A Water Consumption Forecasting Model by Using a Nonlinear Autoregressive Network with Exogenous Inputs Based on Rough Attributes

Zheng, Yuan; Zhang, Wanjuan; Xie, Jingjing; Liu, Qiao

doi:10.3390/w14030329

Cited by 9 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They become the input data for the NARNN model to predict the output value 𝑦𝑦 �(𝑡𝑡), which is the estimated value at future time 𝑡𝑡. Following the research by Zheng et al (2022), the range of feedback delays is within the number of values in the training set.…”

Section: Number Of Past Valuesmentioning

confidence: 99%

Nonlinear Autoregressive Neural Network for Forecasting COVID-19 Confirmed Cases in Malaysia

ABD RAHMAN

2023

JOSMA

View full text Add to dashboard Cite

A nonlinear autoregressive neural network (NARNN) model is a feedforward neural network for handling complex nonlinear time series problem. In this study, the tangent sigmoid (tansig) activation function with different number of past values and different number of hidden neurons for NARNN model is determined. The COVID-19 daily confirmed cases in Malaysia are collected with different amount of sample used which are 100, 500 and 900. Therefore, data of 100, 500 and 900 days prior to 21 September 2022 are extracted for the NARNN model training, validation and testing procedure. The lowest average mean squared error (MSE) are considered as the best combination. Result shown that the past value 1:10 and number of neurons of 10 when the sample size is 100. At sample size 500, past values of 1:10 and number of neurons of 8 enables the model to perform at its best. Whereas for sample size 900, network setting of 1:5 past value and 5 hidden neurons gives the least MSE. Multi-step ahead time series forecasting is conducted to forecast the number of COVID-19 confirmed cases in 7 days which is from 22 to 28 September 2022. The result shown for 7-days-ahead confirmed cases forecasting Malaysia datasets, the best forecasting outcome occurs when 900 samples are inputted.

show abstract

Section: Number Of Past Valuesmentioning

confidence: 99%

Nonlinear Autoregressive Neural Network for Forecasting COVID-19 Confirmed Cases in Malaysia

ABD RAHMAN

2023

JOSMA

View full text Add to dashboard Cite

show abstract

“…They applied the chaos genetic algorithm and used the support vector machine method for parameter optimization of the algorithm. Zheng et al (2022) applied neural networks and rough set theory to predict water consumption based on time series. This paper aims to offer a comprehensive comparative framework by enabling the application of previously unhybridized approaches and known methods for water consumption estimation.…”

Section: Estimated Annual Water Consumption Values Formentioning

confidence: 99%

Forecasting water demand for Istanbul by applying different machine learning algorithms

Pekel

2022

Preprint

View full text Add to dashboard Cite

This paper applies three machine learning algorithms, namely decision tree, random forest, and AdaBoost, and two hybrid algorithms, particle swarm optimization and genetic algorithm, to monthly water prediction data. Experiments were carried out on the train and test set according to the parameters affecting the performance of the relevant algorithms. Further, the implementations of the performed algorithms are experimentally compared with each other in the training and testing stage by providing graphical illustrations of the İstanbul water consumption dataset. The numerical results indicate that the random forest algorithm has shown very decent results in the training and testing phase by providing the 0.92 R2 and 0.0238 mean absolute percentage error (MAPE) and 0.1493 MAPE and 0.83251 R2 respectively.

show abstract

“…An LSTM network adopts memory blocks that are composed of an input gate, an output gate, a memory cell and a forget gate, and tends to do better on a volatile time series with more of a stationary component [35,36]. The NARX model adds loops to feed the previous input and output back to the network, which can be efficiently applied to the long-term prediction of a nonlinear time series for streamflow forecasting and prediction [37,38]. Rjeily et al utilized the NARX neural network to develop a flood forecasting system with urban drainage [39].…”

Section: Introductionmentioning

confidence: 99%

Application of Rainfall-Runoff Simulation Based on the NARX Dynamic Neural Network Model

Shao

Jing

et al. 2022

Water

View full text Add to dashboard Cite

The research into rainfall-runoff plays a very important role in water resource management. However, runoff simulation is a challenging task due to its complex formation mechanism, time-varying characteristics and nonlinear hydrological dynamic process. In this study, a nonlinear autoregressive model with exogenous input (NARX) is used to simulate the runoff in the Linyi watershed located in the northeastern part of the Huaihe river basin. In order to better evaluate the performance of NARX, a distributed hydrological model, TOPX, is used to simulate the discharge as a reference, and runoff classification by cluster analysis is used to further improve the accuracy of runoff simulation. Based on the four statistics indexes of the Nash–Sutcliffe efficiency (NSE), correlation coefficient (CC), root mean square error (RMSE) and mean relative bias (Bias), the NARX model is capable of simulating the rainfall-runoff dynamic process satisfactorily, although there is a little underestimation of the peak flow. After runoff classification, underestimation has been improved, and discharge simulation driven by NARX based on runoff classification (C-NARX) is well consistent with the observation. It is feasible to take it as a promising method, which also can be seen as a good reference and replacement for the current rainfall-runoff simulation.

show abstract

A Water Consumption Forecasting Model by Using a Nonlinear Autoregressive Network with Exogenous Inputs Based on Rough Attributes

Cited by 9 publications

References 36 publications

Nonlinear Autoregressive Neural Network for Forecasting COVID-19 Confirmed Cases in Malaysia

Nonlinear Autoregressive Neural Network for Forecasting COVID-19 Confirmed Cases in Malaysia

Forecasting water demand for Istanbul by applying different machine learning algorithms

Application of Rainfall-Runoff Simulation Based on the NARX Dynamic Neural Network Model

Contact Info

Product

Resources

About