Статті в журналах: "Seasonal Artificial Neural Network"

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Fallah-Gha, G. A., M. Mousavi-Ba, and M. Habibi-Nok. "Seasonal Rainfall Forecasting Using Artificial Neural Network." Journal of Applied Sciences 9, no. 6 (March 1, 2009): 1098–105. http://dx.doi.org/10.3923/jas.2009.1098.1105.

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Tran Anh, Duong, Thanh Duc Dang, and Song Pham Van. "Improved Rainfall Prediction Using Combined Pre-Processing Methods and Feed-Forward Neural Networks." J 2, no. 1 (February 14, 2019): 65–83. http://dx.doi.org/10.3390/j2010006.

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Rainfall prediction is a fundamental process in providing inputs for climate impact studies and hydrological process assessments. Rainfall events are, however, a complicated phenomenon and continues to be a challenge in forecasting. This paper introduces novel hybrid models for monthly rainfall prediction in which we combined two pre-processing methods (Seasonal Decomposition and Discrete Wavelet Transform) and two feed-forward neural networks (Artificial Neural Network and Seasonal Artificial Neural Network). In detail, observed monthly rainfall time series at the Ca Mau hydrological station in Vietnam were decomposed by using the two pre-processing data methods applied to five sub-signals at four levels by wavelet analysis, and three sub-sets by seasonal decomposition. After that, the processed data were used to feed the feed-forward Neural Network (ANN) and Seasonal Artificial Neural Network (SANN) rainfall prediction models. For model evaluations, the anticipated models were compared with the traditional Genetic Algorithm and Simulated Annealing algorithm (GA-SA) supported by Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA). Results showed both the wavelet transform and seasonal decomposition methods combined with the SANN model could satisfactorily simulate non-stationary and non-linear time series-related problems such as rainfall prediction, but wavelet transform along with SANN provided the most accurately predicted monthly rainfall.

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Mzyece, Lillian, Mayumbo Nyirenda, Monde K. Kabemba, and Grey Chibawe. "Forecasting Seasonal Rainfall in Zambia – An Artificial Neural Network Approach." Zambia ICT Journal 2, no. 1 (June 29, 2018): 16–24. http://dx.doi.org/10.33260/zictjournal.v2i1.46.

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Weather forecasting is an ever-challenging area of investigation for scientists. It is the application of science and technology in order to predict the state of the atmosphere for a given time and location. Rainfall is one of the weather parameters whose accurate forecasting has significant implications for agriculture and water resource management. In Zambia, agriculture plays a key role in terms of employment and food security. Rainfall forecasting is one of the most complicated and demanding operational responsibilities carried out by meteorological services all over the world. Long-term rainfall prediction is even more a challenging task. It is mainly done by experts who have gained sufficient experience in the use of appropriate forecasting techniques like modelling. It is mainly done by experts who have gained sufficient experience in the use of appropriate forecasting techniques like modelling. In this paper, a rainfall forecasting model using Artificial Neural Network is proposed as a model that that can be 'trained' to mimic the knowledge of rainfall forecasting experts. This makes it possible for researchers to adapt different techniques for different stages in the forecasting process. We begin by noting the five main stages in the seasonal rainfall forecasting process. We then apply artificial neural networks at each step. Initial results show that the artificial neural networks can successfully replace the currently used processes together with the expert knowledge. We further propose the use of these neural networks for teaching such forecasting processes, as they make documentation of the forecasting process easier and hence making the educational process of teaching to forecast seasonal rainfall easier as well. Artificial Neural Networks are reliable, handle more data at one time by virtual of being computer based, are less tedious and less dependent on user experience.

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Baldigara, Tea. "Modeliranje i prognoziranje broja zaposlenih u turizmu i hotelskoj industriji u Republici Hrvatskoj primjenom modela umjetnih neuronskih mreža." Oeconomica Jadertina 10, no. 2 (December 17, 2020): 3–20. http://dx.doi.org/10.15291/oec.3162.

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The paper investigates the performance and prognostic power of artificial neural network models in modelling and forecasting of time series of seasonal character. Models of artificial neural networks have been applied in modelling and forecasting the monthly total number of employees, the number of employed men and the number of employed women in the activity of providing accommodation services and preparing and serving food and beverages in the Republic of Croatia. The obtained modelling results have been compared with the results obtained by applying some of the traditionally used quantitative models in the analysis of seasonal time series, such as the Holt-Winters model of triple exponential smoothing and the seasonal multiplicative model of exponential trend. The evaluation of the performance and prognostic power of individual models was performed by comparing the average absolute and average absolute percentage error and the correlation coefficient between the actual and estimated values, and the predicted values were compared with the actual values. The evaluation of the obtained results showed that the selected model of acyclic multilayer perceptron is suitable for modelling and forecasting time series of seasonal character. The comparison of prognostic powers and actual and projected values of the number of employees suggests that the designed model of the artificial neural network is very reliable. This indicates that the models of artificial neural networks have great application potentials in the domain of modelling and forecasting of time series of a seasonal character.

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S, Benkachcha, Benhra J, and El Hassani. H. "Seasonal Time Series Forecasting Models based on Artificial Neural Network." International Journal of Computer Applications 116, no. 20 (April 22, 2015): 9–14. http://dx.doi.org/10.5120/20451-2805.

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Oscar CLAVERIA, Oscar CLAVERIA, Enric MONTE, and Salvador TORRA. "DATA PRE-PROCESSING FOR NEURAL NETWORK-BASED FORECASTING: DOES IT REALLY MATTER?" Technological and Economic Development of Economy 23, no. 5 (November 4, 2015): 709–25. http://dx.doi.org/10.3846/20294913.2015.1070772.

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This study aims to analyze the effects of data pre-processing on the forecasting performance of neural network models. We use three different Artificial Neural Networks techniques to predict tourist demand: multi-layer perceptron, radial basis function and the Elman neural networks. The structure of the networks is based on a multiple-input multiple-output (MIMO) approach. We use official statistical data of inbound international tourism demand to Catalonia (Spain) and compare the forecasting accuracy of four processing methods for the input vector of the networks: levels, growth rates, seasonally adjusted levels and seasonally adjusted growth rates. When comparing the forecasting accuracy of the different inputs for each visitor market and for different forecasting horizons, we obtain significantly better forecasts with levels than with growth rates. We also find that seasonally adjusted series significantly improve the forecasting performance of the networks, which hints at the significance of deseasonalizing the time series when using neural networks with forecasting purposes. These results reveal that, when using seasonal data, neural networks performance can be significantly improved by working directly with seasonally adjusted levels.

