scholarly journals Forecasting Reservoir Water Level-A Case Study of Bhadar-1 using Artificial Neural Network

2018 ◽  
Vol 6 (4) ◽  
pp. 4188-4194
Author(s):  
Srushti U. Joshi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Artificial Neural

Dam Safety Reliability Analysis Based on Artificial Neural Network

2011 ◽  
Vol 255-260 ◽  
pp. 3620-3625
Author(s):  
Hai Wei ◽  
Hua Shu Yang ◽  
Liang Wu ◽  
Yue Gui
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Reliability Index ◽  
Safety Evaluation ◽  
Mathematic Model ◽  
Dam Safety ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Artificial Neural

There are many factors, such as climate, flood, material, geology, structure, management, to influence dam safety. So dam safety evaluation, involving many fields, is very complicated, and very difficult to establish mathematic model for assessment. Artificial Neural Network (ANN) has many obvious advantages to deal with these problems influenced by multi-factor, consequently is widely used in engineering fields. This paper considered water level, temperature, main factors influencing dam deformation, as random variables, employed ANN and statistical model to establish performance function of dam hidden trouble deformation and abnormal deformation. Then reliability theory was used to analyze dam safety reliability and sensitivity. The results show that temperature has great effect on probability of dam hidden trouble deformation and abnormal deformation than reservoir water level, due to great variability of temperature. Change of Reliability index of dam is contrary to reservoir water level. Temperature, especially average temperature in 10 days and 5 days, has great effect on sensitivity of reliability index than water level.

scholarly journals The Water Level and Outflow Prediction Using the Artificial Neural Network (ANN) for the Management of the Reservoir Flooding

2019 ◽  
Vol 8 (10) ◽  
pp. 4699-4706
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Discharge Rate ◽  
Reservoir Management ◽  
Normal Operation ◽  
Ann Model ◽  
Reservoir Water ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The aim of operation reservoir during flood is to prevent overflow that endangers the dams. It is also to prevent flooding in the downstream of the dam, which leads to loss of life and property. This aim can be achieved with optimal reservoir management which is influenced by the reservoir’s condition during flooding such as: rain, reservoir storage, inflow, water level, and discharge of reservoir water released to the downstream. The successfully of the reservoir management depends on the accuracy of the estimated a). water level (due to the inflow of the reservoir) and b). outflow from the reservoir. One of the models which can be used to predict the water level and reservoir water released during flooding is the Artificial Neural Network (ANN). ANN can simulates flood events that are similar in fact to the previous occurence In this study a backpropagation ANN model was applied to the Wonogiri Reservoir in Central Java, Indonesia. The optimal ANN architecture produced in this study are the Input Pattern of 5-3-4 (which has a rain input recorded 1 – 5 hours earlier, a water level input recorded 1 – 3 hours earlier and a release input recorded 1 – 4 hours earlier). 27 pieces hidden layer, total epoch which is 200 and the learning rate of 0.01. The output is predicting the water level, the Outflow and Gate Opening of Reservoir. The current flood data was applied to the above model and it was concluded that the network can follow the flood management pattern adequately. In addition, the network is extra flexible with a lower flood discharge rate; and has the final elevation of the reservoir slightly lower than the normal operation.

scholarly journals Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2011
Author(s):  
Pablo Páliz Larrea ◽  
Xavier Zapata Ríos ◽  
Lenin Campozano Parra
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Clustering Algorithm ◽  
Network Models ◽  
Water Levels ◽  
Subtractive Clustering ◽  
Neural Network Models ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Maximum Delay

Despite the importance of dams for water distribution of various uses, adequate forecasting on a day-to-day scale is still in great need of intensive study worldwide. Machine learning models have had a wide application in water resource studies and have shown satisfactory results, including the time series forecasting of water levels and dam flows. In this study, neural network models (NN) and adaptive neuro-fuzzy inference systems (ANFIS) models were generated to forecast the water level of the Salve Faccha reservoir, which supplies water to Quito, the Capital of Ecuador. For NN, a non-linear input–output net with a maximum delay of 13 days was used with variation in the number of nodes and hidden layers. For ANFIS, after up to four days of delay, the subtractive clustering algorithm was used with a hyperparameter variation from 0.5 to 0.8. The results indicate that precipitation was not influencing input in the prediction of the reservoir water level. The best neural network and ANFIS models showed high performance, with a r > 0.95, a Nash index > 0.95, and a RMSE < 0.1. The best the neural network model was t + 4, and the best ANFIS model was model t + 6.

Knowledge-Based Design of Artificial Neural Network Topology for Additive Manufacturing Process Modeling: A New Approach and Case Study for Fused Deposition Modeling

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Hari P. N. Nagarajan ◽  
Hossein Mokhtarian ◽  
Hesam Jafarian ◽  
Saoussen Dimassi ◽  
Shahriar Bakrani-Balani ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Additive Manufacturing ◽  
Fused Deposition Modeling ◽  
Expert Knowledge ◽  
Fused Deposition ◽  
Knowledge Based ◽  
Deposition Modeling ◽  
Artificial Neural

Additive manufacturing (AM) continues to rise in popularity due to its various advantages over traditional manufacturing processes. AM interests industry, but achieving repeatable production quality remains problematic for many AM technologies. Thus, modeling different process variables in AM using machine learning can be highly beneficial in creating useful knowledge of the process. Such developed artificial neural network (ANN) models would aid designers and manufacturers to make informed decisions about their products and processes. However, it is challenging to define an appropriate ANN topology that captures the AM system behavior. Toward that goal, an approach combining dimensional analysis conceptual modeling (DACM) and classical ANNs is proposed to create a new type of knowledge-based ANN (KB-ANN). This approach integrates existing literature and expert knowledge of the AM process to define a topology for the KB-ANN model. The proposed KB-ANN is a hybrid learning network that encompasses topological zones derived from knowledge of the process and other zones where missing knowledge is modeled using classical ANNs. The usefulness of the method is demonstrated using a case study to model wall thickness, part height, and total part mass in a fused deposition modeling (FDM) process. The KB-ANN-based model for FDM has the same performance with better generalization capabilities using fewer weights trained, when compared to a classical ANN.

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