Chaos and reproduction in sea level

River flow estimation using records of past time series is importance in water resources engineering and management and is required in hydrologic studies. In the past two decades, the approaches based on the artificial neural networks (ANN) were developed. River flow modeling is a non-linear process and highly affected by the inputs to the modeling. In this study, the best input combination of the models was identified using the Gamma test then MLP-ANN and hybrid multilayer perceptron (MLP-FFA) is used to forecast monthly river flow for a set of time intervals using observed data. The measurements from three gauge at Ajichay watershed, East Azerbaijani, were used to train and test the models approach for the period from January 2004 to July 2016. Calibration and validation were performed within the same period for MLP-ANN and MLP-FFA models after the preparation of the required data. Statistics, the root mean square error and determination coefficient, are used to verify outputs from MLP-ANN to MLP-FFA models. The results show that MLP-FFA model is satisfactory for monthly river flow simulation in study area.

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“…The first local minimum of I( ) estimates the optimal selection for the lag time necessary for input selection (De Domenico et al 2013). …”

Section: Finding Optimum Lag Timementioning

confidence: 99%

River flow simulation using a multilayer perceptron-firefly algorithm model

Darbandi

Pourhosseini

2018

Appl Water Sci

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“…Secara fizikal, evolusi sistem dinamik ini diterjemahkan melalui plot ruang fasa (Domenico et al 2013).…”

Section: Pembinaan Semula Ruang Fasaunclassified

Aplikasi Model Baharu Penambahbaikan Pendekatan Kalut ke atas Peramalan Siri Masa Kepekatan Ozon

Hamid

Noorani²

2017

JSM

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ABSTRAK Kajian ini merupakan aplikasi pendekatan kalut ke atas peramalan siri masa bahan pencemar udara ozon di stesen asas Malaysia yang terletak di (2016) melaporkan bahawa pencemaran ozon meningkatkan kadar kematian kerana ia membawa kepada pelbagai penyakit pernafasan dan kardiovaskular. Oleh itu, pembangunan model peramalan ke atas siri masa bahan pencemar udara ozon adalah penting.Sifat sesebuah siri masa boleh dikelaskan kepada berketentuan atau rawak. Siri masa berketentuan adalah siri masa yang boleh diramal manakala siri masa rawak tidak boleh diramal. Sifat kalut berada antara sifat berketentuan dan rawak (Abarbanel 1996). Siri masa kalut boleh diramal; walau bagaimanapun, disebabkan oleh kebergantungan sensitif kepada keadaan awal, maka, bagi sesebuah siri masa kalut, hanya peramalan jangka pendek dibenarkan (Sprott 2003).Terdapat pelbagai pendekatan yang telah digunakan oleh kajian lepas untuk menguji sama ada siri masa ozon adalah kalut atau tidak. Menggunakan kaedah matra korelasi, kamiran kolerasi, entropi dan eksponen Lyapunov, kajian seperti Chattopadhyay dan Chattopadhyay Plot ruang fasa dan kaedah Cao (Cao 1997) dapat mengklasifikasikan sifat siri masa. Walau bagaimanapun, kaedah ini jarang digunakan ke atas siri masa ozon walaupun kedua-dua telah terbukti berkesan oleh kajian

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“…Chaos theory has been applied to various time series such as river flow [5], sea level [6], carbon monoxide [7] and ozone pollution [8]. Chaos theory has been found to successfully modelling the temperature time series in several countries such as Nigeria [9], Iran [10] and Denmark [11].…”

Section: Introductionmentioning

confidence: 99%

“…In Malaysia, the chaos theory was used by [12]to predict ozone, [13] to predict particulate matter and [14] to predict river flow. Researches by [6] and [8] have employed the method to predict temperature time series. Recently, [22] has applied chaos theory to predict carbon monoxide.…”

Section: Introductionmentioning

confidence: 99%

A Pilot Study Using Chaos Theory to Predict Temperature Time Series in Malaysian Semi Urban Area

et.al¹

2021

TURCOMAT

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Chaos theory draws more attention since it has been widely used in modeling various time series. Changes in temperature can cause bad effect on health and lead to death. Therefore, in this study, chaos theory was applied to the temperature time series. The temperature time series is observed hourly in one of Malaysiansemi urban area namely Tanjong Malim,located in the state of Perak. This pilot study begins by detecting the chaos nature in time series through phase space approach and Cao method. Next, the time series was predicted through the local approximation method, a method based on chaos theory. This study resulted that the nature of the observed temperature time series was chaos. Prediction through the local approximation method was success with correlation coefficient value 0.9138. This shows that there exist a strong relationship between the predicted and observed temperature time series. Therefore, chaos theorywas a good approach that can be used to determine the nature and predict temperature time series in the semi urban area. In implication, this findingwas expected to serve stakeholders such as Ministry of Higher Education, Meteorological Department as well as Department of Environment in temperature time series management.

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Chaos and reproduction in sea level

Cited by 25 publications

References 22 publications

River flow simulation using a multilayer perceptron-firefly algorithm model

River flow simulation using a multilayer perceptron-firefly algorithm model

Aplikasi Model Baharu Penambahbaikan Pendekatan Kalut ke atas Peramalan Siri Masa Kepekatan Ozon

A Pilot Study Using Chaos Theory to Predict Temperature Time Series in Malaysian Semi Urban Area

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