Water Demand Prediction: A Novel Approach Using Artificial Neural Network

2021 ◽  
Author(s):  
Aiswarya K D ◽  
Amarnath J ◽  
Archana T R ◽  
Haneena P ◽  
Shyba A
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Demand ◽  
Demand Prediction ◽  
Novel Approach ◽  
Water Demand Prediction ◽  
Artificial Neural

scholarly journals Intelligent Control of Agricultural Irrigation through Water Demand Prediction Based on Artificial Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qiuyu Bo ◽  
Wuqun Cheng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intelligent Control ◽  
Water Demand ◽  
Accurate Estimation ◽  
Agricultural Irrigation ◽  
Demand Prediction ◽  
Ecological Water Demand ◽  
Water Demand Prediction ◽  
Artificial Neural

In irrigated areas, the intelligent management and scientific decision-making of agricultural irrigation are premised on the accurate estimation of the ecological water demand for different crops under different spatiotemporal conditions. However, the existing estimation methods are blind, slow, or inaccurate, compared with the index values of the water demand collected in real time from irrigated areas. To solve the problem, this paper innovatively introduces the spatiotemporal features of ecological water demand to the forecast of future water demand by integrating an artificial neural network (ANN) for water demand prediction with the prediction indices of water demand. Firstly, the ecological water demand for agricultural irrigation of crops was calculated, and a radial basis function neural network (RBFNN) was constructed for predicting the water demand of agricultural irrigation. On this basis, an intelligent control strategy was presented for agricultural irrigation based on water demand prediction. The structure of the intelligent control system was fully clarified, and the main program was designed in detail. The proposed model was proved effective through experiments.

A Novel Approach for Gas Turbine Condition Monitoring Combining CUSUM Technique and Artificial Neural Network

2009 ◽  
Author(s):  
Magnus Fast ◽  
Thomas Palme´ ◽  
Magnus Genrup
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gas Turbine ◽  
Condition Monitoring ◽  
Sequential Analysis ◽  
Ann Model ◽  
Novel Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Operational Data

Investigation of a novel condition monitoring approach, combining artificial neural network (ANN) with a sequential analysis technique, has been reported in this paper. For this purpose operational data from a Siemens SGT600 gas turbine has been employed for the training of an ANN model. This ANN model is subsequently used for the prediction of performance parameters of the gas turbine. Simulated anomalies are introduced on two different sets of operational data, acquired one year apart, whereupon this data is compared with corresponding ANN predictions. The cumulative sum (CUSUM) technique is used to improve and facilitate the detection of such anomalies in the gas turbine’s performance. The results are promising, displaying fast detection of small changes and detection of changes even for a degraded gas turbine.

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