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Rezaei, Mohsen, Ahmad Ali Akbari Motlaq, Ali Rezvani Mahmouei, and Seyed Hojjatollah Mousavi. "River Flow Forecasting using artificial neural network (Shoor Ghaen)." Ciência e Natura 37 (December 21, 2015): 207. http://dx.doi.org/10.5902/2179460x20849.

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In our country, most of the rivers located in dry and warm climate areas are seasonal, and many of them have experienced floods. That, along with concerns about scarcity of water resources and the need to control surface water, makes identification, modeling, and simulation of rivers’ behavior, necessary for to long-term planning and proper and rational use of river flows potential. Rainfall phenomenon and the resulting runoff in watersheds, as well as predicting them are of nonlinear system types. Artificial neural networks are able to analyze and simulate phenomena in nonlinear and uncertain system where the relationship between the components and system parameters are not well known or describable. Shoor Ghayen River, with 100 km length is the biggest seasonal river of Qaenat city and the main source of water in Farrokhi storage dam. Therefore, in this study according to the rainfall and runoff statistic of Khonik Olya hydrometric and Ghayen synoptic stations between 1976-1977 and 2010-2011 water years, precipitation phenomena and river runoff was predicted. MATLAB software is used to perform calculations. For modeling artificial neural network, 85 percent of data were used for training the proposed method, the remaining 15% were used for validating the method using 10 neurons, and a network with an error of less than 5% was developed for each month. The maximum correlation in evaluation phase was for April with the value of 0.99, and the minimum was for June and August with a value of 0.92. Overall results indicate optimum performance of artificial neural networks in predicting runoff caused by rainfall. It is also found that better results can be achieved by standardizing the data.

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Jayadianti, Herlina, Tedy Agung Cahyadi, Nur Ali Amri, and Muhammad Fathurrahman Pitayandanu. "METODE KOMPARASI ARTIFICIAL NEURAL NETWORK PADA PREDIKSI CURAH HUJAN - LITERATURE REVIEW." Jurnal Tekno Insentif 14, no. 2 (August 27, 2020): 48–53. http://dx.doi.org/10.36787/jti.v14i2.150.

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Abstrak - Penelitian untuk mencari model prediksi curah hujan yang akurat di berbagai bidang sudah banyak dilakukan, maka dilakukan di-review kembali guna membantu proses penyaliran dalam perusahaan tambang. Review dilakukan dengan membandingkan hasil dari setiap model yang telah dilakukan pada beberapa penelitian sebelumnya. Penelitian ini menggunakan metode kuantitatif. Model yang dibandingkan pada penelitian di antaranya yaitu model Fuzzy, Fast Fourier Transformation (FFT), Emotional Artificial Neural Network (EANN), Artificial Neural Network (ANN), Adaptive Ensemble Empirical Mode Decomposition-Artificial Neural Network (AEEMD-ANN), E-SVR-Artificial Neural Network (E-SVR-ANN), Artificial Neural Network Backpropagation (BPNN), Adaptive Splines Threshold (ASTAR), Seasonal First-Order Autoregressive (SAR), Gumbel, Autoregressive Integrated Moving Average (ARIMA), Feed Forward Neural Network (FFNN), Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), dan Artificial Neural Network-Fuzzy (ANN-Fuzzy). Hasil dari review menyimpulkan bahwa model Artificial Neural Network memiliki beberapa kelebihan dibandingkan dengan metode yang lain, yakni ANN mampu memberikan hasil yang dapat mengenali pola-pola dengan baik dan mudah dikembangkan menjadi bermacam-macam variasi sesuai dengan permasalahan maupun parameter yang ada, sehingga ANN direkomendasikan untuk perhitungan prediksi hujan. Abstract - Various kinds of research have been carried out to find accurate models to predict rainfall in various fields, so the research that has been done previously was reviewed again to help the drainage process in mining companies. The review is done by comparing the results of each model that has been conducted in several previous studies. This research used quantitative methods. Models compared in this study include the Fuzzy model, Fast Fourier Transformation (FFT), Emotional Artificial Neural Network (EANN), Artificial Neural Network (ANN), Adaptive Ensemble Empirical Mode Decomposition-Artificial Neural Network (AEEMD-ANN), E-SVR -Artificial Neural Network (E-SVR-ANN), Artificial Neural Network Backpropagation (BPNN), Adaptive Splines Threshold (ASTAR), Seasonal First-Order Autoregressive (SAR), Gumbel, Autoregressive Integrated Moving Average (ARIMA), Feed Forward Neural Network (FFNN), Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Artificial Neural Network-Fuzzy (ANN-Fuzzy). The results of the review concluded that the Artificial Neural Network model has several advantages compared to other methods, namely ANN is able to provide results that can recognize patterns well and easily developed into a variety of variations in accordance with existing problems and parameters, so ANN is recommended for rain prediction calculation.

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Gregorić, Maja, and Tea Baldigara. "Artificial neural networks in modelling seasonal tourism demand - case study of Croatia." Zbornik Veleučilišta u Rijeci 8, no. 1 (2020): 19–39. http://dx.doi.org/10.31784/zvr.8.1.2.

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The purpose of this paper is to design an artificial neural network in the attempt to define the data generating process of the number of German tourist arrivals in Croatia considering the strong seasonal character of empirical data. The presence of seasonal unit roots in tourism demand determinants is analysed using the approach developed by Hylleberg, Engle, Granger and Yoo – Hegy test. The study is based on seasonality analysis and Artificial Neural Networks approach in building a model which intend to describe the behaviour of the German tourist flows to Croatia. Different neural network architectures were trained and tested, and after the modelling phase, the forecasting accuracy and model performances were analysed. Model performance and forecasting accuracy evaluation was tested using the mean absolute percentage error. Based on the augmented HEGY test procedure it can be concluded the German tourist arrivals to the Republic of Croatia have nonstationary behaviour associated with the zero frequency and seasonal frequency. Taking this into consideration, in the analysis of the phenomenon it is necessary to consider its seasonal character. Given the importance of the tourism for Croatian economic development, the research results could be useful, for both, researchers and practitioners, in the process of planning and routing the future Croatian hotel industry development and improvement of business performances.

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Sulandari, Winita, Subanar Subanar, Suhartono Suhartono, and Herni Utami. "Forecasting electricity load demand using hybrid exponential smoothing-artificial neural network model." International Journal of Advances in Intelligent Informatics 2, no. 3 (November 30, 2016): 131. http://dx.doi.org/10.26555/ijain.v2i3.69.

