Machine learning in telemetry data mining of space mission: basics, challenging and future directions

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

doi:10.1007/s10462-019-09760-1

Cited by 56 publications

(27 citation statements)

References 51 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

163

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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

163

View full text Add to dashboard Cite

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“…Enriched in the past 30 years by advancements in computer processor technology and data‐base management systems, AI technologies have gained significant prominence in addressing difficult challenges related to engineering, [ 5,6 ] medicine, [ 7,8 ] economics, [ 9 ] automation, [ 10 ] gaming, [ 11 ] logistics, [ 12 ] finance, [ 13 ] data mining, [ 14 ] and many other disciplines. [ 15–18 ]…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering

Dutta

Upreti

2021

Can J Chem Eng

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In the last three decades, artificial intelligence (AI) has been increasingly and vigorously utilized for process control in chemical, biochemical, and biomedical engineering. These disciplines frequently involve fairly sophisticated processes under risks of operational upsets, and thus have an ever increasing demand of superior control strategies. As the deployment of AI in process control pushes the limits in this regard, the research advances, which are multitudinous and varied, need to be assimilated and organized to help promote further utilization and progress in the field. To that end, we examine more than 280 relevant research publications, and systematically collate the information. The AI‐based technologies are classified, and their over‐arching control paradigm is presented. Common AI‐based control technologies are then presented, which are based on expert systems, fuzzy logic, artificial neural networks, nature‐inspired algorithms, and hybrid approaches. Their working principles, types, and implementations are summarized along with advantages, limitations, and comparisons, if available. Important applications in the above disciplines are included with the help of tables that capture important details. A discussion is also provided on advanced and newly emerging AI‐based control technologies with pertinent applications. Overall trends are analyzed, and future prospects are identified based on the survey.

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“…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%

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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Machine learning in telemetry data mining of space mission: basics, challenging and future directions

Cited by 56 publications

References 51 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

Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering

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

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