scholarly journals Dam Inflow Prediction by using Artificial Neural Network Reservoir Computing

2019 ◽  
Vol 9 (2) ◽  
pp. 662-667
Keyword(s):  
Neural Network ◽  
Water Management ◽  
Artificial Neural Network ◽  
Economic Losses ◽  
Reservoir Computing ◽  
Model Complex ◽  
Inflow Forecasting ◽  
Artificial Neural ◽  
Effective Water ◽  
Multipurpose Dam

A multipurpose dam serves multiple modalities like agriculture, hydropower, industry, daily usage. Generally dam water level and inflow are changing throughout the year. So, multipurpose dams require effective water management strategies in place for efficient utilization of water. Discrepancy in water management may lead to significant socio-economic losses and may have effect on agriculture patterns in surrounding areas. Inflow is one of the dynamic driving factors in water management. So accurate inflow forecasting is necessary for effective water management. For inflow forecasting various methods are used by researchers. Among them Auto Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) techniques are most popular. Both of these techniques have shown significant contribution in various domains in regards to forecasting. But they have a common drawback in handling non-stationary inflow patterns. To address this drawback, in this work neural Reservoir Computing technique is used. In this work, Context reverberation network, also known as reservoir computing approach, is applied for inflow forecasting. It comprises of a dynamic neural reservoir. As the nature of a neural reservoir is dynamic, it can easily model complex nonstationary patterns along with stationary ones. Proposed model is applied on daily inflow data of Srisailam Dam which is a multipurpose dam. Here ARIMA and ANN models are compared with Reservoir Computing model. On various evaluation parameters Reservoir computing is proved better than ARIMA and ANN.

Monthly Inflow Forecasting using Autoregressive Artificial Neural Network

2012 ◽  
Vol 12 (20) ◽  
pp. 2139-2147 ◽  
Author(s):  
M. Valipour ◽  
M.E. Banihabib ◽  
S.M.R. Behbahani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inflow Forecasting ◽  
Artificial Neural

scholarly journals Evaluation of the BMA probabilistic inflow forecasts using TIGGE numeric precipitation predictions based on artificial neural network

2018 ◽  
Vol 49 (5) ◽  
pp. 1417-1433 ◽  
Author(s):  
Yixuan Zhong ◽  
Shenglian Guo ◽  
Huanhuan Ba ◽  
Feng Xiong ◽  
Fi-John Chang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bayesian Model Averaging ◽  
Weather Prediction ◽  
Lead Times ◽  
Hydrologic Modelling ◽  
Probabilistic Forecasts ◽  
Inflow Forecasting ◽  
Artificial Neural ◽  
Reservoir Basin

Abstract Reservoir inflow forecasting is a crucial task for reservoir management. Without considering precipitation predictions, the lead time for inflow is subject to the concentration time of precipitation in the basin. With the development of numeric weather prediction (NWP) techniques, it is possible to forecast inflows with long lead times. Since larger uncertainty usually occurs during the forecasting process, much attention has been paid to probabilistic forecasts, which uses a probabilistic distribution function instead of a deterministic value to predict the future status. In this study, we aim at establishing a probabilistic inflow forecasting scheme in the Danjiangkou reservoir basin based on NWP data retrieved from the Interactive Grand Global Ensemble (TIGGE) database by using the Bayesian model averaging (BMA) method, and evaluating the skills of the probabilistic inflow forecasts. An artificial neural network (ANN) is used to implement hydrologic modelling. Results show that the corrected TIGGE NWP data can be applied sufficiently to inflow forecasting at 1–3 d lead times. Despite the fact that the raw ensemble inflow forecasts are unreliable, the BMA probabilistic inflow forecasts perform much better than the raw ensemble forecasts in terms of probabilistic style and deterministic style, indicating the established scheme can offer a useful approach to probabilistic inflow forecasting.

scholarly journals Development of a Cyberattack Detection Model for a Water Distribution System using Water Quality and Hydraulic Criteria

2020 ◽  
Vol 20 (5) ◽  
pp. 47-56
Author(s):  
Kyoung Won Min ◽  
Young Hwan Choi ◽  
Joong Hoon Kim
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Distribution Systems ◽  
Water Distribution ◽  
Detection Performance ◽  
Economic Losses ◽  
Water Quality Criteria ◽  
Quality Factors ◽  
Artificial Neural