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Short-term electricity load demand forecast is a vital requirements for power systems. This research considers the combination of exponential smoothing for double seasonal patterns and neural network model. The linear version of Holt-Winter method is extended to accommodate a second seasonal component. In this work, the Fourier with time varying coefficient is presented as a means of seasonal extraction. The methodological contribution of this paper is to demonstrate how these methods can be adapted to model the time series data with multiple seasonal pattern, correlated non stationary error and nonlinearity components together. The proposed hybrid model is started by implementing exponential smoothing state space model to obtain the level, trend, seasonal and irregular components and then use them as inputs of neural network. Forecasts of future values are then can be obtained by using the hybrid model. The forecast performance was characterized by root mean square error and mean absolute percentage error. The proposed hybrid model is applied to two real load series that are energy consumption in Bawen substation and in Java-Bali area. Comparing with other existing models, results show that the proposed hybrid model generate the most accurate forecast

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Camara, Abdoulaye, Wang Feixing, and Liu Xiuqin. "Energy Consumption Forecasting Using Seasonal ARIMA with Artificial Neural Networks Models." International Journal of Business and Management 11, no. 5 (April 18, 2016): 231. http://dx.doi.org/10.5539/ijbm.v11n5p231.

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<p>In many areas such as financial, energy, economics, the historical data are non-stationary and contain trend and seasonal variations. The goal is to forecast the energy consumption in U.S. using two approaches, namely the statistical approach (SARIMA) and Neural Networks approach (ANN), and compare them in order to find the best model for forecasting. The energy area has an important role in the development of countries, thus, consumption planning of energy must be made accurately, despite they are governed by other factors such that population, gross domestic product (GDP), weather vagaries, storage capacity etc. This paper examines the forecasting performance for the residential energy consumption data of United States between SARIMA and ANN methodologies. The multi-layer perceptron (MLP) architecture is used in the artificial neural networks methodology. According to the obtained results, we conclude that the neural network model has slight superiority over SARIMA model and those models are not directional. </p>

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Pabreja, Kavita. "Artificial Neural Network for Markov Chaining of Rainfall Over India." International Journal of Business Analytics 7, no. 3 (July 2020): 71–84. http://dx.doi.org/10.4018/ijban.2020070105.

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Rainfall forecasting plays a significant role in water management for agriculture in a country like India where the economy depends heavily upon agriculture. In this paper, a feed forward artificial neural network (ANN) and a multiple linear regression model has been utilized for lagged time series data of monthly rainfall. The data for 23 years from 1990 to 2012 over Indian region has been used in this study. Convincing values of root mean squared error between actual monthly rainfall and that predicted by ANN has been found. It has been found that during monsoon months, rainfall of every n+3rd month can be predicted using last three months' (n, n+1, n+2) rainfall data with an excellent correlation coefficient that is more than 0.9 between actual and predicted rainfall. The probabilities of dry seasonal month, wet seasonal month for monsoon and non-monsoon months have been found.

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Hamzaçebi, Coşkun, Hüseyin Avni Es, and Recep Çakmak. "Forecasting of Turkey’s monthly electricity demand by seasonal artificial neural network." Neural Computing and Applications 31, no. 7 (August 25, 2017): 2217–31. http://dx.doi.org/10.1007/s00521-017-3183-5.

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Mekanik, Fatemeh, H. M. Rasel, Monzur Alam Imteaz, and Iqbal Hossain. "Artificial neural network modelling technique in predicting Western Australian seasonal rainfall." International Journal of Water 14, no. 1 (2020): 14. http://dx.doi.org/10.1504/ijw.2020.10035275.

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Hossain, Iqbal, H. M. Rasel, Fatemeh Mekanik, and Monzur Alam Imteaz. "Artificial neural network modelling technique in predicting Western Australian seasonal rainfall." International Journal of Water 14, no. 1 (2020): 14. http://dx.doi.org/10.1504/ijw.2020.112711.

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Franses, Philip Hans, and Gerrit Draisma. "Recognizing changing seasonal patterns using artificial neural networks." Journal of Econometrics 81, no. 1 (November 1997): 273–80. http://dx.doi.org/10.1016/s0304-4076(97)00047-x.

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Anochi, Juliana Aparecida, and Haroldo Fraga de Campos Velho. "PREVISÃO CLIMÁTICA DE PRECIPITAÇÃO PARA A REGIÃO SUL POR REDE NEURAL AUTOCONFIGURADA." Ciência e Natura 38 (July 20, 2016): 98. http://dx.doi.org/10.5902/2179460x19968.

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Climate prediction for precipitation field is a key issue, because such meteorological variable is the challenge for climate and weather forecasting due to the high spatial and temporal variability with strong impact on the society. A method based on the artificial neural network is applied to monthly and seasonal precipitation forecast in southern Brazil. The use of neural networks as a predictive model is widespread in different applications. The best configuration for the neural network is automatically calculated. The autoconfiguration scheme is described as an optimization problem.

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Hashemi, M. R., Z. Ghadampour, and S. P. Neill. "Using an artificial neural network to model seasonal changes in beach profiles." Ocean Engineering 37, no. 14-15 (October 2010): 1345–56. http://dx.doi.org/10.1016/j.oceaneng.2010.07.004.

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19

Musa, SY, and EV Mbaga. "Daily Nigerian peak load forecasting using artificial neural network with seasonal indices." Nigerian Journal of Technology 33, no. 1 (February 12, 2014): 114. http://dx.doi.org/10.4314/njt.v33i1.15.

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De Abreu, Jadson Coelho, Carlos Pedro Boechat Soares, and Helio Garcia Leite. "ASSESSING ALTERNATIVES TO ESTIMATE THE STEM VOLUME OF A SEASONAL SEMI-DECIDUOUS FOREST." FLORESTA 47, no. 4 (December 21, 2017): 375. http://dx.doi.org/10.5380/rf.v47i4.54259.

