scholarly journals Shallow Landslide Susceptibility Models Based on Artificial Neural Networks Considering the Factor Selection Method and Various Non-Linear Activation Functions

2020 ◽  
Vol 12 (7) ◽  
pp. 1194 ◽  
Author(s):  
Deuk-Hwan Lee ◽  
Yun-Tae Kim ◽  
Seung-Rae Lee
Keyword(s):  
Landslide Susceptibility ◽  
Activation Function ◽  
Shallow Landslide ◽  
Predisposing Factors ◽  
Landslide Susceptibility Mapping ◽  
Activation Functions ◽  
Kappa Index ◽  
Output Layer ◽  
Non Linear ◽  
Hidden Layer

Landslide susceptibility mapping is well recognized as an essential element in supporting decision-making activities for preventing and mitigating landslide hazards as it provides information regarding locations where landslides are most likely to occur. The main purpose of this study is to produce a landslide susceptibility map of Mt. Umyeon in Korea using an artificial neural network (ANN) involving the factor selection method and various non-linear activation functions. A total of 151 historical landslide events and 20 predisposing factors consisting of Geographic Information System (GIS)-based morphological, hydrological, geological, and land cover datasets were constructed with a resolution of 5 x 5 m. The collected datasets were applied to information gain ratio analysis to confirm the predictive power and multicollinearity diagnosis to ensure the correlation of independence among the landslide predisposing factors. The best 11 predisposing factors that were selected in this study were randomly divided into a 70:30 ratio for training and validation datasets, which were used to produce ANN-based landslide susceptibility models. The ANN model used in this study had a multi-layer perceptron (MLP) structure consisting of an input layer, one hidden layer, and an output layer. In the output layer, the logistic sigmoid function was used to represent the result value within the range of 0 to 1, and six non-linear activation functions were used for the hidden layer. The performance of the landslide susceptibility models was evaluated using the receiver operating characteristic curve, Kappa index, and five statistical indices (sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV)) with the training dataset. In addition, the landslide susceptibility models were validated using the aforementioned measures with the validation dataset and were compared using the Friedman test to check the significant differences among the six developed models. The optimal number of neurons was determined based on the aforementioned performance evaluation and validation results. Overall, the model with the best performance was the MLP model with the logistic sigmoid activation function in the output layer and the hyperbolic tangent sigmoid activation function with five neurons in the hidden layer. The validation results of the best model showed a sensitivity of 82.61%, specificity of 78.26%, accuracy of 80.43%, PPV of 79.17%, NPV of 81.82%, a Kappa index of 0.609, and AUC of 0.879. The results of this study highlight the effectiveness of selecting an optimal MLP model structure for shallow landslide susceptibility mapping using an appropriate predisposing factor section method.

Analysis of Non-Linear Activation Functions for Classification Tasks Using Convolutional Neural Networks

2019 ◽  
Vol 12 (3) ◽  
pp. 156-161 ◽  
Author(s):  
Aman Dureja ◽  
Payal Pahwa
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Primary Objective ◽  
Experimental Comparison ◽  
Activation Functions ◽  
Practical Applications ◽  
Network Activation ◽  
Non Linear ◽  
Hidden Layer

Background: In making the deep neural network, activation functions play an important role. But the choice of activation functions also affects the network in term of optimization and to retrieve the better results. Several activation functions have been introduced in machine learning for many practical applications. But which activation function should use at hidden layer of deep neural networks was not identified. Objective: The primary objective of this analysis was to describe which activation function must be used at hidden layers for deep neural networks to solve complex non-linear problems. Methods: The configuration for this comparative model was used by using the datasets of 2 classes (Cat/Dog). The number of Convolutional layer used in this network was 3 and the pooling layer was also introduced after each layer of CNN layer. The total of the dataset was divided into the two parts. The first 8000 images were mainly used for training the network and the next 2000 images were used for testing the network. Results: The experimental comparison was done by analyzing the network by taking different activation functions on each layer of CNN network. The validation error and accuracy on Cat/Dog dataset were analyzed using activation functions (ReLU, Tanh, Selu, PRelu, Elu) at number of hidden layers. Overall the Relu gave best performance with the validation loss at 25th Epoch 0.3912 and validation accuracy at 25th Epoch 0.8320. Conclusion: It is found that a CNN model with ReLU hidden layers (3 hidden layers here) gives best results and improve overall performance better in term of accuracy and speed. These advantages of ReLU in CNN at number of hidden layers are helpful to effectively and fast retrieval of images from the databases.

