Evaluating the ability of artificial neural network and PCA-M5P models in predicting leachate COD load in landfills

Azadi, Sama; Amiri, Hamid; Rakhshandehroo, Gholamreza

doi:10.1016/j.wasman.2016.05.025

Cited by 36 publications

(17 citation statements)

References 37 publications

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“…The learning rate was set to 0.1, and the number of neurons in the three-layer BPNN was calculated by eq . The number of neurons was limited to the maximum value (five), so as to improve interpretability. , Hyperbolic tangent (tanh) and logistic functions were examined as activation functions in the hidden layer, while pure linear (purelin), tanh, and logistic functions were in the output layer . The resilient BPNN algorithm is more suitable when the data set is small and adopted completely .…”

Section: Methodsmentioning

confidence: 99%

“…44,46 Hyperbolic tangent (tanh) and logistic functions were examined as activation functions in the hidden layer, while pure linear (purelin), tanh, and logistic functions were in the output layer. 47 The resilient BPNN algorithm is more suitable when the data set is small and adopted completely. 48 Four resilient BPNN algorithms were applied to train networks and modify weights, including resilient back-propagation with weight backtracking (rprop+), resilient back-propagation without weight backtracking (rprop−), rprop− with the smallest absolute gradient learning rate (sag), and rprop− with the smallest learning rate (slr).…”

Section: Preprocessing Of the Pcmsw Datamentioning

confidence: 99%

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Estimating Physical Composition of Municipal Solid Waste in China by Applying Artificial Neural Network Method

Zhou

Chi

et al. 2020

Environ. Sci. Technol.

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Physical composition of municipal solid waste (PCMSW) is the fundamental parameter in domestic waste management; however, high fidelity, wide coverage, upscaling, and year continuous data sets of PCMSW in China are insufficient. A traceable and predictable methodology for estimating PCMSW in China is established for the first time by analyzing 503 PCMSW data sets of 135 prefecture-level cities in China. A hyperspherical transformation method was used to eliminate the constant sum constraint in statistically analyzing PCMSW data. Moreover, a back-propagation (BP) neural network methodology was applied to establish quantitative models between city-level PCMSW and its socio-economic factors, including city size, per capita gross regional product, geographical location, gas coverage rate, and year. Results show that (1) national-level PCMSW in 2017 was estimated as organic fraction (53.7%), ash and stone (8.3%), paper (16.9%), plastic and rubber (13.6%), textile (2.3%), wood (2.2%), metal (0.6%), glass (1.5%), and others (1.0%); (2) organic fraction, paper, and plastics showed an increasing trend from 1990 to 2017, while ash and stone decreased significantly; (3) organic fractions in East, North, and Central-South China were higher than those in other regions. This enables us to fill the data gap in the practice of municipal solid waste management in China.

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Section: Methodsmentioning

confidence: 99%

Section: Preprocessing Of the Pcmsw Datamentioning

confidence: 99%

Estimating Physical Composition of Municipal Solid Waste in China by Applying Artificial Neural Network Method

Zhou

Chi

et al. 2020

Environ. Sci. Technol.

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“…The layer sends a numeric value to the next layer based on the threshold. MLP does not provide an increase in computing power over single-layer networks if the activation function is linear (Azadi et al 2016). MLP's power is determined by the non-linear activation function.…”

Section: Multilayer Perceptronmentioning

confidence: 99%

Deep learning model for daily rainfall prediction: case study of Jimma, Ethiopia

2021

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Rainfall prediction is a critical task because many people rely on it, particularly in the agricultural sector. Rainfall forecasting is difficult due to the ever-changing nature of weather conditions. In this study, we carry out a rainfall predictive model for Jimma, a region located in southwestern Oromia, Ethiopia. We proposed a Long Short-Term Memory (LSTM)-based prediction model capable of forecasting Jimma's daily rainfall. Experiments were conducted to evaluate the proposed models using various metrics such as Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) Nash-Sutcliffe model efficiency (NSE), and R2, and the results were 0.01, 0.4786 0.81 and 0.9972, respectively. We also compared the proposed model to existing machine learning regressions like Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT). The RMSE of MLP was the lowest of the four existing learning models i.e., 0.03. The proposed LSTM model outperforms the existing models, with an RMSE of 0.01. The experimental results show that the proposed model has a lower RMSE and a higher R2.

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“…53 It is a widely used decision tree algorithm by many researchers for prediction purposes. [54][55][56] There are three main steps in the M5P algorithm: (1) constructing the tree, (2) pruning the tree, and (3) smoothing the tree. 57 The first step involves identifying different data classes and building a tree.…”

Section: M5p Model Treementioning

confidence: 99%

The impact of COVID ‐19 on the electricity demand: a case study for Turkey

Ceylan

2021

Int J Energy Res

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Due to the extraordinary impact of the Coronavirus Disease 2019 and the resulting lockdown measures, the demand for energy in business and industry has dropped significantly. This change in demand makes it difficult to manage energy generation, especially electricity production and delivery. Thus, reliable models are needed to continue safe, secure, and reliable power.An accurate forecast of electricity demand is essential for making a reliable decision in strategic planning and investments in the future. This study presents the extensive effects of COVID-19 on the electricity sector and aims to predict electricity demand accurately during the lockdown period in Turkey. For this purpose, well-known machine learning algorithms such as Gaussian process regression (GPR), sequential minimal optimization regression (SMOReg), correlated Nyström views (XNV), linear regression (LR), reduced error pruning tree (REPTree), and M5P model tree (M5P) were used. The SMOReg algorithm performed best with the lowest mean absolute percentage error (3.6851%), mean absolute error (21.9590), root mean square error (29.7358), and root relative squared error (36.5556%) values in the test dataset. This study can help policy-makers develop appropriate policies to control the harms of not only the current pandemic crisis but also an unforeseeable crisis.

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Evaluating the ability of artificial neural network and PCA-M5P models in predicting leachate COD load in landfills

Cited by 36 publications

References 37 publications

Estimating Physical Composition of Municipal Solid Waste in China by Applying Artificial Neural Network Method

Estimating Physical Composition of Municipal Solid Waste in China by Applying Artificial Neural Network Method

Deep learning model for daily rainfall prediction: case study of Jimma, Ethiopia

The impact of COVID ‐19 on the electricity demand: a case study for Turkey

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