scholarly journals A Novel Approach Combining Particle Swarm Optimization and Deep Learning for Flash Flood Detection from Satellite Images

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2846
Author(s):  
Do Ngoc Tuyen ◽  
Tran Manh Tuan ◽  
Le Hoang Son ◽  
Tran Thi Ngan ◽  
Nguyen Long Giang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Satellite Images ◽  
Flash Flood ◽  
Particle Swarm ◽  
Early Warning Systems ◽  
Flash Floods ◽  
Warning Systems ◽  
Swarm Optimization ◽  
Number Of Layers

Flood is one of the deadliest natural hazards worldwide, with the population affected being more than 2 billion between 1998–2017 with a lack of warning systems according to WHO. Especially, flash floods have the potential to generate fatal damages due to their rapid evolution and the limited warning and response time. An effective Early Warning Systems (EWS) could support detection and recognition of flash floods. Information about a flash flood can be mainly provided from observations of hydrology and from satellite images taken before the flash flood happens. Then, predictions from satellite images can be integrated with predictions based on sensors’ information to improve the accuracy of a forecasting system and subsequently trigger warning systems. The existing Deep Learning models such as UNET has been effectively used to segment the flash flood with high performance, but there are no ways to determine the most suitable model architecture with the proper number of layers showing the best performance in the task. In this paper, we propose a novel Deep Learning architecture, namely PSO-UNET, which combines Particle Swarm Optimization (PSO) with UNET to seek the best number of layers and the parameters of layers in the UNET based architecture; thereby improving the performance of flash flood segmentation from satellite images. Since the original UNET has a symmetrical architecture, the evolutionary computation is performed by paying attention to the contracting path and the expanding path is synchronized with the following layers in the contracting path. The UNET convolutional process is performed four times. Indeed, we consider each process as a block of the convolution having two convolutional layers in the original architecture. Training of inputs and hyper-parameters is performed by executing the PSO algorithm. In practice, the value of Dice Coefficient of our proposed model exceeds 79.75% (8.59% higher than that of the original UNET model). Experimental results on various satellite images prove the advantages and superiority of the PSO-UNET approach.

scholarly journals Application of particle swarm optimization in optimal placement of tsunami sensors

2020 ◽  
Vol 6 ◽  
pp. e333
Author(s):  
Angelie Ferrolino ◽  
Renier Mendoza ◽  
Ikha Magdalena ◽  
Jose Ernie Lope
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Early Warning Systems ◽  
Optimal Placement ◽  
Warning Systems ◽  
Swarm Optimization ◽  
Tsunami Detection ◽  
Nonlinear Shallow Water Equations ◽  
Tsunami Risk ◽  
Risk Reduction Measures

Rapid detection and early warning systems demonstrate crucial significance in tsunami risk reduction measures. So far, several tsunami observation networks have been deployed in tsunamigenic regions to issue effective local response. However, guidance on where to station these sensors are limited. In this article, we address the problem of determining the placement of tsunami sensors with the least possible tsunami detection time. We use the solutions of the 2D nonlinear shallow water equations to compute the wave travel time. The optimization problem is solved by implementing the particle swarm optimization algorithm. We apply our model to a simple test problem with varying depths. We also use our proposed method to determine the placement of sensors for early tsunami detection in Cotabato Trench, Philippines.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

scholarly journals Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5609 ◽  
Author(s):  
Shahab S. Band ◽  
Saeid Janizadeh ◽  
Subodh Chandra Pal ◽  
Asish Saha ◽  
Rabin Chakrabortty ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Gully Erosion ◽  
Slope Aspect ◽  
Swarm Optimization ◽  
Proposed Model ◽  
Deep Learning Neural Network

This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70%) and testing (30%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.

scholarly journals Deep Loving - The Friend of Deep Learning

2020 ◽  
pp. 51-53
Author(s):  
Satish Gajawada ◽  
Hassan M. H. Mustafa
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Literature Review ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Definition Of

Artificial Intelligence and Deep Learning are good fields of research. Recently, the brother of Artificial Intelligence titled "Artificial Satisfaction" was introduced in literature [10]. In this article, we coin the term “Deep Loving”. After the publication of this article, "Deep Loving" will be considered as the friend of Deep Learning. Proposing a new field is different from proposing a new algorithm. In this paper, we strongly focus on defining and introducing "Deep Loving Field" to Research Scientists across the globe. The future of the "Deep Loving" field is predicted by showing few future opportunities in this new field. The definition of Deep Learning is shown followed by a literature review of the "Deep Loving" field. The World's First Deep Loving Algorithm (WFDLA) is designed and implemented in this work by adding Deep Loving concepts to Particle Swarm Optimization Algorithm. Results obtained by WFDLA are compared with the PSO algorithm.

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