RAINFALL RUNOFF MODELING BY MULTILAYER PERCEPTRON NEURAL NETWORK FOR LUI RIVER CATCHMENT

2016 ◽  
Vol 78 (6-12) ◽  
Author(s):  
Nadeem Nawaz ◽  
Sobri Harun ◽  
Rawshan Othman ◽  
Arien Heryansyah
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Moving Average ◽  
Rainfall Runoff ◽  
Runoff Modeling ◽  
Hourly Rainfall ◽  
Physically Based ◽  
Auto Regressive ◽  
Event Based ◽  
Relationship Of

Reliable modeling for the rainfall-runoff processes embedded with high complexity and non-linearity can overcome the problems associated with managing a watershed. Physically based rainfall-runoff models need many realistic physical components and parameters which are sometime missing and hard to be estimated. In last decades the artificial intelligence (AI) has gained much popularity for calibrating the nonlinear relationships of rainfall–runoff processes. The AI models have the ability to provide direct relationship of the input to the desired output without considering any internal processes. This study presents an application of Multilayer Perceptron neural network (MLPNN) for the continuous and event based rainfall-runoff modeling to evaluate its performance for a tropical catchment of Lui River in Malaysia. Five years (1999-2013) daily and hourly rainfall and runoff data was used in this study. Rainfall-runoff processes were also simulated with a traditionally used statistical modeling technique known as auto-regressive moving average with exogenous inputs (ARMAX). The study has found that MLPNN model can be used as reliable rainfall-runoff modeling tool in tropical catchments.  

EVENT-BASED RAINFALL-RUNOFF MODELING USING ADAPTIVE NETWORK-BASED FUZZY INFERENCE SYSTEM

2016 ◽  
Vol 78 (9-4) ◽  
Author(s):  
Nadeem Nawaz ◽  
Sobri Harun ◽  
Amin Talei ◽  
Tak Kwin Chang
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Bench Mark ◽  
Physical Parameters ◽  
Rainfall Runoff ◽  
Adaptive Network ◽  
Inference System ◽  
Runoff Modeling ◽  
Hourly Rainfall ◽  
Event Based

Population growth and transformation of agricultural or forest landscapes to built-up areas are the common phenomenon in the fast developing countries. Such changes have significant impact on hydrologic processes in the catchment which in turn may end up with an increase in both magnitude and frequency of floods in urban areas. Therefore, reliable rainfall-runoff models that are able to estimate discharge of a catchment accurately are in need. To date, several physically-based models are developed to capture the rainfall-runoff process; however, they require significant number of parameters which could be difficult to be measured or estimated. Beside these models, the artificial intelligence techniques have shown their ability to identify a direct mapping between inputs and outputs with less number of physical parameters. Adaptive network-based fuzzy inference system (ANFIS) is one of the well-practiced techniques in hydrological time series modeling. The aim of this study was to check the capability of ANFIS in event-based rainfall runoff modeling for a tropical catchment. A total of 70 rainfall-runoff events were extracted from twelve years hourly rainfall and runoff data of Semenyih River catchment where 50 of them were chosen for training and the remaining 20 for testing. An input selection method based on correlation analysis and mutual information was developed to identify the proper input combinations of rainfall and discharge antecedents. The results obtained by ANFIS model were then compared with an autoregressive model with exogenous inputs (ARX) as a bench mark. Results showed that ANFIS outperforms ARX model and has capabilities to be used as a reliable rainfall-runoff modeling tool.

Modelling and control of greenhouse system using neural networks

2016 ◽  
Vol 40 (3) ◽  
pp. 918-929 ◽  
Author(s):  
A Manonmani ◽  
T Thyagarajan ◽  
M Elango ◽  
S Sutha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Moving Average ◽  
Growth Conditions ◽  
Operating Conditions ◽  
Adverse Weather ◽  
Control Schemes ◽  
Non Linear ◽  
Auto Regressive ◽  
And Control

A greenhouse system (GHS) is a closed structure that facilitates modified growth conditions to crops and provides protection from pests, diseases and adverse weather. However, a GHS exhibits non-linearity due to the interaction between the biological subsystem and the physical subsystem. Non-linear systems are difficult to control, particularly when their characteristics change with time. These systems are best handled with methods of computation intelligence, such as artificial neural networks (ANNs) and fuzzy systems. In the present work, the approximation capability of a neural network is used to model and control sufficient growth conditions of a GHS. An optimal neural network-based non-linear auto regressive with exogenous input (NARX) time series model is developed for a GHS. Based on the NARX model, two intelligent control schemes, namely a neural predictive controller (NPC) and non-linear auto regressive moving average (NARMA-L2) controller are proposed to achieve the desired growth conditions such as humidity and temperature for a better yield. Finally, closed-loop performances of the above two control schemes for servo and regulatory operations are analysed for various operating conditions using performance indices.

A Structural Identification Method Based on Recurrent Neural Network and Auto-Regressive and Moving Average Model

2012 ◽  
Vol 256-259 ◽  
pp. 2261-2265
Author(s):  
Jing Xu ◽  
Xiu Li Wang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Moving Average ◽  
Structural Identification ◽  
Modal Parameters ◽  
Average Model ◽  
Identification Method ◽  
Moving Average Model ◽  
Nonlinear Relation ◽  
Auto Regressive

The work presented a structural identification method based on recurrent neural network and auto-regressive and moving average model. The proposed approach involves two steps. The first step is to build a recurrent neural network to map the complex nonlinear relation between the excitations and responses of the structure-unknown system by on-line learning . The second step is to propose a procedure to determine the modal parameters of the structure from the trained neural networks. The dynamic characteristics of the structure are directly evaluated from the weighting matrices of the trained recurrent neural network. Furthermore, a illustrative example demonstrates the feasibility of using the proposed method to identify modal parameters of structure-unknown systems.

Development and Integration of Sub-hourly Rainfall–Runoff Modeling Capability Within a Watershed Model

2010 ◽  
Vol 24 (15) ◽  
pp. 4505-4527 ◽  
Author(s):  
Jaehak Jeong ◽  
Narayanan Kannan ◽  
Jeff Arnold ◽  
Roger Glick ◽  
Leila Gosselink ◽  
...  
Keyword(s):  
Rainfall Runoff ◽  
Watershed Model ◽  
Runoff Modeling ◽  
Hourly Rainfall
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