scholarly journals Hardware implementation of radial-basis neural networks with Gaussian activation functions on FPGA

2021 ◽  
Author(s):  
Volodymyr Shymkovych ◽  
Sergii Telenyk ◽  
Petro Kravets
Keyword(s):  
Neural Networks ◽  
Hardware Implementation ◽  
Gaussian Function ◽  
Activation Function ◽  
Rbf Neural Networks ◽  
Activation Functions ◽  
Rbf Network ◽  
Combination Scheme ◽  
Radial Basis ◽  
Hidden Layer

AbstractThis article introduces a method for realizing the Gaussian activation function of radial-basis (RBF) neural networks with their hardware implementation on field-programmable gaits area (FPGAs). The results of modeling of the Gaussian function on FPGA chips of different families have been presented. RBF neural networks of various topologies have been synthesized and investigated. The hardware component implemented by this algorithm is an RBF neural network with four neurons of the latent layer and one neuron with a sigmoid activation function on an FPGA using 16-bit numbers with a fixed point, which took 1193 logic matrix gate (LUTs—LookUpTable). Each hidden layer neuron of the RBF network is designed on an FPGA as a separate computing unit. The speed as a total delay of the combination scheme of the block RBF network was 101.579 ns. The implementation of the Gaussian activation functions of the hidden layer of the RBF network occupies 106 LUTs, and the speed of the Gaussian activation functions is 29.33 ns. The absolute error is ± 0.005. The Spartan 3 family of chips for modeling has been used to get these results. Modeling on chips of other series has been also introduced in the article. RBF neural networks of various topologies have been synthesized and investigated. Hardware implementation of RBF neural networks with such speed allows them to be used in real-time control systems for high-speed objects.

Shallow Landslide Susceptibility Mapping for Zagreb Hilly Area, Croatia

2014 ◽  
pp. 527-531
Author(s):  
Chunxiang Wang ◽  
Snjezana Mihalić Arbanas ◽  
Hideaki Marui ◽  
Naoki Watanabe ◽  
Gen Furuya
Keyword(s):  
Landslide Susceptibility ◽  
Shallow Landslide ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Hilly Area

Activation Functions Defined on Higher-Dimensional Spaces for Approximation on Compact Sets with and without Scaling

2003 ◽  
Vol 15 (9) ◽  
pp. 2199-2226
Author(s):  
Yoshifusa Ito
Keyword(s):  
Fourier Transform ◽  
Activation Function ◽  
Continuous Functions ◽  
Activation Functions ◽  
Compact Sets ◽  
Higher Dimensional ◽  
Hidden Layer ◽  
The Fourier Transform ◽  
Increasing Function ◽  
Sigmoid Functions

Let g be a slowly increasing function of locally bounded variation defined on Rc, 1 ≤c≤d. We investigate when g can be an activation function of the hidden-layer units of three-layer neural networks that approximate continuous functions on compact sets. If the support of the Fourier transform of g includes a converging sequence of points with distinct distances from the origin, it can be an activation function without scaling. If and only if the support of its Fourier transform includes a point other than the origin, it can be an activation function with scaling. We also look for a condition on which an activation function can be used for approximation without rotation. Any nonpolynomial functions can be activation functions with scaling, and many familiar functions, such as sigmoid functions and radial basis functions, can be activation functions without scaling. With or without scaling, some of them defined on Rd can be used without rotation even if they are not spherically symmetric.

scholarly journals Landslide Susceptibility Mapping Based on Selected Optimal Combination of Landslide Predisposing Factors in a Large Catchment

