Henry’s gas solubility optimization algorithm in formulating deep neural network for landslide susceptibility assessment in mountainous areas

Nguyen, Quoc Hung; Chou, Tien-Yin; Yeh, Mei‐Ling; Hoang, Thanh-Van; Nguyen, Huu-Duy; Bui, Quang‐Thanh

doi:10.1007/s12665-021-09711-6

Cited by 12 publications

(7 citation statements)

References 47 publications

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“…Nguyen et al used genetic algorithms to optimize the initial weight and threshold of adaptive neural networks. Improved adaptive neural network design significantly improves the speed of integration during training, thereby improving the accuracy of house price predictions [10]. Meng proposed an improvement on the traditional genetic algorithm for the premature convergence caused by the same generation crossover in most genetic algorithms [11].…”

Section: Literature Reviewmentioning

confidence: 99%

Football Match Scoring Method Based on Adaptive Neural Network Algorithm

Yun

2022

Security and Communication Networks

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To study in more detail the impact of various indicators on the scoring system of a football match, the author suggests a football game scoring method based on an adaptive neural network algorithm. Firstly, the application background of football match prediction, the research and application status of the adaptive neural network algorithm, and the related research of football match prediction are described; Secondly, the factors affecting the outcome of football games are analyzed, and the applicability of the adaptive neural network algorithm in football match prediction are summarized. Through the system requirement analysis, the overall design of the system and the design of the database are completed. From here, we see that there is a huge opportunity to use an adaptive neural network in an automatic scoring system after a football match, which can replace manual scoring and reduce the manual workload.

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Section: Literature Reviewmentioning

confidence: 99%

Football Match Scoring Method Based on Adaptive Neural Network Algorithm

Yun

2022

Security and Communication Networks

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“…Equally, soil type plays a key role in flooding because, as is increasingly recognized, water management and soil type are strongly linked. Areas with high vegetation density are less likely to experience flood, as is borne out by previous studies (Nguyen et al, 2021; Nguyen, Quang‐Thanh, et al, 2022).…”

Section: Discussionmentioning

confidence: 63%

“…In another study, SVM, decision trees, and logistic regression were applied to produce landslide susceptibility maps in the Fruška Gora Mountain area of Serbia. These models obtained an AUC value of 0.9 and integrated deep neural networks with Henry's gas solubility optimization algorithm to assess landslide susceptibility in the Ha Giang province of Vietnam (Nguyen et al, 2021). The AUC value of this model was 0.86.…”

Section: Discussionmentioning

confidence: 99%

Multi‐hazard assessment using machine learning and remote sensing in the North Central region of Vietnam

Nguyen

Dang

Bui

et al. 2023

Transactions in GIS

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Natural hazards constitute a diverse category and are unevenly distributed in time and space. This hinders predictive efforts, leading to significant impacts on human life and economies. Multi‐hazard prediction is vital for any natural hazard risk management plan. The main objective of this study was the development of a multi‐hazard susceptibility mapping framework, by combining two natural hazards—flooding and landslides—in the North Central region of Vietnam. This was accomplished using support vector machines, random forest, and AdaBoost. The input data consisted of 4591 flood points, 1315 landslide points, and 13 conditioning factors, split into training (70%), and testing (30%) datasets. The accuracy of the models' predictions was evaluated using the statistical indices root mean square error, area under curve (AUC), mean absolute error, and coefficient of determination. All proposed models were good at predicting multi‐hazard susceptibility, with AUC values over 0.95. Among them, the AUC value for the support vector machine model was 0.98 and 0.99 for landslide and flood, respectively. For the random forest model, these values were 0.98 and 0.98, and for AdaBoost, they were 0.99 and 0.99. The multi‐hazard maps were built by combining the landslide and flood susceptibility maps. The results showed that approximately 60% of the study area was affected by landslides, 30% by flood, and 8% by both hazards. These results illustrate how North Central is one of the regions of Vietnam that is most severely affected by natural hazards, particularly flooding, and landslides. The proposed models adapt to evaluate multi‐hazard susceptibility at different scales, although expert intervention is also required, to optimize the algorithms. Multi‐hazard maps can provide a valuable point of reference for decision makers in sustainable land‐use planning and infrastructure development in regions faced with multiple hazards, and to prevent and reduce more effectively the frequency of floods and landslides and their damage to human life and property.

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Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

“…Human activities such as hunting, illegal logging, picnics, and smoking are among the main causes of wildfires [118][119][120][121][122]. Previous studies have shown that the distance from roads and residential areas are important human-caused factors in wildfires [10,[121][122][123][124][125]. Roads are a key link between human activities and forests, and short distances from residential areas can lead to more human intervention in the forest ecosystem [126].…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Wildfire Susceptibility Mapping Using Deep Learning Algorithms in Two Satellite Imagery Dataset

Bahadori,

Razavi-Termeh,

Sadeghi-Niaraki

et al. 2023

Forests

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Recurring wildfires pose a critical global issue as they undermine social and economic stability and jeopardize human lives. To effectively manage disasters and bolster community resilience, the development of wildfire susceptibility maps (WFSMs) has emerged as a crucial undertaking in recent years. In this research endeavor, two deep learning algorithms were leveraged to generate WFSMs using two distinct remote sensing datasets. Specifically, the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat-8 images were utilized to monitor wildfires that transpired during the year 2021. To develop an effective WFSM, two datasets were created by incorporating 599 wildfire locations with Landsat-8 images and 232 sites with MODIS images, as well as twelve factors influencing wildfires. Deep learning algorithms, namely the long short-term memory (LSTM) and recurrent neural network (RNN), were utilized to model wildfire susceptibility using the two datasets. Subsequently, four WFSMs were generated using the LSTM (MODIS), LSTM (Landsat-8), RNN (MODIS), and RNN (Landsat-8) algorithms. The evaluation of the WFSMs was performed using the area under the receiver operating characteristic (ROC) curve (AUC) index. The results revealed that the RNN (MODIS) (AUC = 0.971), RNN (Landsat-8) (AUC = 0.966), LSTM (MODIS) (AUC = 0.964), and LSTM (Landsat-8) (AUC = 0.941) algorithms demonstrated the highest modeling accuracy, respectively. Moreover, the Gini index was employed to assess the impact of the twelve factors on wildfires in the study area. The results of the random forest (RF) algorithm indicated that temperature, wind speed, slope, and topographic wetness index (TWI) parameters had a significant effect on wildfires in the study region. These findings are instrumental in facilitating efficient wildfire management and enhancing community resilience against the detrimental effects of wildfires.

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Henry’s gas solubility optimization algorithm in formulating deep neural network for landslide susceptibility assessment in mountainous areas

Cited by 12 publications

References 47 publications

Football Match Scoring Method Based on Adaptive Neural Network Algorithm

Football Match Scoring Method Based on Adaptive Neural Network Algorithm

Multi‐hazard assessment using machine learning and remote sensing in the North Central region of Vietnam

Wildfire Susceptibility Mapping Using Deep Learning Algorithms in Two Satellite Imagery Dataset

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