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In this paper, daily and monthly runoff discharge forecasts are improved by developing an ensemble model based on the Bayesian maximum entropy (BME), which integrates the outcomes of several single-source simulation models. The performance of the developed ensemble model in daily and monthly discharge forecasting is evaluated by comparing its results with those obtained from 12 other ensemble models, including simple averaging (SA), weighted averaging (WA), Bayesian model averaging (BMA), the best model in last time step (BMLS), artificial neural network (ANN), artificial neural network-based on differenced data, error analysis (EA), wavelet-artificial neural network (Wavelet-ANN), multi-model super ensemble (MMSE), modified multi-model super ensemble (MMMSE), Kriging and an ensemble model based on K-means clustering. Six models, namely artificial neural network (ANN), auto-regressive integrated moving average (ARIMA), adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), M5tree, and Wavelet-ANN, are used as single-source forecasting models. These models provide the inputs of the ensemble models. After analyzing different hydrologic variables, the best set of predictors is chosen for the single-source models. Then, the single-source models are calibrated and validated and the ones with the best performances are selected for being used in the ensemble phase. The performance of the models is evaluated by applying them to two different case studies of the Liqvan watershed, a sub-watershed of Lake Urmia catchment in the north-west of Iran, and the Latyan watershed, in the east of Tehran, Iran. The results show that the ensemble models, especially the BME and ANN-based ensemble models, significantly improve both monthly and daily river discharge forecasts. The values of the performance criteria of Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R2) in forecasting the extreme values of monthly runoff discharge, using the best single-source model, are respectively 0.787 and 0.869. The corresponding values for the best ensemble model are 0.933 and 0.962, respectively. For the daily runoff discharge forecast, the values of the mentioned performance criteria for the best single-source model are respectively 0.874 and 0.877, while corresponding values for the best ensemble model are 0.927 and 0.935, respectively. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases belong to the leading causes of disability and premature death worldwide, including in Iran. It is predicted that the burden of the disease in Iran in 2025 will be more than doubled compared to 2005. Therefore, many forecasting models have been used to predict disease progression, estimate mortality rates, and assess risk factors. Our study focused on two time series prediction on models: autoregressive integrated moving average with exogenous variable (ARIMAX) and Convolutional neural network–long short-term memory network (CNN-LSTM). ARIMAX (6,1,6) had the best MSE of 0.655 among time series regression models. The prediction of this model shows a significant association in lag 4 and lag 6. Nitrogen dioxide (NO2) was also significant in lag 6, while CNN-LSTM had a much better MSE of 0.21. For the time series analysis and forecasts studied in this paper, deep learning models provided more accurate results than classical methods such as ARIMAX. [ABSTRACT FROM AUTHOR]

• A novel approach for regional modeling and forecasting of PWV using LS-SVR is suggested. • The results of the new model are analyzed at control and radiosonde stations. • A comparative analysis between the results of new model, GPS and VBT is made. We propose a new model for spatio-temporal modeling and forecasting of precipitable water vapor (PWV) using least square support vector regression (LS-SVR) method. The LS-SVR uses simple linear equations. As a result, the complexity of the computational algorithm is reduced. In addition, the convergence speed and accuracy of the results increase. The evaluation of the new method has been done with the observations of the GPS networks at north-west and central Alborz in Iran. In the north-west GPS network, observations of 23 GPS stations in the period of 27 October to 10 November 2011 are used. However, in central Alborz network, the observations of 11 GPS stations in the period of 10 to 24 June 2016 have been used. The north-west GPS network is in the mountainous region and its observations are for the winter season. But, the second network is near the coastal area and summer season measurements are used. The latitude, longitude and height of GPS stations, DOY, time, relative humidity, temperature and pressure are considered as an input of the LS-SVR model. The output of the new model is the PWV (LS-SVR PWV). After the training step, the new model is used to estimate the spatio-temporal variation of PWV. The results of the LS-SVR model are compared and evaluated with the standard neural network (SNN), adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), Saastamoinen, global pressure and temperature 3 (GPT3), voxel-based tomography (VBT) models, as well as with radiosonde measurements. Error evaluation of models has been done in control stations as well as by precise point positioning (PPP) method. For LS-SVR model, the averaged RMSE in the control stations of the north-west GPS network is 1.69 mm, while for the central Alborz GPS network, 1.88 mm is calculated. Also, the averaged relative error of LS-SVR model calculated in the Tabriz and Tehran radiosonde stations are 4.66 % and 6.12 %, respectively. The results of this paper show that the LS-SVR model has a very high capability in forecasting the spatio-temporal variation of PWV at the GPS network territory. The new model can be used for accurate estimation of PWV, meteorological applications and flood forecasting. [ABSTRACT FROM AUTHOR]