2015 ◽  
Vol 7 (12) ◽  
pp. 16653-16669 ◽  
Author(s):  
Qianqian Wang ◽  
Dongchuan Wang ◽  
Yong Huang ◽  
Zhiheng Wang ◽  
Lihui Zhang ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Predisposing Factors ◽  
Optimal Combination ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Large Catchment

scholarly journals PERBANDINGAN MODEL JARINGAN SYARAF TIRUAN DENGAN ALGORITMA LEVENBERG-MARQUADT DAN POWELL-BEALE CONJUGATE GRADIENTPADA KECEPATAN ANGIN RATA-RATA DI KOTA SEMARANG

2020 ◽  
Vol 8 (2) ◽  
pp. 127
Author(s):  
Dwi Ispriyanti ◽  
Alan Prahutama ◽  
Tarno Tarno ◽  
Budi Warsito ◽  
Hasbi Yasin ◽  
...  
Keyword(s):  
Wind Speed ◽  
Conjugate Gradient ◽  
Water Waves ◽  
Sea Water ◽  
Activation Function ◽  
Conjugate Gradient Algorithms ◽  
Wind Speed Prediction ◽  
Output Layer ◽  
Gradient Algorithms ◽  
Hidden Layer

Wind is one of the most important weather components. Wind is defined as the dynamics of horizontal air mass displacement measured in two parameters, namely speed and direction. Wind speed and direction depend on the air pressure conditions around the place. High wind speed intensity can cause high sea water waves. To estimate wind speed intensity required a study of wind speed prediction. One of method that can be used is Artificial Neural Network (ANN). In ANN there are several models, one of which is backpropagation. Thepurpose of this researchis to compare between backpropagation model with Levenberg-Marquadt and Powell-Beale Conjugate Gradient algorithms. The results of this researchshowing that Powell-Beale Conjugate Gradient better than Levenberg-Marquadtalgorithms. The best model architecture obtained is a network with two input layer neurons, six hidden layer neurons, and one output layer neuron. The activation function used are the logistic sigmoid in the hidden layer and linear in the output layer. MAPE value based on the chosen model is 0,0136% in training process and 0,0088% in testing process.

scholarly journals Rainfall-Induced Shallow Landslide Susceptibility Mapping at Two Adjacent Catchments Using Advanced Machine Learning Algorithms

2020 ◽  
Vol 9 (10) ◽  
pp. 569
Author(s):  
Ananta Man Singh Pradhan ◽  
Yun-Tae Kim
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Shallow Landslide ◽  
Machine Learning Algorithms ◽  
Landslide Inventory ◽  
Aerial Photographs ◽  
Landslide Susceptibility Mapping ◽  
Gradient Boosting ◽  
Extreme Gradient Boosting ◽  
Testing Accuracy

Landslides impact on human activities and socio-economic development, especially in mountainous areas. This study focuses on the comparison of the prediction capability of advanced machine learning techniques for the rainfall-induced shallow landslide susceptibility of Deokjeokri catchment and Karisanri catchment in South Korea. The influencing factors for landslides, i.e., topographic, hydrologic, soil, forest, and geologic factors, are prepared from various sources based on availability, and a multicollinearity test is also performed to select relevant causative factors. The landslide inventory maps of both catchments are obtained from historical information, aerial photographs and performed field surveys. In this study, Deokjeokri catchment is considered as a training area and Karisanri catchment as a testing area. The landslide inventories contain 748 landslide points in training and 219 points in testing areas. Three landslide susceptibility maps using machine learning models, i.e., Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN), are prepared and compared. The outcomes of the analyses are validated using the landslide inventory data. A receiver operating characteristic curve (ROC) method is used to verify the results of the models. The results of this study show that the training accuracy of RF is 0.756 and the testing accuracy is 0.703. Similarly, the training accuracy of XGBoost is 0.757 and testing accuracy is 0.74. The prediction of DNN revealed acceptable agreement between the susceptibility map and the existing landslides, with a training accuracy of 0.855 and testing accuracy of 0.802. The results showed that the DNN model achieved lower prediction error and higher accuracy results than other models for shallow landslide modeling in the study area.

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