scholarly journals Spatio-Temporal Groundwater Drought Monitoring Using Multi-Satellite Data Based on an Artificial Neural Network

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1953 ◽  
Author(s):  
Seo ◽  
Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Satellite Data ◽  
Severity Index ◽  
Drought Severity ◽  
Human Society ◽  
Groundwater Storage ◽  
Groundwater Drought ◽  
Artificial Neural ◽  
Spatio Temporal

Drought is a complex phenomenon caused by lack of precipitation that affects water resources and human society. Groundwater drought is difficult to assess due to its complexity and the lack of spatio-temporal groundwater observations. In this study, we present an approach to evaluate groundwater drought based on relatively high spatial resolution groundwater storage change data. We developed an artificial neural network (ANN) that employed satellite data (Gravity Recovery and Climate Experiment (GRACE) and Tropical Rainfall Measuring Mission (TRMM)) as well as Global Land Data Assimilation System (GLDAS) models. The Standardized Groundwater Level Index (SGI) was calculated by normalizing ANN-predicted groundwater storage changes from 2003 to 2015 across South Korea. The ANN-predicted 25 km groundwater storage changes correlated well with both the in situ and the water balance equation (WBE)-estimated groundwater storage changes, with mean correlation coefficients of 0.87 and 0.64, respectively. The Standardized Precipitation–Evapotranspiration Index (SPEI), having an accumulation time of 1–6 months, and the Palmer Drought Severity Index (PDSI) were used to validate the SGI. The results showed that the SGI had a pattern similar to that of SPEI-1 and SPEI-2 (1- and 2-month accumulation periods, respectively), and PDSI. However, the SGI performance fluctuated slightly due to its relatively short study period (13 years) as compared to SPEI and PDSI (more than 30 years). The SGI, which was developed using a new approach in this study, captured the characteristics of groundwater drought, thus presenting a framework for the assessment of these characteristics.

scholarly journals Artificial Neural Network coupled Condition Monitoring for advanced Fault Diagnosis of Engine

2021 ◽  
Author(s):  
Suvendu Mohanty ◽  
Swarup Paul ◽  
Soudip Hazra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Engine Performance ◽  
Wear Particle ◽  
Severity Index ◽  
Ann Model ◽  
Artificial Neural ◽  
Engine Failure ◽  
Artificial Neural Network Ann ◽  
Nonlinear Autoregressive

Abstract This paper reflects on the use of the Artificial Neural Network ( ANN) approach to diagnose and interpret engine failure behaviour. The current research focuses on the analysis of quantitative wear trend patterns through Condition Tracking (CM) and soft computational approaches. Oil analysis has been carried out to observe the engine failure trend. An ANN model using a Nonlinear Autoregressive with Exogenous Input (NARX) architecture has been employed to predict quantitative outputs such as Wear Particle Concentration (WPC), Wear Severity Index (WSI), Severity Index (SI) and Percentage of Large Particle (PLP) in connection with input functions of Engine Running Hours, RPM and oil temperature. Correlation function and error similarity are statistically evaluated to represent the model's robustness and effectively chart the loss input-output sequence. The subsequent ANN model demonstrates the capabilities for advance diagnosis and better prediction of engine performance.

Application of Artificial Neural Network for Automatic Contingency Analysis in Power Security Assessment

2006 ◽  
Vol 3 (1) ◽  
pp. 11
Author(s):  
Ismail Musirin ◽  
Titik Khawa Abdul Rahman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Severity Index ◽  
System Stability ◽  
Security Assessment ◽  
Contingency Analysis ◽  
Data Set ◽  
Power Failure ◽  
Test Systems ◽  
Artificial Neural

Several incidents that occurred around the world involving power failure caused by unscheduled line outages were identified as one of the main contributors to power failure and cascading blackout in electric power environment. With the advancement of computer technologies, artificial intelligence (AI) has been widely accepted as one method that can be applied to predict the occurrence of unscheduled disturbance. This paper presents the development of automatic contingency analysis and ranking algorithm for the application in the Artificial Neural Network (ANN). The ANN is developed in order to predict the post-outage severity index from a set of pre-outage data set. Data were generated using the newly developed automatic contingency analysis and ranking (ACAR) algorithm. Tests were conducted on the 24-bus IEEE Reliability Test Systems. Results showed that the developed technique is feasible to be implemented practically and an agreement was achieved in the results obtained from the tests. The developed ACAR can be utilised for further testing and implementation in other IEEE RTS test systems particularly in the system, which required fast computation time. On the other hand, the developed ANN can be used for predicting the post-outage severity index and hence system stability can be evaluated.

Modeling the Spatio-Temporal Evolution of Chlorophyll-a in Three Tropical Rivers Comoé, Bandama, and Bia Rivers (Côte d’Ivoire) by Artificial Neural Network

Wetlands ◽  
2020 ◽  
Vol 40 (5) ◽  
pp. 939-956
Author(s):  
Maley-Pacôme Soro ◽  
Koffi Marcellin Yao ◽  
N’Guessan Louis Berenger Kouassi ◽  
Ahmed Abauriet Ouattara ◽  
Thomas Diaco
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Chlorophyll A ◽  
Cote D'ivoire ◽  
Côte D’Ivoire ◽  
Temporal Evolution ◽  
Tropical Rivers ◽  
Cote D’Ivoire ◽  
Artificial Neural ◽  
Spatio Temporal
Sign in / Sign up

Export Citation Format

Share Document