Minimum Input Variances for Modelling Rainfall-runoff Using ANN

2014 ◽  
Vol 69 (3) ◽  
Author(s):  
Zulkarnain Hassan ◽  
Supiah Shamsudin ◽  
Sobri Harun
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Conceptual Models ◽  
Data Series ◽  
Rainfall Runoff ◽  
Antecedent Rainfall ◽  
Runoff Series ◽  
Artificial Neural Network Ann ◽  
The Relationship

This paper presents the study of possible input variances for modeling the long-term runoff series using artificial neural network (ANN). ANN has the ability to derive the relationship between the inputs and outputs of a process without the physics being provided to it, and it is believed to be more flexible to be used compared to the conceptual models [1]. Data series from the Kurau River sub-catchment was applied to build the ANN networks and the model was calibrated using the input of rainfall, antecedent rainfall, temperature, antecedent temperature and antecedent runoff. In addition, the results were compared with the conceptual model, named IHACRES. The study reveal that ANN and IHACRES can simulate well for mean runoff but ANN gives a remarkable performance compared to IHACRES, if the model customizes with a good configuration.  

scholarly journals Solving multiple linear regression problem using artificial neural network

2022 ◽  
Vol 12 (1) ◽  
pp. 770
Author(s):  
Mohammad S. Khrisat ◽  
Ziad A. Alqadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Problem ◽  
Simple Method ◽  
Scientific Experiments ◽  
Linear Regression Problem ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Relationship ◽  
Linear Regressions

<span>Multiple linear regressions are an important tool used to find the relationship between a set of variables used in various scientific experiments. In this article we are going to introduce a simple method of solving a multiple rectilinear regressions (MLR) problem that uses an artificial neural network to find the accurate and expected output from MLR problem. Different artificial neural network (ANN) types with different architecture will be tested, the error between the target outputs and the calculated ANN outputs will be investigated. A recommendation of using a certain type of ANN based on the experimental results will be raised.</span>

AN ARTIFICIAL NEURAL NETWORK APPROACH TO HOTEL EMPLOYEE SATISFACTION: THE CASE OF CHINA

2008 ◽  
Vol 36 (4) ◽  
pp. 467-482 ◽  
Author(s):  
Xizhou Tian ◽  
Yongjian Pu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Employee Satisfaction ◽  
High Rate ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Hotel Employee ◽  
And Gender ◽  
Artificial Neural Network Ann

At present, the hotel employment sector in China has a high rate of employee turnover compared to other services. This is not unlike other countries. The reason for the turnover among hotel employees may be lower worker satisfaction resulting in decreased – or no – loyalty to employers. This study was based on an Artificial Neural Network (ANN). The factors influencing employee satisfaction were examined and the impacts of demographic characteristics on hotel employee satisfaction were analyzed. Results show that hotel employee satisfaction in China is low, hotel employee satisfaction differs by age and gender, and that professional development opportunities for employees and the long-term growth prospects of the hotels themselves are the most important contributors to employee satisfaction. On the basis of these findings, several recommendations for improving employee satisfaction, thereby sustaining the long-term economic health of China's hospitality industry, are provided.

scholarly journals Monthly Rainfall-Runoff Modeling for Sondur Reservoir

2021 ◽  
Vol 23 (07) ◽  
pp. 1453-1459
Author(s):  
Shashi Kant Jaiswal ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Model Development ◽  
Monthly Rainfall ◽  
Nonlinear Mapping ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Regression Procedure ◽  
Runoff Modeling ◽  
Artificial Neural Network Ann

This study presents the application of Artificial Neural Network (ANN) to modeling the rainfall-inflow relationship for Sondur Reservoir located in Chhattisgarh State of India. ANNs are usually assumed to be powerful tools for nonlinear mapping in various applications. ANN is superior to linear regression procedure used for rainfallinflow modeling. For model development twenty nine years data of monthly rainfall and inflow have been used. The results extracted from study indicated that the ANN model is efficient for rainfall-inflow modeling.

scholarly journals Artificial Neural Network Analyzing Wearable Device Gait Data for Identifying Patients With Stroke Unable to Return to Work

2021 ◽  
Vol 12 ◽  
Author(s):  
Marco Iosa ◽  
Edda Capodaglio ◽  
Silvia Pelà ◽  
Benedetta Persechino ◽  
Giovanni Morone ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Return To Work ◽  
Measurement Unit ◽  
Walking Ability ◽  
Double Support ◽  
Important Input ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

A potential dramatic effect of long-term disability due to stroke is the inability to return to work. An accurate prognosis and the identification of the parameters inflating the possibility of return to work after neurorehabilitation are crucial. Many factors may influence it, such as mobility and, in particular, walking ability. In this pilot study, two emerging technologies have been combined with the aim of developing a prognostic tool for identifying patients able to return to work: a wearable inertial measurement unit for gait analysis and an artificial neural network (ANN). Compared with more conventional statistics, the ANN showed a higher accuracy in identifying patients with respect to healthy subjects (90.9 vs. 75.8%) and also in identifying the subjects unable to return to work (93.9 vs. 81.8%). In this last analysis, the duration of double support phase resulted the most important input of the ANN. The potentiality of the ANN, developed also in other fields such as marketing on social networks, could allow a powerful support for clinicians that today should manage a large amount of instrumentally recorded parameters in patients with stroke.

scholarly journals OPTIMIZATION RAINFALL-RUNOFF MODELING FOR CIUJUNG RIVER USING BACK PROPAGATION METHOD

SINERGI ◽  
2018 ◽  
Vol 22 (3) ◽  
pp. 193
Author(s):  
Ika Sari Damayanthi Sebayang ◽  
Agus Suroso ◽  
Alnis Gustin Laoli
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Urban Areas ◽  
Back Propagation ◽  
Water Volume ◽  
Rainfall Runoff ◽  
Back Propagation Algorithm ◽  
Runoff Modeling ◽  
Artificial Neural ◽  
The Relationship