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AbstractThe objective of this study was to evaluate the use of linear and hybrid linear models, artificial neural networks (ANN) and support vector machine (SVM) in the estimation of the stem volume in a Seasonal Semi-deciduous Forest. Cubing data of 99 sample-trees of 15 species were used for this purpose. After analysis, we verified that the inclusion of the species as random effect did not contribute to increase the accuracy of the estimates in the structure of a hybrid model. Artificial neural networks and support vector machines, including species as input categorical variables, were the best alternatives to estimate the stem volume of trees of the Seasonal Semi-deciduous Forest.Keywords: Stem volume; artificial neural networks; support vector machines; hybrid linear models; uneven-aged forest. ResumoAvaliando alternativas para estimar o volume do fuste de uma Floresta Estacional Semidecidual. O objetivo desse estudo foi avaliar o uso de modelos lineares e lineares mistos, redes neurais artificiais (RNA) e máquina de vetor de suporte (MVS) na estimação dos volumes dos fustes de árvores em uma Floresta Estacional Semidecidual. Dados de cubagem de 99 árvores-amostra de 15 espécies foram utilizados para esta finalidade. Após análises, verificou-se que a inclusão das espécies como efeito aleatório não contribuiu para aumentar a exatidão das estimativas na estrutura de um modelo misto. As redes neurais artificiais e as máquinas de vetores de suporte, incluindo as espécies como variáveis categóricas de entrada, foram as melhores alternativas para estimar o volume dos fustes das árvores da Floresta Estacional Semidecidual.Palavras-chaves: Volume do fuste; redes neurais artificiais; máquinas de vetor de suporte; modelos lineares mistos; floresta inequiânea.

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Shamseldin, Asaad Y. "Artificial neural network model for river flow forecasting in a developing country." Journal of Hydroinformatics 12, no. 1 (September 1, 2009): 22–35. http://dx.doi.org/10.2166/hydro.2010.027.

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The present paper deals with exploring the use of Artificial Neural Networks (ANN) for forecasting the Blue Nile river flows in Sudan. Four ANN rainfall–runoff models based on the structure of the well-known multi-layer perceptron are developed. These models use the rainfall index as a common external input, with the rainfall index being a weighted sum of the recent and current rainfall. These models differ in terms of the additional external inputs being used by the model. The additional inputs are basically the seasonal expectations of both the rainfall index and the observed discharge. The results show that the model, which uses both the seasonal expectation of the observed discharge and the rainfall index as additional inputs, has the best performance. The estimated discharges of this model are further updated using a non-linear Auto-Regressive Exogenous-input model (NARXM)-ANN river flow forecasting output-updating procedure. In this way, a real-time river flow forecasting model is developed. The results show that the forecast updating has significantly enhanced the quality of the discharge forecasts. The results also indicate that the ANN has considerable potential to be used for river flow forecasting in developing countries.

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Chen, Yanyan, Shuwei Wang, Ning Chen, Xueqin Long, and Xiru Tang. "Forecasting Cohesionless Soil Highway Slope Displacement Using Modular Neural Network." Discrete Dynamics in Nature and Society 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/504574.

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The highway slope failures are triggered by the rainfall, namely, to create the disaster. However, forecasting the failure of highway slop is difficult because of nonlinear time dependency and seasonal effects, which affect the slope displacements. Starting from the artificial neural networks (ANNs) since the mid-1990s, an effective means is suggested to judge the stability of slope by forecasting the slope displacement in the future based on the monitoring data. In order to solve the problem of forecasting the highway slope displacement, a displacement time series forecasting model of cohesionless soil highway slope is given firstly, and then modular neural network (MNN) is used to train it. With the randomness of rainfall information, the membership function based on distance measurement is constructed; after that, a fuzzy discrimination method to sample data is adopted to realize online subnets selection to improve the self-adapting ability of artificial neural networks (ANNs). The experiment on the sample data of Beijing city’s highway slope demonstrates that this model is superior to others in accuracy and adaptability.

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Vochozka, Marek, Jakub Horák, and Petr Šuleř. "Equalizing Seasonal Time Series Using Artificial Neural Networks in Predicting the Euro–Yuan Exchange Rate." Journal of Risk and Financial Management 12, no. 2 (April 30, 2019): 76. http://dx.doi.org/10.3390/jrfm12020076.

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The exchange rate is one of the most monitored economic variables reflecting the state of the economy in the long run, while affecting it significantly in the short run. However, prediction of the exchange rate is very complicated. In this contribution, for the purposes of predicting the exchange rate, artificial neural networks are used, which have brought quality and valuable results in a number of research programs. This contribution aims to propose a methodology for considering seasonal fluctuations in equalizing time series by means of artificial neural networks on the example of Euro and Chinese Yuan. For the analysis, data on the exchange rate of these currencies per period longer than 9 years are used (3303 input data in total). Regression by means of neural networks is carried out. There are two network sets generated, of which the second one focuses on the seasonal fluctuations. Before the experiment, it had seemed that there was no reason to include categorical variables in the calculation. The result, however, indicated that additional variables in the form of year, month, day in the month, and day in the week, in which the value was measured, have brought higher accuracy and order in equalizing of the time series.

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Cheeneebash, Jayrani, Ashvin Harradon, and Ashvin Gopaul. "Forecasting Rainfall in Mauritius using Seasonal Autoregressive Integrated Moving Average and Artificial Neural Networks." Environmental Management and Sustainable Development 7, no. 1 (January 31, 2018): 115. http://dx.doi.org/10.5296/emsd.v7i1.12566.

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In this paper, two forecasting methods namely, the autoregressive integrated moving average (ARIMA) and the artificial neural network (ANN) are studied to forecast the amount of rainfall in Mauritius. Indeed due to the geographical location of Mauritius, the rainfall pattern is deeply affected by the season prevailing whereby the period of summer receives a relatively high amount of rainfall when compared to winter. As such, forecasting rainfall can help the local authorities to manage the distribution of water in the country especially during droughts. The results obtained from both methods are compared in terms of their mean square error, mean absolute difference and mean absolute percentage difference. It is then seen that artificial neural network is a much better model as it is more accurate. This is due to its nonlinearity characteristic and ability to learn and train itself.

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Ahn, J. C., S. W. Lee, G. S. Lee, and J. Y. Koo. "Predicting water pipe breaks using neural network." Water Supply 5, no. 3-4 (November 1, 2005): 159–72. http://dx.doi.org/10.2166/ws.2005.0096.

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The relationships between pipe breaks of service pipes and mains and several factors were examined. Historical pipe breaks, and water and soil temperatures were also modeled by an artificial neural network to predict pipe breaks for efficient management and maintenance of the pipe networks. It was observed that the breaks of pipes increased after the temperatures of water and soil crossed in spring and fall. The pipe breaks were closely related to water and soil temperature, especially mains were affected more than service pipes. The fittings and valves were susceptible to the temperatures and needed to take measures for preventing breaks. The prediction of the pipe breaks by the ANN model built had a good performance except that the sensitivity was not good when the pipe breaks rapidly increased or decreased. The ANN model gave a good performance and was to be useful to predict the patterns of pipe breaks on a seasonal basis.

