Modeling minimum temperature using adaptive neuro-fuzzy inference system based on spectral analysis of climate indices: A case study in Iran

Daneshmand, Hojatollah; Tavousi, Taghi; Khosravi, Mahmood; Tavakoli, Saeed

doi:10.1016/j.jssas.2013.06.001

Cited by 24 publications

(18 citation statements)

References 15 publications

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“…(2008) reported that ANN models had desirable performance for the same time interval [9]. Daneshmand et al (2013) also suggested that ANFIS had desirable performance in the prediction of daily minimum temperatures of Mashhad City located in the east of Iran [12]. Similar to the previous studies, in this study, also the artificial intelligence models provided desirable performance.…”

Section: One Month Forecasting Horizonsupporting

confidence: 81%

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Application of Heuristic Algorithms in Improving Performance of Soft Computing Models for Prediction of Min, Mean and Max Air Temperatures

Pirayesh¹,

Farzin²,

Malekani³

et al. 2019

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Traditionally, climate conditions have been one of the influential factors in population growth in worldwide. Hence, predicting these conditions can be an important step to improve life conditions in worldwide. In this study, application of genetic algorithm (GA) and particle swarm algorithm (PSO) were considered as alternatives to available algorithms for training artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in order to predict air temperature. Therefore, monthly minimum, average and maximum air temperatures of Tehran-Iran station at 64-years (1951-2014) were selected as predicted time-series. First, the most appropriate inputs were selected for the models using sensitivity analysis. After that, long-term air temperatures (1 month, 1-, 2-and 3-year ahead) were modeled. Results showed that: 1) the given algorithms had acceptable results in improving the models' performance in forecasting minimum, mean and maximum air temperatures. Also, they could improve the performance of ANN and ANFIS in most of the prediction intervals, 2) ANFIS-GA was selected as the most suitable model so that its average determination coefficient (R 2 ), root mean square errors (RMSE) and mean absolute errors (MAE) were 0.88, 1.41 and 2.52, respectively, 3) the sensitivity analysis provided suitable results in selecting the most appropriate model inputs for forecasting the minimum, mean and maximum air temperatures in different intervals.

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Section: One Month Forecasting Horizonsupporting

confidence: 81%

“…In another study, Daneshmand et.al. (2013) predicted monthly minimum temperature in Mashhad City, located in northeast of Iran, using ANNs and ANFIS models [12]. Results showed that the ANFIS had suitable performance.…”

Section: Introductionmentioning

confidence: 99%

Application of Heuristic Algorithms in Improving Performance of Soft Computing Models for Prediction of Min, Mean and Max Air Temperatures

Pirayesh¹,

Farzin²,

Malekani³

et al. 2019

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“…Pada beberapa tahun terakhir, model alternatif telah dikembangkan untuk menganalisis data runtun waktu nonlinear antara lain Neural Networks (NN) oleh Fausset (1994); Haykin (1999)), fuzzy system dan hibridanya (Jang et al, 1997). ANFIS yang menggabungkan antara NN dan fuzzy system telah diimplementasikan dalam berbagai bidang penelitian runtun waktu antara lain aplikasi ANFIS berdasarkan singular spectrum analysis untuk prediksi chaotic time series oleh Abdollahzade et al (2015); prediksi chaotic time series menggunakan ANFIS oleh Behmanesh et al (2014); prediksi runtun waktu fuzzy oleh Cheng et al (2016); pengembangan pendekatan baru untuk prediksi kenaikan harga minyak (Mombeini et al, 2014); computational intelligence untuk prediksi chaotic time series; pemodelan temperatur minimum oleh Daneshmand et al (2015); prediksi return saham oleh Wei et al (2011); prediksi volatilitas finansial oleh Luna and Ballini (2012); prediksi nilai tukar valuta asing oleh Fahimifard et al (2009); dan prediksi fluktuasi tinggi danau menggunakan model ensembles ANN dan ANFIS (Talebizadeh, 2011). Sebagian besar penelitian tersebut menyimpulkan bahwa ANFIS lebih baik dari metode yang lain.…”

Section: Pendahuluanunclassified

Pemodelan Hybrid Arima-Anfis Untuk Data Produksi Tanaman Hortikultura Di Jawa Tengah

Tarno

Rusgiyono

Warsito

et al. 2018

Medstat

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“…The combination model is called Adaptive Neuro-Fuzzy Inference System (ANFIS). There are many fields of time series research such as application of ANFIS based on singular spectrum analysis for forecasting chaotic time series [10]; chaotic time series prediction using improved ANFIS [11]; fuzzy time series forecasting [12]; developing a new approach for forecasting the trends of oil price [13]; forecasting of stock return [14]; forecasting of financial volatility [15]. The conclusion of the research related to ANFIS performance for forecasting non-linear data.…”

Section: Introductionmentioning

confidence: 99%

Construction of ANFIS Model Based on LM-Test for Forecasting of Chili Price Data in Semarang

Tarno¹,

Maruddani²,

Rahmawati³

2020

Proceedings of the Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bog

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The research aim is constructing Adaptive Neuro-Fuzzy Inference System (ANFIS) model for forecasting time series data. The ANFIS model is constructed and applied to chili price data in Semarang. The daily data are written during December 2018 to May 2019. The input selection in ANFIS is done by using the Lagrange Multiplier (LM) test. The lag-1 with 2 membership functions is selected as optimal input. The performance of prediction based on in-sample data is measured by the values of mean absolute percentage error (MAPE) and root mean squares error (RMSE). The values of MAPE and RMSE are 2.9% and 939.8 respectively.

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Modeling minimum temperature using adaptive neuro-fuzzy inference system based on spectral analysis of climate indices: A case study in Iran

Cited by 24 publications

References 15 publications

Application of Heuristic Algorithms in Improving Performance of Soft Computing Models for Prediction of Min, Mean and Max Air Temperatures

Application of Heuristic Algorithms in Improving Performance of Soft Computing Models for Prediction of Min, Mean and Max Air Temperatures

Pemodelan Hybrid Arima-Anfis Untuk Data Produksi Tanaman Hortikultura Di Jawa Tengah

Construction of ANFIS Model Based on LM-Test for Forecasting of Chili Price Data in Semarang

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