

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“…Alonso et al performed a study on the estimation of the environmental and agricultural impacts of sewage sludge on fertilization using decision trees. The results proved that decision trees interpret predictions for complex data in medium and long periods of agricultural experience [37].…”

Section: Introductionmentioning

confidence: 69%

“…In the case of data with a lot of features, it is very hard to correctly fit data into a single model. Decision trees divide a dataset into two or more subsets until they reach the stop criterion [37]. Alonso et al performed a study on the estimation of the environmental and agricultural impacts of sewage sludge on fertilization using decision trees.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of Heat Transfer Coefficient in Solar Greenhouse Type Drying Systems

Çerçi

Daş

2019

Sustainability

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As a sustainable energy source, solar energy is used in many applications. A greenhouse type dryer, which is a food drying system, directly benefits from solar energy. Convective heat transfer coefficient (hc) is an important parameter in food drying systems, in terms of system design and performance. Many parameters and equations are used to determine hc. However, as it is difficult to manually process and analyze large amounts of data and different formulations, machine learning algorithms are preferred. In this study, natural and forced convective solar greenhouse type dryers were designed. In a solar greenhouse type dryer, grape is dried in natural (GDNC) and forced convection (GDFC). For convective heat transfer coefficient (hc), predictive models were created using a multilayer perceptron (MLP)—which has many uses in drying applications, as mentioned in the literature—and decision tree (DT), which has not been used before in food drying applications. The machine learning algorithms and results of the estimated models are compared in this study. Error analyses were performed to determine the accuracy rates of the obtained models. As a result, the hc value of the dried grape product in a natural convective solar greenhouse type dryer was 11.3% higher than that of the forced type. The DT algorithm was found to be a more successful model than the MLP algorithm in estimating hc values in HDFC according to Root Mean Square Error. (RMSE = 0.0903). On the contrary, the MLP algorithm was more successful than the DT algorithm in estimating hc values in GDNC (RMSE = 0.0815).

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Section: Introductionmentioning

confidence: 69%

Section: Introductionmentioning

confidence: 99%

Modeling of Heat Transfer Coefficient in Solar Greenhouse Type Drying Systems

Çerçi

Daş

2019

Sustainability

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“…In the context of environmental impact, the authors tried to estimate: the illegal disposal of waste (Yang, Fan, et al, 2019), the emissions of polluting gases from the inadequate final disposal of waste (Dimishkovsk et al, 2019;Kumar et al, 2016;Vu et al, 2018), soil pollution by heavy metals (Perez-Alonso et al, 2017) and the calorific value of waste as a potential source of energy (Baghban and Shamshirband, 2019;Bagheri et al, 2019;Boumanchar et al, 2019;Drudi et al, 2019;Li et al, 2020;Rostami and Baghban, 2018). In terms of economic impact, studies sought to predict the generation of waste to promote cost efficiency in the provision of services and to identify the best form of service provision (Pérez-López et al, 2016).…”

Section: Main Challenges and Opportunitiesmentioning

confidence: 99%

Data mining and knowledge discovery in databases for urban solid waste management: A scientific literature review

et al. 2021

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The processes related to solid waste management (SWM) are being revised as new technologies emerge and are applied in the area to achieve greater environmental, social and economic sustainability for society. To achieve our goal, two robust review protocols (Population, Intervention, Comparison, Outcome, and Context (PICOC) and Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)) were used to systematically analyze 62 documents extracted from the Web of Science database to identify the main techniques and tools for Knowledge Discovery in Databases (KDD) and Data Mining (DM) as applied to SWM and explore the technological potential to optimize the stages of collecting and transporting waste. Moreover, it was possible to analyze the main challenges and opportunities of KDD and DM for SWM. The results show that the most used tools for SWM are MATLAB (29.7%) and GIS (13.5%), whereas the most used techniques are Artificial Neural Networks (35.8%), Linear Regression (16.0%) and Support Vector Machine (12.3%). In addition, 15.3% of the studies were conducted with data from China, 11.1% from India and 9.7% of the studies analyzed and compared data from several other countries. Furthermore, the research showed that the main challenges in the field of study are related to the collection and treatment of data, whereas the opportunities appear to be linked mainly to the impact on the pillars of sustainable development. Thus, this study portrays important issues associated with the use of KDD and DM for optimal SWM and has the potential to assist and direct researchers and field professionals in future studies.

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