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HAMZACEBI, C. "Improving artificial neural networks’ performance in seasonal time series forecasting." Information Sciences 178, no. 23 (December 1, 2008): 4550–59. http://dx.doi.org/10.1016/j.ins.2008.07.024.

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Lee, J. H., S. J. Moon, and B. S. Kang. "Drought frequency projection using regional climate scenarios reconstructed by seasonal artificial neural network model." Journal of Water and Climate Change 5, no. 4 (June 20, 2014): 578–92. http://dx.doi.org/10.2166/wcc.2014.130.

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The climate change impacts on drought in the Korean peninsula were projected using Global Climate Model (GCM) output reconstructed regionally by an artificial neural network (ANN) model. The reconstructed model outputs were subsequently used as an input to project drought severity evaluated by Standard Precipitation Index (SPI). The original GCM output corresponds to the CGCM3.1/T63 under the 20C3M reference scenario and the IPCC A1B, A2 and B1 projection scenarios. Because in general GCM shows limitation in capturing typhoon generation occurred at sub-grid scale, the training and validation of the ANN model utilized a precipitation data set with typhoon-generated rainfall eliminated for enhancing the ANN's computational performance. The non-stationarity characteristics of SPI was examined using the Mann–Kendall test. The projection was implemented for the near future period (2011–2040), mid-term (2041–2070) and long-term (2071–2100) future periods. The results indicated mitigated drought severity under all scenarios in terms of frequency, magnitude and drought spells even for the mildest B1 scenario. The SDF (severity-duration-frequency) curves illustrate the common patterns of alleviated drought severity for most future scenarios and elongated drought duration. The reconstructed GCM projection recovers the underestimated precipitation and provided more realistic drought projection even though there would be still uncertainties of spatial and temporal variability.

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Paramasivan, Senthil Kumar. "Deep Learning Based Recurrent Neural Networks to Enhance the Performance of Wind Energy Forecasting: A Review." Revue d'Intelligence Artificielle 35, no. 1 (February 28, 2021): 1–10. http://dx.doi.org/10.18280/ria.350101.

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In the modern era, deep learning is a powerful technique in the field of wind energy forecasting. The deep neural network effectively handles the seasonal variation and uncertainty characteristics of wind speed by proper structural design, objective function optimization, and feature learning. The present paper focuses on the critical analysis of wind energy forecasting using deep learning based Recurrent neural networks (RNN) models. It explores RNN and its variants, such as simple RNN, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional RNN models. The recurrent neural network processes the input time series data sequentially and captures well the temporal dependencies exist in the successive input data. This review investigates the RNN models of wind energy forecasting, the data sources utilized, and the performance achieved in terms of the error measures. The overall review shows that the deep learning based RNN improves the performance of wind energy forecasting compared to the conventional techniques.

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Vrbka, J., J. Horák, and V. Machová. "Using Artificial Neural Networks for Equalizing Time Series Considering Seasonal Fluctuations." SHS Web of Conferences 71 (2019): 01003. http://dx.doi.org/10.1051/shsconf/20197101003.

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The objective of this contribution is to prepare a methodology of using artificial neural networks for equalizing time series when considering seasonal fluctuations on the example of the Czech Republic import from the People´s Republic of China. If we focus on the relation of neural networks and time series, it is possible to state that both the purpose of time series themselves and the nature of all the data are what matters. The purpose of neural networks is to record the process of time series and to forecast individual data points in the best possible way. From the discussion part it follows that adding other variables significantly improves the quality of the equalized time series. Not only the performance of the networks is very high, but the individual MLP networks are also able to capture the seasonal fluctuations in the development of the monitored variable, which is the CR import from the PRC.

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Spławińska, M. "Factors Determining Seasonal Variations in Traffic Volumes." Archives of Civil Engineering 63, no. 4 (December 1, 2017): 35–50. http://dx.doi.org/10.1515/ace-2017-0039.

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Abstract The characteristics of seasonal variations in traffic volumes are used for a variety of purposes, for example to determine the basic parameters describing annual average daily traffic – AADT, and design hourly volume – DHV, analyses of road network reliability, and traffic management. Via these analyses proper classification of road sections into appropriate seasonal factor groups (SFGs) has a decisive influence on results. This article, on the basis of computational experiments (models of artificial neural networks, discriminatory analysis), aims to identify which factors have the greatest impact on the allocation of a section of road to the corresponding SFG, based on short-term measurements. These factors are presented as qualitative data: the Polish region, spatial relationships, functions of road, cross-sections, technical class; and quantitative data: rush hour traffic volume.

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Tsakiri, Katerina, Antonios Marsellos, and Stelios Kapetanakis. "Artificial Neural Network and Multiple Linear Regression for Flood Prediction in Mohawk River, New York." Water 10, no. 9 (August 29, 2018): 1158. http://dx.doi.org/10.3390/w10091158.

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This research introduces a hybrid model for forecasting river flood events with an example of the Mohawk River in New York. Time series analysis and artificial neural networks are combined for the explanation and forecasting of the daily water discharge using hydrogeological and climatic variables. A low pass filter (Kolmogorov–Zurbenko filter) is applied for the decomposition of the time series into different components (long, seasonal, and short-term components). For the prediction of the water discharge time series, each component has been described by applying the multiple linear regression models (MLR), and the artificial neural network (ANN) model. The MLR retains the advantage of the physical interpretation of the water discharge time series. We prove that time series decomposition is essential before the application of any model. Also, decomposition shows that the Mohawk River is affected by multiple time scale components that contribute to the hydrologic cycle of the included watersheds. Comparison of the models proves that the application of the ANN on the decomposed time series improves the accuracy of forecasting flood events. The hybrid model which consists of time series decomposition and artificial neural network leads to a forecasting up to 96% of the explanation for the water discharge time series.

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Chen, Qing Ming, Xiao Zhong Xu, and Nan Zeng. "Combination Regression and Neural Network for Short Term Load Forecasting." Advanced Materials Research 690-693 (May 2013): 2787–95. http://dx.doi.org/10.4028/www.scientific.net/amr.690-693.2787.

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The forecasting of gas demand has become one of the major research fields in gas engineering. Gas demand possesses dual property of increasement and seasonal fluctuation simultaneously, so it makes gas demand variation possess complex nonlinear combined character. Accurately forecast were essential part of an efficient gas system planning and operation. In this paper, a new forecasting model which named regression combined neural network is put forward. In this approach we used regression to model the trend and used neural network for calculating predicted values and errors. Taking the advantages of regression analysis and artificial neural network, the model improves the forecasting accuracy of power demand obviously. The results indicate that the model is effective and feasible for load forecasting.

