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The COVID-19 pandemic has profoundly impacted every aspect of our lives, from economic to the social facets of contemporary society. While the new COVID-19 waves may not be anticipated to be as severe as previous ones, it would be unreasonable to assume that they will cease any time soon. Consequently, forecasting the number of future infections, recovered patients, and death cases remains a very much logical step in trying to fight against further waves, in conjunction with ongoing vaccination efforts. In this paper, we investigate the efficiency of three intelligent machine learning algorithms, namely GMDH, Bi-LSTM, and GA + NN, for COVID-19 forecasting, with an application to Iran and the United Kingdom. The experimental results show that the algorithms can be used to forecast the next six months of COVID-19 in terms of confirmed, recovered, and death cases, which gives a more ample timeframe for using the results to make better practical yet strategic decisions and take appropriate actions or measures to deploy resources effectively to contain or curb the spread of the coronavirus. Despite the distinct dynamics observed in the data, our analysis proves the robustness of the employed models. [ABSTRACT FROM AUTHOR]

In this paper, the monthly long-term precipitation of the city of Ardabil from 1976 to 2020 is simulated by a modern hybrid learning machine. To this end, the Wavelet and Outlier Robust Extreme Learning Machine (ORELM) models are integrated to produce a hybrid model called "Wavelet-Outlier Robust Extreme Learning Machine (WORELM)". First, the observed data are normalized, and the best normalization coefficients for the study are acquired. About 70% of the observed data are employed to train the artificial intelligence models and the remaining (i.e., 30%) to test them. After that, the optimal mother wavelet and the best decomposition level of the wavelet model are computed. Then, the optimal number of the hidden layer neurons and the best activation function of the ORELM model is chosen by performing a trial and error procedure. In this study, the regularization parameter of the ORELM model is also optimized. Moreover, using the autocorrelation function (ACF), the most influencing lags of the time-series data are detected, and 14 WORELM models are developed. By analyzing the WORELM models, the best model and the effective lags of the time-series data are introduced. The outcomes of the hybrid WORELM model are finally compared with other machine learning (ML) algorithms to prove the superiority of the WORELM model. [ABSTRACT FROM AUTHOR]

Point predictions of hydroclimatic processes through nonlinear modeling tools are associated with uncertainty. The main goal of this research was to construct prediction intervals (PIs) for nonlinear artificial neural network (ANN)-based models of evaporation and the standardized precipitation index (SPI). These are two critical indicators for climate for four stations in Iran (i.e., Tabriz, Urmia, Ardabil, and Ahvaz) to qualify their predicted uncertainty values (UVs). We used classical techniques of bootstrap (BS), mean variance estimation (MVE), and Delta, as well as an optimization-based method of lower upper bound estimation (LUBE), to construct and compare the PIs. The wavelet-based denoising method was also adopted to denoise input data, enhancing the modeling performance. The obtained results indicate the ability of the BS and LUBE methods to estimate the uncertainty bound. The Delta method mostly failed to find the desired coverage due to its narrow PIs. On the other hand, the MVE method, due to its wide bound, did not convey valuable information about uncertainty. According to the obtained results, denoising the input vector could enhance the PI quality in the modeling of the SPI by up to 76%. It was more prominent than reducing the UV for evaporation models, which was observed the most at the Ardabil station, up to 30%. The inherently more random nature of drought than the evaporation process was interpreted as the cause of this reaction. From the results, Urmia station seems the riskiest regarding drought ventures. Significance Statement: The point predictions of evaporation and precipitation (in the form of SPI) are subject to uncertainty. The best way is to provide an area with the highest contingency as a prediction interval. The reduction in the width of such an interval leads to increased confidence in explaining and predicting these processes. We investigated different methods and found that by utilizing the optimization-based method for denoised inputs, uncertainty values of the output were conveyed better. Additionally, we concluded that the more random the process, the greater its uncertainty. A primary sense of the drought risk was made from the uncertainty perspective. [ABSTRACT FROM AUTHOR]

The structure of the electricity market in Iran is based on a pay-as-bid auction mechanism. In such a market, hydropower generators need to have accurate estimates of energy price in peak hours of the day-ahead market to optimally operate the reservoir and maximize the revenue. This paper aims at providing a robust model with the best predictors for forecasting the maximum daily electricity price (MDEP) in Iran's electricity market. To reach the goal, hourly electricity prices in 2020 and 2021 were used and several artificial intelligence models were employed to predict the MDEP and ADEP (average daily electricity price). A sensitivity analysis of the inputs showed that in most of the models for forecasting the day-ahead MDEP (P t max), the best predictors were P t − 1 max , P t − 7 max , and P t − 30 m a x . Also, the convolutional neural-long short-term memory network (CNN-LSTM) had the best performance for forecasting both MDEP and ADEP in Iran's energy market. Compared to the multivariate linear regression model, the CNN-LSTM dealt well with sinusoidal characteristics and fluctuation of electricity prices. Therefore, it could improve the accuracy and correlation of the MDEP forecasts by 31% and 3.5%, respectively. • The maximum daily electricity price (MDEP) is very important for hydropower plant's operation. • Nonlinearity, instability, and hourly fluctuations of electricity price make it difficult to forecast. • Several artificial intelligent models, SVM, ANN, ANFIS, ANN-GA, and CNN-LSTM, were used for forecasting MDEP. • The best predictors (P t − 1 max , P t − 7 max , and P t − 30 max) were determined in a sensitivity analysis. • The CNN-LSTM model improved the accuracy of forecasts for MDEP by 31% in comparison with the conventional MLR model. [ABSTRACT FROM AUTHOR]

A two-level modeling strategy is formulated to predict groundwater levels (GWL) within a portion of Lake Urmia's aquifer in NW Iran during 14 years (2001–2015), which both aquifer and lake suffer significant water decline. At Level 1, three artificial intelligence (AI) models were trained and tested, which comprise artificial neural network (ANN), Sugeno fuzzy logic (SFL), and neuro-fuzzy (NF). At Level 2, a novel formulation was employed, referred to as the Shannon entropy model averaging (EMA). This formulation combines the results at Level 1 by calculating the weights of Level 1 models based on an innovative approach, which incorporates performance, stability, and parsimony criteria. The results indicate that the models at Level 1 are fit-for-purpose and can capture the water table decline in GWL, but EMA improves RMSE by 5% in the testing phase. Although EMA does not significantly increase the performance of the models, the results of the homoscedastic test in models' residuals indicate that EMA increases the reliability of prediction owing to the homoscedastic residuals with the highest p value compared to Level 1 models. The p values as per Breusch–Pagan and White tests are 0.88 and 1, respectively, which indicates further information does not remain in the EMA residual. The EMA formulation can be applied to other water resource management problems. [ABSTRACT FROM AUTHOR]