The rainfall-runoff model is required to ascertain the relationship between rainfall and runoff. Hydrologists are often confronted with problems of prediction and estimation of runoff using the rainfall date. In actual fact the relationship of rainfall-runoff is known to be highly non-linear and complex. The spatial and temporal precipitation patterns and the variability of watershed characteristics create a more complex hydrologic phenomenon. Runoff is part of the rain water that enters and flows and enters the river body. Rainfall-runoff modeling in this study using Artificial Neural Network, back propagation method and sigmoid binary activation function. This model is used to simulate single or long-term continuous events, water volume, making it very appropriate for urban areas. Back propagation is an inherited learning algorithm and is commonly used by perceptron with multiple layers to change the weights associated with neurons in the hidden layer. Back propagation algorithm uses output error to change the values of its weight in the backward direction. The location of the review is the Ciujung River Basin (DAS), the data used are rainfall and debit data of Ciujung River from 2011-2017. Based on training and simulation results, obtained R2 value: 2012 = 0,85102; 2013 = 0,78661; 2014 = 0,81188; 2015 = 0,77902; 2016 = 0,7279. on model 2 = 0,8724. On model 3 R2:  January = 0,96937; February = 0,92984; March = 0,90666; April = 0,92566; May = 0,9128; June = 0,87975; July= 0,85292; August = 0,95943; September = 0,88229; October = 0,90537; November = 0,93522; December = 0,9111. with MSE (Mean Squared Error) of 0,0018479. The closer value of MSE to 0 and the value of R2 close to 1 then the better designed artificial neural network. If the data used for training more, the artificial neural network will produce a larger R2 value.

scholarly journals A Comparison of Emotional Neural Network (ENN) and Artificial Neural Network (ANN) Approach for Rainfall-Runoff Modelling

2019 ◽  
Vol 5 (10) ◽  
pp. 2120-2130 ◽  
Author(s):  
Suraj Kumar ◽  
Thendiyath Roshni ◽  
Dar Himayoun
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Sets ◽  
Rainfall Runoff ◽  
Data Set ◽  
Discharge Data ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Emotional Neural Network ◽  
Selection Of

Reliable method of rainfall-runoff modeling is a prerequisite for proper management and mitigation of extreme events such as floods. The objective of this paper is to contrasts the hydrological execution of Emotional Neural Network (ENN) and Artificial Neural Network (ANN) for modelling rainfall-runoff in the Sone Command, Bihar as this area experiences flood due to heavy rainfall. ENN is a modified version of ANN as it includes neural parameters which enhance the network learning process. Selection of inputs is a crucial task for rainfall-runoff model. This paper utilizes cross correlation analysis for the selection of potential predictors. Three sets of input data: Set 1, Set 2 and Set 3 have been prepared using weather and discharge data of 2 raingauge stations and 1 discharge station located in the command for the period 1986-2014.  Principal Component Analysis (PCA) has then been performed on the selected data sets for selection of data sets showing principal tendencies.  The data sets obtained after PCA have then been used in the model development of ENN and ANN models. Performance indices were performed for the developed model for three data sets. The results obtained from Set 2 showed that ENN with R= 0.933, R2 = 0.870, Nash Sutcliffe = 0.8689, RMSE = 276.1359 and Relative Peak Error = 0.00879 outperforms ANN in simulating the discharge. Therefore, ENN model is suggested as a better model for rainfall-runoff discharge in the Sone command, Bihar.

scholarly journals APPLICABILITY OF ARTIFICIAL NEURAL NETWORK MODEL FOR SIMULATION OF MONTHLY RUNOFF IN COMPARISON WITH SOM OTHER TRADITIONAL MODELS

2009 ◽  
Vol 12 (4) ◽  
pp. 94-106 ◽  
Author(s):  
Duc Van Le
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Research Trend ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Runoff Forecasting ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Artificial Neural Network (ANN) model along with Back Propagation Algorithm (BPA) has been applied in many fields, especially in hydrology and water resources management to simulate or forecast rainfall runoff process, discharge and water level - time series, and other hydrological variables. Several researches have recently been focusing to compare the applicability of ANN model with other theory-driven and data-driven approaches. The comparison of ANN with M5 model trees for rainfall-runoff forecasting, with ARMAX models for deriving flow series, with AR models and regression models for forecasting and estimating daily river flows have been carried out. The better results that were implemented by ANN model have been concluded. So, this research trend is continued for the comparison of ANN model with Tank, Harmonic, Thomas and Fiering models in simulation of the monthly runoffs at Dong Nai river basin, Viet Nam. The results proved ANN being the best choice among these models, if suitable and enough data sources were available.

scholarly journals Application of Soft Computing (ANN) Techniques to study the relationship between Solar Activity Features and Total Column Ozone

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Seema Pande ◽  
Mahesh Chandra Mathpal ◽  
Bimal Pande
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Activity ◽  
Solar Flares ◽  
Total Column Ozone ◽  
Active Prominence ◽  
Artificial Neural Network Ann ◽  
Column Ozone ◽  
The Relationship ◽  
Total Column

Using 30 years data (1986-2015) we have made an attempt to study the dependency of total column ozone (TCO) on solar activity features: solar flares (SF), solar active prominence (SAP) and sunspot numbers (SN) for two hill stations of Uttarakhand viz. Nainital (29.40 N.79.470E) and Mussorie (30.270 N 78.060 E) by Artificial neural network (ANN) technique. Our study supports the fact that solar activity features contribute to the production of ozone.

Sign in / Sign up

Export Citation Format

Share Document