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Vochozka, Marek, and Zuzana Rowland. "Forecasting trade balance of Czech Republic and People´s Republic of China in equalizing time series and considering seasonal fluctuations." SHS Web of Conferences 73 (2020): 01032. http://dx.doi.org/10.1051/shsconf/20207301032.

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The objective of the contribution is to introduce a methodology for considering seasonal fluctuations in equalizing time series using artificial neural networks on the example of the Czech Republic and the People´s Republic of China trade balance. The data available is the data on monthly balance for the period between January 2000 and July 2018, that is, 223 input data. The unit is Euro. The data for the analysis are available on the World Bank web pages etc. Regression analysis is carried out using artificial neural networks. There are two types on neural networks generated, multilayer perceptron networks (MLP) and radial basis function networks (RBF). In order to achieve the optimal result, two sets of neural structures are generated. There are generated a total of 10,000 neural structures, out of which only 5 with the best characteristics are retained. Finally, the results of both groups of retained neural networks are compared. The contribution this paper brings is the involvement of variables that are able to forecast a possible seasonal fluctuation in the time series development when using artificial neural networks. Moreover, neural networks have been identified that achieve slightly better results than other networks, specifically these are the neural networks 1. MLP 13-6-1 and 3. MLP 13-8-1.

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Liu, Shuai, Hong Ji, and Morgan C. Wang. "Nonpooling Convolutional Neural Network Forecasting for Seasonal Time Series With Trends." IEEE Transactions on Neural Networks and Learning Systems 31, no. 8 (August 2020): 2879–88. http://dx.doi.org/10.1109/tnnls.2019.2934110.

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Wang, Wei-Lei, Guisheng Song, François Primeau, Eric S. Saltzman, Thomas G. Bell, and J. Keith Moore. "Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network." Biogeosciences 17, no. 21 (November 6, 2020): 5335–54. http://dx.doi.org/10.5194/bg-17-5335-2020.

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Abstract. Marine dimethyl sulfide (DMS) is important to climate due to the ability of DMS to alter Earth's radiation budget. Knowledge of the global-scale distribution, seasonal variability, and sea-to-air flux of DMS is needed in order to improve understanding of atmospheric sulfur, aerosol/cloud dynamics, and albedo. Here we examine the use of an artificial neural network (ANN) to extrapolate available DMS measurements to the global ocean and produce a global climatology with monthly temporal resolution. A global database of 82 996 ship-based DMS measurements in surface waters was used along with a suite of environmental parameters consisting of latitude–longitude coordinates, time of day, time of year, solar radiation, mixed layer depth, sea surface temperature, salinity, nitrate, phosphate, and silicate. Linear regressions of DMS against the environmental parameters show that on a global-scale mixed layer depth and solar radiation are the strongest predictors of DMS. These parameters capture ∼9 % and ∼7 % of the raw DMS data variance, respectively. Multilinear regression can capture more of the raw data variance (∼39 %) but strongly underestimates DMS in high-concentration regions. In contrast, the artificial neural network captures ∼66 % of the raw data variance in our database. Like prior climatologies our results show a strong seasonal cycle in surface ocean DMS with the highest concentrations and sea-to-air fluxes in the high-latitude summertime oceans. We estimate a lower global sea-to-air DMS flux (20.12±0.43 Tg S yr−1) than the prior estimate based on a map interpolation method when the same gas transfer velocity parameterization is used. Our sensitivity test results show that DMS concentration does not change unidirectionally with each of the environmental parameters, which emphasizes the interactions among these parameters. The ANN model suggests that the flux of DMS from the ocean to the atmosphere will increase with global warming. Given that larger DMS fluxes induce greater cloud albedo, this corresponds to a negative climate feedback.

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S.M., Karthik, and Arumugam P. "STOCHASTIC MODELLING BASED MONTHLY RAINFALL PREDICTION USING SEASONAL ARTIFICIAL NEURAL NETWORKS." ICTACT Journal on Soft Computing 07, no. 02 (January 1, 2017): 1421–26. http://dx.doi.org/10.21917/ijsc.2017.0196.

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Pinto, R., and S. Cavalieri. "SEASONAL TIME SERIES PREDICTION WITH ARTIFICIAL NEURAL NETWORKS AND LOCAL MEASURES." IFAC Proceedings Volumes 38, no. 1 (2005): 337–42. http://dx.doi.org/10.3182/20050703-6-cz-1902.01478.

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Fahimi Nezhad, Elham, Gholamabbas Fallah Ghalhari, and Fateme Bayatani. "Forecasting Maximum Seasonal Temperature Using Artificial Neural Networks “Tehran Case Study”." Asia-Pacific Journal of Atmospheric Sciences 55, no. 2 (January 21, 2019): 145–53. http://dx.doi.org/10.1007/s13143-018-0051-x.

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Asimakopoulou, Fani E., George J. Tsekouras, Ioannis F. Gonos, and Ioannis A. Stathopulos. "Estimation of seasonal variation of ground resistance using Artificial Neural Networks." Electric Power Systems Research 94 (January 2013): 113–21. http://dx.doi.org/10.1016/j.epsr.2012.07.018.

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Selbesoglu, Mahmut Oguz. "Spatial Interpolation of GNSS Troposphere Wet Delay by a Newly Designed Artificial Neural Network Model." Applied Sciences 9, no. 21 (November 4, 2019): 4688. http://dx.doi.org/10.3390/app9214688.

