Application of ANN Algorithm in Tree Height Modeling

Li, Yao Xiang; Li, Jiang

doi:10.4028/www.scientific.net/amm.20-23.756

Cited by 9 publications

(7 citation statements)

References 3 publications

(5 reference statements)

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“…When compared with multiple linear regression (MLR), ANN presents a computational path to measure a non-linear relationship between several inputs and one or more outputs. ANN has been applied for modeling, identifying, and predicting complex systems (Li and Jiang, 2010). Comparing estimates using regression and ANN has shown that the performance value of the ANN model is better than the regression model.…”

Section: About Consumer Failures Inmentioning

confidence: 99%

How Large the Direct Rebound Effect for Residential Electricity Consumption When the Artificial Neural Network Takes on the Role? A Taiwan Case Study of Household Electricity Consumption

Adha¹,

Hong

2021

IJEEP

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Amid the energy reform efforts by the Taiwan government, residential energy demand continues to face an escalating trend every year. This indicates the phenomenon of the energy efficiency gap. One of the factors that control the energy efficiency gap is the rebound effect. The rebound effect is related to the increase in energy consumption through efforts to reduce the use of energy itself. This can be due to the low cost of usage that causes a person to be encouraged to use more energy. This study aims to estimate the magnitude of the direct rebound effect of household electricity consumption in Taiwan using monthly time series data from January 1998 to December 2018 and to implement the artificial neural network (ANN) as an alternative approach to measure the direct rebound effect. Based on the simulation results, the direct rebound effect magnitude for household electricity consumption in Taiwan is in the range of 11.17% to 21.95%. GDP growth is the most important input in the model. Additionally, population growth and climate change are also critical factors and have significant implications in the model.

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Section: About Consumer Failures Inmentioning

confidence: 99%

How Large the Direct Rebound Effect for Residential Electricity Consumption When the Artificial Neural Network Takes on the Role? A Taiwan Case Study of Household Electricity Consumption

Adha¹,

Hong

2021

IJEEP

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“…In comparison with a multiple linear regression (MLR) model, an ANN provides a computational way of determining a non-linear relationship between some inputs and one or more outputs. ANN has applied for modeling, identification, and prediction of complex systems [48]. Although the methods used for the prediction of the water discharge provide prediction accuracy approximately up to 80% [14], the performance of the prediction may be increased using ANNs.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network and Multiple Linear Regression for Flood Prediction in Mohawk River, New York

Tsakiri

Marsellos

Kapetanakis

2018

Water

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This research introduces a hybrid model for forecasting river flood events with an example of the Mohawk River in New York. Time series analysis and artificial neural networks are combined for the explanation and forecasting of the daily water discharge using hydrogeological and climatic variables. A low pass filter (Kolmogorov–Zurbenko filter) is applied for the decomposition of the time series into different components (long, seasonal, and short-term components). For the prediction of the water discharge time series, each component has been described by applying the multiple linear regression models (MLR), and the artificial neural network (ANN) model. The MLR retains the advantage of the physical interpretation of the water discharge time series. We prove that time series decomposition is essential before the application of any model. Also, decomposition shows that the Mohawk River is affected by multiple time scale components that contribute to the hydrologic cycle of the included watersheds. Comparison of the models proves that the application of the ANN on the decomposed time series improves the accuracy of forecasting flood events. The hybrid model which consists of time series decomposition and artificial neural network leads to a forecasting up to 96% of the explanation for the water discharge time series.

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“…In the previous research, the utilization of artificial neural network (ANN) methods to recognize data patterns and forecasting have been carried out in many applications, such as biological (Gu et al, 2012), food (Stangierski et al, 2019), chemical (Radfard et al, 2018), environment (Li & Jiang, 2010;Ul-Saufie et al, 2011), and disaster (Borujeni & Nateghi, 2019;Elsafi, 2014;Pradhan & Lee, 2010;Tsakiri et al, 2018). Notably, Pradhan and Lee (2010), used the backpropagation neural network to analyze landslide susceptibility.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Intelligent System on the Early Warning System of Landslide

Sofwan¹,

Sumardi²,

Najib³

et al. 2021

JST

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Landslide is a natural sloping ground movement disaster that can occur due to several factors such as high rainfall, soil moisture in the depth of the soil of an area, vibrations experienced in the region, and the slope of the ground structure. A system that can deliver these factor values into the levels of vulnerability of landslide disasters is needed. The system uses Arduino Mega 2560 to process the level of vulnerability. It can predict the moment and the probability of the disaster occurring as an early warning system. The artificial neural network (ANN) intelligent system can expect an event of a disaster. The designed ANN used five parameters causing landslide as input data: rainfall, slope, soil moisture on the surface, soil moisture in the ground’s depth, and soil vibration. The ANN system output delivered three-level conditions: the safe, the standby, and the hazardous. The feed-forward backpropagation (FFBP) and the cascade forward backpropagation (CFBP) methods were analyzed. The performance of both methods was compared in terms of minimum square error (MSE). The MSE results of FFBP and CFBP in the safe, the standby, and the hazardous conditions were 0.017076 and 0.034952; 0.049597 and 0.046764; 0.062105 and 0.060355; respectively. The results point to the supremacy of CFBP to FFBP in standby and hazardous conditions. Therefore, the CFBP is implemented into the hardware of the early warning system.

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Application of ANN Algorithm in Tree Height Modeling

Cited by 9 publications

References 3 publications

How Large the Direct Rebound Effect for Residential Electricity Consumption When the Artificial Neural Network Takes on the Role? A Taiwan Case Study of Household Electricity Consumption

How Large the Direct Rebound Effect for Residential Electricity Consumption When the Artificial Neural Network Takes on the Role? A Taiwan Case Study of Household Electricity Consumption

Artificial Neural Network and Multiple Linear Regression for Flood Prediction in Mohawk River, New York

Artificial Neural Network Intelligent System on the Early Warning System of Landslide

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