A Trend Analysis of Development Projects in South Korea during 2007–2016 Using a Multi-Layer Perceptron Based Artificial Neural Network

In Korea, the Ministry of Environment and regional environment management agencies conduct environmental impact assessments (EIA) to mitigate and assess the impact of major development projects on the environment. EIA Big Data are used in conjunction with a geographical information system (GIS), and consist of indicators related to air, soil, and water that are measured before and after the development project. The impact of the development project on the environment can be evaluated through the variations of each indicator. This study analyzed trends in the environmental impacts of development projects during 2007–2016 using 21 types of EIA Big Data. A model was developed to estimate the Korean Environment Institute’s Environmental Impact Assessment Index for Development Projects (KEIDP) using a multi-layer perceptron-based artificial neural network (MLP-ANN) approach. A trend analysis of development projects in South Korea revealed that the mean value of KEIDP gradually increased over the study period. The rate of increase was 0.007 per year, with an R2 value of 0.8. In the future, it will be necessary for all management agencies to apply the KEDIP calculation model to minimize the impact of development projects on the environment and reduce deviations among development projects through continuous monitoring.

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The Impact of Spatial Resolution on Reconstruction of Simple Pattern Through Multi Layer Perceptron Artificial Neural Network

Lecture Notes in Electrical Engineering - Fundamental Research in Electrical Engineering ◽

10.1007/978-981-10-8672-4_71 ◽

2018 ◽

pp. 931-939

Author(s):

Pardis Jafari ◽

Saeideh Sarmadi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Spatial Resolution ◽

Multi Layer Perceptron ◽

Simple Pattern ◽

Artificial Neural ◽

The Impact

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Study on Correlation between Urban Development and Air Pollution Based on Artificial Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.361-363.860 ◽

2013 ◽

Vol 361-363 ◽

pp. 860-863

Author(s):

Jian Ping Li ◽

Qiao Song ◽

Hai Ying Yang ◽

Han Ming Duan

Keyword(s):

Neural Network ◽

Air Pollution ◽

Artificial Neural Network ◽

Environmental Impact ◽

Urban Development ◽

Impact Factors ◽

Pollution Indicators ◽

Pollution Indicator ◽

Artificial Neural ◽

The Impact

The paper presents a method of researching the impact of urban development on air quality on the basis of artificial neural network (ANN). Statistical data in a monitoring period constitute a sample which contains monitoring values of environmental impact factors and air pollution indicators. Several samples are employed to train the ANN, and the mapping relationship between environmental impact factors and air pollution indicators is established through the trained ANN. The impact degree of each environmental impact factor on each air pollution indicator can be obtained by using the connection weights of the trained ANN. The case study illustrates the feasibility of the method mentioned in the paper which explores a new idea to the study of environmental impact of urban development.

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Modeling Environmental Effects on Electromagnetic Signal Propagation Using Multi-Layer Perceptron Artificial Neural Network

International Journal on Communications Antenna and Propagation (IRECAP) ◽

10.15866/irecap.v10i3.18135 ◽

2020 ◽

Vol 10 (3) ◽

pp. 175

Author(s):

Virginia C. Ebhota ◽

Viranjay M. Srivastava

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Environmental Effects ◽

Signal Propagation ◽

Electromagnetic Signal ◽

Multi Layer Perceptron ◽

Artificial Neural

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Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

Scientific Reports ◽

10.1038/s41598-021-81556-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Zhonghui Thong ◽

Jolena Ying Ying Tan ◽

Eileen Shuzhen Loo ◽

Yu Wei Phua ◽

Xavier Liang Shun Chan ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Regression Model ◽

Sensitivity Threshold ◽

Ann Model ◽

Blood Samples ◽

Ann Models ◽

Artificial Neural ◽

The Impact ◽

Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

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Transmission Line Fault Classification and Location Using Multi-Layer Perceptron Artificial Neural Network

IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society ◽

10.1109/iecon43393.2020.9254653 ◽

2020 ◽

Author(s):

A. K. Onaolapo ◽

R. Pillay Carpanen ◽

D. G. Dorrell ◽

E. E. Ojo

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Transmission Line ◽

Fault Classification ◽

Multi Layer Perceptron ◽

Artificial Neural

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Prognosis of water levels in a moor groundwater system influenced by hydrology and water extraction using an artificial neural network

10.5194/egusphere-egu21-3013 ◽

2021 ◽

Author(s):

Sascha Flaig ◽

Timothy Praditia ◽

Alexander Kissinger ◽

Ulrich Lang ◽

Sergey Oladyshkin ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mean Squared Error ◽

Meteorological Data ◽

Water Levels ◽

Typical Structure ◽

Water Abstraction ◽

Physical Knowledge ◽

Artificial Neural ◽

The Impact

In order to prevent possible negative impacts of water abstraction in an ecologically sensitive moor south of Munich (Germany), a &#8220;predictive control&#8221; scheme is in place. We design an artificial neural network (ANN) to provide predictions of moor water levels and to separate hydrological from anthropogenic effects. As the moor is a dynamic system, we adopt the &#8222;Long short-term memory&#8220; architecture.To find the best LSTM setup, we train, test and compare LSTMs with two different structures: (1) the non-recurrent one-to-one structure, where the series of inputs are accumulated and fed into the LSTM; and (2) the recurrent many-to-many structure, where inputs gradually enter the LSTM (including LSTM forecasts from previous forecast time steps). The outputs of our LSTMs then feed into a readout layer that converts the hidden states into water level predictions. We hypothesize that the recurrent structure is the better structure because it better resembles the typical structure of differential equations for dynamic systems, as they would usually be used for hydro(geo)logical systems. We evaluate the comparison with the mean squared error as test metric, and conclude that the recurrent many-to-many LSTM performs better for the analyzed complex situations. It also produces plausible predictions with reasonable accuracy for seven days prediction horizon.Furthermore, we analyze the impact of preprocessing meteorological data to evapotranspiration data using typical ETA models. Inserting knowledge into the LSTM in the form of ETA models (rather than implicitly having the LSTM learn the ETA relations) leads to superior prediction results. This finding aligns well with current ideas on physically-inspired machine learning.As an additional validation step, we investigate whether our ANN is able to correctly identify both anthropogenic and natural influences and their interaction. To this end, we investigate two comparable pumping events under different meteorological conditions. Results indicate that all individual and combined influences of input parameters on water levels can be represented well. The neural networks recognize correctly that the predominant precipitation and lower evapotranspiration during one pumping event leads to a lower decrease of the hydrograph.To further demonstrate the capability of the trained neural network, scenarios of pumping events are created and simulated.In conclusion, we show that more robust and accurate predictions of moor water levels can be obtained if available physical knowledge of the modeled system is used to design and train the neural network. The artificial neural network can be a useful instrument to assess the impact of water abstraction by quantifying the anthropogenic influence.

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