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Global Navigation Satellite System (GNSS) signals arrive at the Earth in a nonlinear and slightly curved way due to the refraction effect caused by the troposphere. The troposphere delay of the GNSS signal consists of hydrostatic and wet parts. In particular, tropospheric wet delay prediction and interpolation are more difficult than those of the dry component due to the rapid temporal and spatial variation of the water vapor content. Wet delay estimation and interpolation with a sufficient accuracy is an important issue for all parameters obtained by GNSS positioning techniques. In particular, in real-time positioning applications, errors caused by interpolation of the wet troposphere delay are reflected in the height component of about 1 to 2 cm. Furthermore, the amount of water vapor in the troposphere is very important information in weather forecast applications obtained as a function of wet delay. Therefore, real-time monitoring of the troposphere can be achieved with a higher resolution and accuracy by utilizing neural network models for interpolation of the wet tropospheric delay. In addition, in the absence of the GNSS station, wet delays can be interpolated by means of the surrounding stations to the desired location. In this study, a back propagation artificial neural network (BPNN) model based on meteorological parameters obtained from The New Austrian Meteorological Measuring Network (TAWES) was used to interpolate wet troposphere delay. Analysis was carried out at 40 reference stations of the Echtzeit Positionierung Austria (EPOSA) GNSS Network covering the whole of Austria. The interpolation of zenith wet delays based on the artificial neural network was performed by using latitude, longitude, altitude and meteorological parameters (temperature, pressure, weighted mean temperature, and water vapor pressure). These parameters were then subtracted from the artificial neural network model one by one and six different artificial neural networks were designed. In addition, the linear interpolation method (LIN) and inverse distance weighted interpolation method (IDW) were used as conventional interpolation methods. In order to investigate the effect of the network density on interpolation methods, three networks, including 40, 30, and 20 reference stations, were formed and the increased distance effect on interpolation methods was evaluated. In addition, analyses were conducted in winter, spring, and summer to evaluate the seasonal effects on interpolation methods. According to the statistical analysis, the root mean square error (RMSE) values of the IDW, LIN, and BPNN methods were found to be 12.6, 13.4, and 5.9 mm, respectively.

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Dibike, Yonas B., and Paulin Coulibaly. "TDNN with logical values for hydrologic modeling in a cold and snowy climate." Journal of Hydroinformatics 10, no. 4 (October 1, 2008): 289–300. http://dx.doi.org/10.2166/hydro.2008.049.

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Watershed runoff in areas with heavy seasonal snow cover is usually estimated using physically based conceptual hydrologic models. Such simulation models normally require a snowmelt algorithm consisting of a surface energy balance and some accounting of internal snowpack processes to be part of the modeling system. On the other hand, artificial neural networks are flexible mathematical structures that are capable of identifying such complex nonlinear relationships between input and output datasets from historical precipitation, temperature and streamflow records. This paper presents the findings of a study on using a form of time-delayed neural network, namely time-lagged feedforward neural network (TLFN), that implicitly accounts for snow accumulation and snowmelt processes through the use of logical values and tapped delay lines. The logical values (in the form of symbolic inputs) are used to implicitly include seasonal information in the TLFN model. The proposed method has been successfully applied for improved precipitation–runoff modeling of both the Chute-du-Diable reservoir inflows and the Serpent River flows in northeastern Canada where river flows and reservoir inflows are highly influenced by seasonal snowmelt effects. The study demonstrates that the TLFN with logical values is capable of modeling the precipitation–runoff process in a cold and snowy climate by relying on ‘logical input values’ and tapped delay lines to implicitly recognize the temporal input–output patterns in the historical data. The study results also show that, once the appropriate input patterns are identified, the time-lagged neural network based models performed quite well, especially for spring peak flows, and demonstrated comparable performance in simulating the precipitation–runoff processes to that of a physically based hydrological model, namely HBV.

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Jahan, Khurshid, and Soni M. Pradhanang. "Predicting Runoff Chloride Concentrations in Suburban Watersheds Using an Artificial Neural Network (ANN)." Hydrology 7, no. 4 (October 21, 2020): 80. http://dx.doi.org/10.3390/hydrology7040080.

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Road salts in stormwater runoff, from both urban and suburban areas, are of concern to many. Chloride-based deicers [i.e., sodium chloride (NaCl), magnesium chloride (MgCl2), and calcium chloride (CaCl2)], dissolve in runoff, travel downstream in the aqueous phase, percolate into soils, and leach into groundwater. In this study, data obtained from stormwater runoff events were used to predict chloride concentrations and seasonal impacts at different sites within a suburban watershed. Water quality data for 42 rainfall events (2016–2019) greater than 12.7 mm (0.5 inches) were used. An artificial neural network (ANN) model was developed, using measured rainfall volume, turbidity, total suspended solids (TSS), dissolved organic carbon (DOC), sodium, chloride, and total nitrate concentrations. Water quality data were trained using the Levenberg-Marquardt back-propagation algorithm. The model was then applied to six different sites. The new ANN model proved accurate in predicting values. This study illustrates that road salt and deicers are the prime cause of high chloride concentrations in runoff during winter and spring, threatening the aquatic environment.

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Griffiths, K. A., and R. C. Andrews. "The application of artificial neural networks for the optimization of coagulant dosage." Water Supply 11, no. 5 (December 1, 2011): 605–11. http://dx.doi.org/10.2166/ws.2011.028.

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Filtration is the final physical barrier preventing the passage of microbial pathogens into public drinking water. Proper pre-treatment via coagulation is essential for maintaining good particle removal during filtration. To improve filter performance at the Elgin Area WTP, artificial neural network (ANN) models were applied to optimize pre-filtration processes in terms of settled water turbidity and alum dosage. ANNs were successfully developed to predict future settled water turbidity based on seasonal raw water variables and chemical dosages, with correlation (R2) values ranging from 0.63 to 0.79. Additionally, inverse-process ANNs were developed to predict the optimal alum dosage required to achieve desired settled water turbidity, with correlation (R2) values ranging from 0.78 to 0.89.

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Seyedi, Seyed Navid, Pouyan Rezvan, Saeed Akbarnatajbisheh, and Syed Ahmad Helmi. "Evaluating ARIMA-Neural Network Hybrid Model Performance in Forecasting Stationary Timeseries." Advanced Materials Research 845 (December 2013): 510–15. http://dx.doi.org/10.4028/www.scientific.net/amr.845.510.

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Demand prediction is one of most sophisticated steps in planning and investments. Although many studies are conducted to find the appropriate forecasting models, dynamic nature of forecasted parameters and their effecting factors are apparent evidences for continuous researches. ARIMA, Artificial Neural Network (ANN), and ARIMA-ANN hybrid model are well-known forecasting models. Many researchers concluded that the Hybrid model is the predominant forecasting model in comparison with ARIMA and ANN individual models. Most of these researches are based on non-stationary or seasonal timeseries, whereas in this article, hybrid models forecast ability by stationary time series is studied. Some following demand time steps from a paint manufacturing company are forecasted by all previously mentioned models and ARIMA-ANN hybrid model fails to present the best forecasts.

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Breen, Katherine H., Scott C. James, Joseph D. White, Peter M. Allen, and Jeffery G. Arnold. "A Hybrid Artificial Neural Network to Estimate Soil Moisture Using SWAT+ and SMAP Data." Machine Learning and Knowledge Extraction 2, no. 3 (August 21, 2020): 283–306. http://dx.doi.org/10.3390/make2030016.

