New results on stabilization analysis for fuzzy semi-Markov jump chaotic systems with state quantized sampled-data controller

Wu, Tao; Xiong, Lianglin; Cheng, Jun; Xie, Xueqin

doi:10.1016/j.ins.2020.02.051

Cited by 67 publications

(12 citation statements)

References 43 publications

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“…Soil is one of the important factors for occurrences of flood [48]. The pattern of the surface runoff, infiltration rate, and associated inundation largely depends on soil texture [43].Furthermore, soil composition determines the water storage capacity, pattern of drainage channel, and permeability of water which mainly causes inundation [25]. The soil map (Figure 4b) in this area was prepared from the National Bureau of Soil Survey and Land Use planning (NBSSLUP) soil report.…”

Section: Soil Typesmentioning

confidence: 99%

“…Hence, in the last few decades, FS mapping has been carried out through several statistical methods. In recent years, various fuzzy [24][25][26][27][28], deep learning [29][30][31][32][33][34], multiple criteria decisionmaking (MCDM) [35][36][37], and statistical [38][39][40][41][42][43] models have used by researchers. The most used statistical methods in FS assessment are frequency ratio (FR) [38], analytical hierarchy process (AHP) [39], and more.…”

Section: Introductionmentioning

confidence: 99%

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Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms

Saha

Pal

Arabameri

et al. 2021

Water

119

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Recurrent floods are one of the major global threats among people, particularly in developing countries like India, as this nation has a tropical monsoon type of climate. Therefore, flood susceptibility (FS) mapping is indeed necessary to overcome this type of natural hazard phenomena. With this in mind, we evaluated the prediction performance of FS mapping in the Koiya River basin, Eastern India. The present research work was done through preparation of a sophisticated flood inventory map; eight flood conditioning variables were selected based on the topography and hydro-climatological condition, and by applying the novel ensemble approach of hyperpipes (HP) and support vector regression (SVR) machine learning (ML) algorithms. The ensemble approach of HP-SVR was also compared with the stand-alone ML algorithms of HP and SVR. In relative importance of variables, distance to river was the most dominant factor for flood occurrences followed by rainfall, land use land cover (LULC), and normalized difference vegetation index (NDVI). The validation and accuracy assessment of FS maps was done through five popular statistical methods. The result of accuracy evaluation showed that the ensemble approach is the most optimal model (AUC = 0.915, sensitivity = 0.932, specificity = 0.902, accuracy = 0.928 and Kappa = 0.835) in FS assessment, followed by HP (AUC = 0.885) and SVR (AUC = 0.871).

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Section: Soil Typesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms

Saha

Pal

Arabameri

et al. 2021

Water

119

View full text Add to dashboard Cite

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“…In recent years, new techniques and models have been developed by researchers worldwide [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. During the last 6 years, several studies have been individualized regarding the flash-flood susceptibility investigations, which were carried out through the integration of GIS techniques with bivariate statistical models such as: frequency ratio [ 36 ], weights of evidence [ 37 ], statistical index [ 38 ], evidential belief function [ 39 ], certainty factor [ 40 ], or index of entropy [ 41 ].…”

Section: Introductionmentioning

confidence: 99%

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors

Costache

Arabameri

Blaschke

et al. 2021

Sensors

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There is an evident increase in the importance that remote sensing sensors play in the monitoring and evaluation of natural hazards susceptibility and risk. The present study aims to assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS) databases which were involved as input data into a number of four ensemble models. In a first phase, with the help of high-resolution satellite images from the Google Earth application, 481 points affected by torrential processes were acquired, another 481 points being randomly positioned in areas without torrential processes. Seventy percent of the dataset was kept as training data, while the other 30% was assigned to validating sample. Further, in order to train the machine learning models, information regarding the 10 flash-flood predictors was extracted in the training sample locations. Finally, the following four ensembles were used to calculate the Flash-Flood Potential Index across the Bâsca Chiojdului river basin: Deep Learning Neural Network–Frequency Ratio (DLNN-FR), Deep Learning Neural Network–Weights of Evidence (DLNN-WOE), Alternating Decision Trees–Frequency Ratio (ADT-FR) and Alternating Decision Trees–Weights of Evidence (ADT-WOE). The model’s performances were assessed using several statistical metrics. Thus, in terms of Sensitivity, the highest value of 0.985 was achieved by the DLNN-FR model, meanwhile the lowest one (0.866) was assigned to ADT-FR ensemble. Moreover, the specificity analysis shows that the highest value (0.991) was attributed to DLNN-WOE algorithm, while the lowest value (0.892) was achieved by ADT-FR. During the training procedure, the models achieved overall accuracies between 0.878 (ADT-FR) and 0.985 (DLNN-WOE). K-index shows again that the most performant model was DLNN-WOE (0.97). The Flash-Flood Potential Index (FFPI) values revealed that the surfaces with high and very high flash-flood susceptibility cover between 46.57% (DLNN-FR) and 59.38% (ADT-FR) of the study zone. The use of the Receiver Operating Characteristic (ROC) curve for results validation highlights the fact that FFPIDLNN-WOE is characterized by the most precise results with an Area Under Curve of 0.96.

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“…Ibrahim et al 79 added chaotic opposition strategy to the GWO algorithm and uses differential evolution and interruption. An interesting fact about the chaos is that it can yield self-styled quasi-random patterns 80,81 throughout the exploration, which could help the hand routine of GWO in their work.…”

Section: Introductionmentioning

confidence: 99%

Chaotic diffusion‐limited aggregation enhanced grey wolf optimizer: Insights, analysis, binarization, and feature selection

Heidari

Zhang

et al. 2021

Int J of Intelligent Sys

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Grey wolf optimization (GWO) is a widely used metaheuristic method. It has limited searching potential when solving the majority of function optimization problems. This paper proposes a new variant of GWO, named SCGWO, which combines GWO with an improved spread strategy and a chaotic local search (CLS) mechanism to overcome these performance limitations. In detail, a spread strategy is introduced into the basic GWO to change the search agent's ability to avoid the local optima,

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New results on stabilization analysis for fuzzy semi-Markov jump chaotic systems with state quantized sampled-data controller

Cited by 67 publications

References 43 publications

Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms

Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors

Chaotic diffusion‐limited aggregation enhanced grey wolf optimizer: Insights, analysis, binarization, and feature selection

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