In recent years, Cyber-Physical Systems (CPSs) have been applied to Water Distribution Systems (WDSs) to facilitate efficient operation and maintenance. Since data are transmitted through the network in such systems, a cyberattack can disrupt the operation of WDSs, for example, by causing water supply reduction, water pollution, and economic losses. In the past few years, cyberattack detection algorithms and various cyberattack scenarios have been proposed. These studies considered either hydraulic factors, such as pipe velocity, nodal pressure, or tank level, or water quality factors. However, an algorithm which considers only one factor cannot prevent the various problems that may arise, such as water quality issues, and the hydraulic and quality factors have a correlation. Therefore, in this study, a framework was developed by considering both hydraulic and water quality factors. The proposed approach was applied to an artificial neural network model. Performance indicators were used to examine the detection performance according to the parameters of the artificial neural network. By comparing the detection performance when only hydraulic factors were considered and the performance when both hydraulic and water quality factors were considered, the effectiveness of the algorithm that consider both hydraulic and water quality factors was demonstrated. A cyberattack detection algorithm that considers both hydraulic and water quality criteria can be applicable in more realistic scenarios and contribute to the establishment of safe infrastructure for the entire process of designing and operating WDSs with CPSs.

scholarly journals Estimation of Vehicle Side-Slip Angle Using an Artificial Neural Network

2018 ◽  
Vol 166 ◽  
pp. 02001 ◽  
Author(s):  
Daniel Chindamo ◽  
Marco Gadola
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Point Of View ◽  
Dynamics Simulation ◽  
Slip Angle ◽  
Computational Point ◽  
Side Slip Angle ◽  
Model Complex ◽  
Active Safety Systems ◽  
Artificial Neural

In this work, a reliable and effective method to predict the vehicle side-slip angle is given by means of an artificial neural network. It is well known that artificial neural networks are a very powerful modelling tool. They are largely used in many engineering fields to model complex and strongly non-linear systems. For this application, the network has to be as simple as possible in order to work in real-time within built-in applications such as active safety systems. The network has been trained with the data coming from a custom manoeuvre designed in order to keep the method simple and light from the computational point of view. Therefore, a 5-10-1 (input-hidden-output layer) network layout has been used. These aspects allow the network to give a proper estimation despite its simplicity. The proposed methodology has been tested by means of the CarSim® simulation package, which is considered one of the reference tools in the field of vehicle dynamics simulation. To prove the effectiveness of the method, tests have been carried out under different adherence conditions.

The Influence of Changes in ANN Hidden Layer Unit and Feature Selection on Classification

2021 ◽  
Vol 9 (3) ◽  
pp. 351
Author(s):  
Sawendo Eko Wijana ◽  
I Gede Santi Astawa ◽  
AAIN Eka Karyawati
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Feature Selection ◽  
Liver Disease ◽  
Multilayer Perceptron ◽  
Computation Time ◽  
Model Complex ◽  
Artificial Neural ◽  
Hidden Layer

Abstract Classification is the process of differentiating a set of models into several data classes. There are many methods that can be used for the classification process, one of which is the Artificial Neural Network method. Neural networks are a computational method that mimics biological syafar networks. Artificial condition networks can be used to model complex relationships between input and output to recognize patterns in data [1]. In this study, testing was conducted to determine the effect of uncorrelated or low-correlation features in the data classification process and the effect of changing the number of units in the hidden layer on the classification results. The data used in this study were liver disease dataobtained from the Kaggle Dataset.Where in comparing the results of using feature selection, it is divided into 4 predetermined scenarios through the search for significance values ??with the SPSS correlation test.In the results of the implementation of the Multilayer Perceptron which aims to determine the effect of feature selection on the classification results, the results are that feature selection does not really affect the computation time obtained, and correlated data has more influence on the accuracy obtained when compared to uncorrelated data. In the results of the implementation of the Multilayer Perceptron which aims to determine the effect of changing the number of hidden layer units on the classification results, the results show that changes in the number of units in the hidden layer in Artificial Neural Networks have increased significantly in accuracy in several scenarios, but the computation time increases if the number of units in the hidden layer increases. Keywords: Classification, Artificial Neural Network, Liver Disease, Accuracy, Time.

Reservoir Inflow Forecasting using Artificial Neural Network

2012 ◽  
Vol 35 (1and2) ◽  
pp. 52 ◽  
Author(s):  
M.U. Kale ◽  
M.B. Nagdeve ◽  
S.B. Wadatkar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inflow Forecasting ◽  
Reservoir Inflow ◽  
Artificial Neural
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