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In this work, we developed a data-driven framework to predict near-surface (0–5 cm) soil moisture (SM) by mapping inputs from the Soil & Water Assessment Tool to SM time series from NASA’s Soil Moisture Active Passive (SMAP) satellite for the period 1 January 2016–31 December 2018. We developed a hybrid artificial neural network (ANN) combining long short-term memory and multilayer perceptron networks that were used to simultaneously incorporate dynamic weather and static spatial data into the training algorithm, respectively. We evaluated the generalizability of the hybrid ANN using training datasets comprising several watersheds with different environmental conditions, examined the effects of standard and physics-guided loss functions, and experimented with feature augmentation. Our model could estimate SM on par with the accuracy of SMAP. We demonstrated that the most critical learning of the physical processes governing SM variability was learned from meteorological time series, and that additional physical context supported model performance when test data were not fully encapsulated by the variability of the training data. Additionally, we found that when forecasting SM based on trends learned during the earlier training period, the models appreciated seasonal trends.

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Yuan, Jihui, Craig Farnham, Chikako Azuma, and Kazuo Emura. "Predictive artificial neural network models to forecast the seasonal hourly electricity consumption for a University Campus." Sustainable Cities and Society 42 (October 2018): 82–92. http://dx.doi.org/10.1016/j.scs.2018.06.019.

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Bowden, Gavin J., Graeme C. Dandy, and Holger R. Maier. "Data transformation for neural network models in water resources applications." Journal of Hydroinformatics 5, no. 4 (October 1, 2003): 245–58. http://dx.doi.org/10.2166/hydro.2003.0021.

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A step that should be considered when developing artificial neural network (ANN) models for water resources applications is the selection of an appropriate transformation of the data. In general, the primary motivations for data transformation are: (1) to scale the data so as to be commensurate with the transfer function in the output layer; (2) to standardise each of the variables; (3) to provide a suitable initialization of the ANN; and (4) to modify the distribution of the input variables to provide a better mapping to the outputs. In this paper, five different transformations are investigated in an attempt to improve the ANN's forecasting ability. These are: linear transformation, logarithmic transformation, histogram equalization, seasonal transformation and a transformation to normality. A case study is presented in which each of the ANN models developed using the different transformation techniques is used to forecast salinity in the River Murray at Murray Bridge (South Australia) 14 days in advance. When tested on a validation set from July 1992 to March 1998, the model developed using the linear transformation resulted in the lowest root mean squared forecasting error. This finding further strengthens the claim that the probability distribution of the data does not need to be known to develop effective ANN models. No improvement in the ANN model's forecasting ability was made using the logarithmic, seasonal and normality transformations. The model developed using histogram equalization produced good results for data within the training domain but was not robust on new patterns outside of the calibration range.

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Amaratunga, Vinushi, Lasini Wickramasinghe, Anushka Perera, Jeevani Jayasinghe, and Upaka Rathnayake. "Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data." Mathematical Problems in Engineering 2020 (July 18, 2020): 1–11. http://dx.doi.org/10.1155/2020/8627824.

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Paddy harvest is extremely vulnerable to climate change and climate variations. It is a well-known fact that climate change has been accelerated over the past decades due to various human induced activities. In addition, demand for the food is increasing day-by-day due to the rapid growth of population. Therefore, understanding the relationships between climatic factors and paddy production has become crucial for the sustainability of the agriculture sector. However, these relationships are usually complex nonlinear relationships. Artificial Neural Networks (ANNs) are extensively used in obtaining these complex, nonlinear relationships. However, these relationships are not yet obtained in the context of Sri Lanka; a country where its staple food is rice. Therefore, this research presents an attempt in obtaining the relationships between the paddy yield and climatic parameters for several paddy grown areas (Ampara, Batticaloa, Badulla, Bandarawela, Hambantota, Trincomalee, Kurunegala, and Puttalam) with available data. Three training algorithms (Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugated Gradient (SCG)) are used to train the developed neural network model, and they are compared against each other to find the better training algorithm. Correlation coefficient (R) and Mean Squared Error (MSE) were used as the performance indicators to evaluate the performance of the developed ANN models. The results obtained from this study reveal that LM training algorithm has outperformed the other two algorithms in determining the relationships between climatic factors and paddy yield with less computational time. In addition, in the absence of seasonal climate data, annual prediction process is understood as an efficient prediction process. However, the results reveal that there is an error threshold in the prediction. Nevertheless, the obtained results are stable and acceptable under the highly unpredicted climate scenarios. The ANN relationships developed can be used to predict the future paddy yields in corresponding areas with the future climate data from various climate models.

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Álvarez-Díaz, Marcos, Manuel González-Gómez, and María Otero-Giráldez. "Forecasting International Tourism Demand Using a Non-Linear Autoregressive Neural Network and Genetic Programming." Forecasting 1, no. 1 (September 13, 2018): 90–106. http://dx.doi.org/10.3390/forecast1010007.

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This study explores the forecasting ability of two powerful non-linear computational methods: artificial neural networks and genetic programming. We use as a case of study the monthly international tourism demand in Spain, approximated by the number of tourist arrivals and of overnight stays. The forecasting results reveal that non-linear methods achieve slightly better predictions than those obtained by a traditional forecasting technique, the seasonal autoregressive integrated moving average (SARIMA) approach. This slight forecasting improvement was close to being statistically significant. Forecasters must judge whether the high cost of implementing these computational methods is worthwhile.

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Mithiya, Debasis, Kumarjit Mandal, and Simanti Bandyopadhyay. "Time Series Analysis and Forecasting of Rainfall for Agricultural Crops in India: An Application of Artificial Neural Network." Research in Applied Economics 12, no. 4 (November 6, 2020): 1. http://dx.doi.org/10.5296/rae.v12i4.15967.

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Indian agriculture depends heavily on rainfall. It not only influences agricultural production but also affects the prices of all agricultural commodities. Rainfall is an exogenous variable which is beyond farmers’ control. The outcome of rainfall fluctuation is quite natural. It has been observed that fluctuation in rainfall brings about fluctuation in output leading to price changes. Considering the importance of rainfall in determining agricultural production and prices, the study has attempted to forecast monthly rainfall in India with the help of time series analysis using monthly rainfall data. Both linear and non-linear models have been used. The value of diagnostic checking parameters (MAE, MSE, RMSE) is lower in a non-linear model compared to a linear one. The non-linear model - Artificial Neural Network (ANN) has been chosen instead of linear models, namely, simple seasonal exponential smoothing and Seasonal Auto-Regressive Integrated Moving Average to forecast rainfall. This will help to identify the proper cropping pattern.

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