Intelligent Health Monitoring Systems for Space Missions Based on Data Mining Techniques

Abdelghafar, Sara; Darwish, Ashraf; Hassanien, Aboul Ella

doi:10.1007/978-3-030-20212-5_4

Cited by 16 publications

(6 citation statements)

References 30 publications

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“…And secondly, the flood susceptibility zonation maps will lead to overall fund allocation for different zones depending upon the potential damages probable in those zones. As far as future flood scenarios are concerned, since we are heading towards an era of 'space based monitoring, analysis, assessment and evaluation" of all natural (both biotic and abiotic) and manmade phenomena, the intensive testing (Abdelghafar et al, 2020;Alsdorf et al, 2003;DeVries et al, 2020;Green et al, 2020;Hassanien et al, 2020;Kummerow et al, 2020;Malinowska et al, 2020;Salisbury et al, 2013;Spyropoulos et al, 2020;Sterckx et al, 2020;Tang et al, 2010;Thies and Bendix, 2011;Tonetti et al, 2020) Topographic Wetness Index (TWI) 0.07 7…”

Section: Discussionmentioning

confidence: 99%

Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India

Arora

Arabameri

Pandey

et al. 2021

Science of The Total Environment

153

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This study is an attempt to quantitatively test and compare novel advanced-machine learning algorithms in terms of their performance in achieving the goal of zonation of predicting flood susceptible areas in a low altitudinal range, sub-tropical floodplain environmental setting like that prevailing in the Middle Ganga Plain (MGP), India. This part of the Ganga floodplain region, which under the influence of undergoing active tectonic regime related subsidence, is the hotbed of annual flood disaster, and is one of the best natural laboratories to test the flood susceptibility models for establishing a universalization of such models in low relief highly flood prone areas. Based on highly sophisticated flood inventory archived for this region, and 12 flood conditioning factors viz. annual rainfall, soil type, stream density, distance from stream, distance from road, Topographic Wetness Index (TWI), altitude, slope aspect, slope, curvature, land use/land cover, and geomorphology, an advanced novel hybrid model Adaptive Neuro Fuzzy Inference System (ANFIS), and three metaheuristic models-based ensembles with ANFIS namely ANFIS-GA (Genetic Algorithm), ANFIS-DE (Differential Evolution), and ANFIS-PSO (Particle Swarm Optimization), have been applied for zonation of the flood susceptible areas. The flood inventory dataset, prepared by satellite based collected flood samples, were apportioned into 70:30 classes to prepare training and validation datasets. One independent validation method, the Area-Under Receiver Operating Characteristic (AUROC) Curve, and other 11 cut-off-dependent model evaluation metrices have helped to conclude that the ANIFS-GA has outhustled other three models with highest success rate AUC = 0.922 and prediction rate AUC = 0.924. The accuracy was also found highest for ANFIS-GA during training (0.886) & validation (0.883).Better performance of ANIFS-GA than the individual models as well as some ensemble models suggests and warrants further study in this topoclimatic environment using other classes of susceptibility models for the purpose of establishing a benchmark model with capability of highest accuracy and sensitivity performance in the similar topographic and climatic setting taking assumption of the quality of input parameters as constant.

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Section: Discussionmentioning

confidence: 99%

Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India

Arora

Arabameri

Pandey

et al. 2021

Science of The Total Environment

153

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“…Satellite telemetry data are largely heterogeneous and multimodal, made up of hundreds to thousands of variables and attributes. In addition, telemetry data is usually collected from different sensors in various subsystems with different output formats and operating modes that must be changed from one mode to another over time ( [122] [129], [237]). According to Yairi et al [14] there has been little work done in artificial satellite telemetry data mining, even though in the last few years the applications of SI in data mining have become increasingly implemented in different fields like bioinformatics and forensic analytics.…”

Section: Nature Of Telemetry Datamentioning

confidence: 99%

Applications and Challenges of Artificial Intelligence in Space Missions

2024

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Artificial Intelligence (AI) is increasingly finding acceptance in the space community, especially Machine Learning (ML), a subdomain of AI. ML algorithms now find numerous applications in autonomous navigation, spacecraft health monitoring and operational management of satellite constellations. However, a large number of surveys on the applications of AI in space missions can be classified into two categories. The first category suffers from the limitation of being old and not covering some crucial and recent developments in the field; such as the contributions of Deep Learning (DL) and bioinspired AI algorithms. The challenge with the second category lies in its being too detailed with respect to the development and application of specific AI techniques or algorithms. These limitations have necessitated the need to have a concise survey with a wider scope for those interested in the applications and challenges of AI in the space industry, especially those with technical backgrounds in other fields. In this paper, we surveyed the use of traditional AI techniques in various domains of space missions without delving into formal methods. Some bioinspired AI algorithms were also surveyed and their potential application areas highlighted. Unlike similar surveys that focus only on technological challenges, we also addressed some crucial legal drawbacks that emanate from the reliance and use of AI in space. Summarily, while discussing future directions we reviewed some advancements in Internet of Things (IoT) and Blockchain technologies. Our review prioritized three application domains positioned to benefit immensely from the inevitable AI-Blockchain convergence in the space community. These include the Internet of Space Things (IoST), Satellite Communication and Spacecraft Data Security.

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“…Whereas if the distance between two grasshoppers is greater than 4, the function s returns values close to zero and cannot apply strong forces to them. To control this defect, the distance between the grasshoppers is normalized to [1,4].…”

Section: A Binary Grasshopper Optimization Algorithmmentioning

confidence: 99%

“…In each iteration, factor c is updated according to Eq. 9, and the distance between the grasshoppers is normalized to [1,4]. In addition, the step vectors, and position vectors are updated according to Eq.…”

Section: A Feature Selection Phasementioning

confidence: 99%

“…The space systems' nonlinearity and complexity characteristics make it difficult to determine these systems' health states using conventional approaches like mathematical models, limit checking, and expert systems. Therefore, data-driven methods have been widely developed to address all aerospace applications' health prognostic and monitoring operations [3], [4].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Hybrid Artificial Intelligence Approach for Fault Diagnosis of Aerospace Systems

et al. 2021

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Due to the space environment's hazards and challenges, aerospace systems are continuously exposed to many failures, such as the degradation of the subsystem performance, sensor faults, connection loss, or equipment damage. Therefore, the effective fault diagnosis for detecting and identifying any failures or unusual behaviors can be recognized as the fundamental and critical role of aerospace systems' predictive health management process. This paper proposes a novel fault diagnosis approach using Deep Learning (DL) technique. DL has recently become a popular approach in artificial intelligence (AI) due to its supremacy in accuracy and fast inference with the huge amount of data and highest order network structures. The proposed approach consists of two main phases; the feature selection phase by Binary Grasshopper Optimization Algorithm (BGOA), and the learning and prediction phase by Artificial Neural Networks (ANNs) with voting ensemble method. The proposed approach named BGOA-EANNs has been validated and evaluated its efficacy by comparing two existing diagnosis techniques using two types of aerospace health diagnosis datasets; satellite power system and aircraft engines. The experimental results demonstrated the effectiveness and superiority of the proposed approach.

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Intelligent Health Monitoring Systems for Space Missions Based on Data Mining Techniques

Cited by 16 publications

References 30 publications

Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India

Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India

Applications and Challenges of Artificial Intelligence in Space Missions

Multi-Objective Hybrid Artificial Intelligence Approach for Fault Diagnosis of Aerospace Systems

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