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Drought forecasting is a major component of a drought preparedness and mitigation plan. This paper focuses on an investigation of artificial neural networks (ANN) models for drought forecasting in the algerois basin in Algeria in comparison with traditional stochastic models (ARIMA and SARIMA models). A wavelet pre-processing of input data (wavelet neural networks WANN) was used to improve the accuracy of ANN models for drought forecasting. The standard precipitation index (SPI), at three time scales (SPI-3, SPI-6 and SPI-12), was used as drought quantifying parameter for its multiple advantages. A number of different ANN and WANN models for all SPI have been tested. Moreover, the performance of WANN models was investigated using several mother wavelets including Haar wavelet (db1) and 16 daubechies wavelets (dbn, n varying between 2 and 17). The forecast results of all models were compared using three performance measures (NSE, RMSE and MAE). A comparison has been done between observed data and predictions, the results of this study indicate that the coupled wavelet neural network (WANN) models were the best models for drought forecasting for all SPI time series and over lead times varying between 1 and 6 months. The structure of the model was simplified in the WANN models, which makes them very convenient and parsimonious. The final forecasting models can be utilized for drought early warning.

Accurate estimation of reference evapotranspiration (ET o ) is profoundly crucial in crop modeling, sustainable management, hydrological water simulation, and irrigation scheduling, since it accounts for more than two-thirds of global precipitation losses. Therefore, ET o -based estimation is a major concern in the hydrological cycle. The estimation of ET o can be determined using various methods, including field measurement (the scale of the lysimeter), experimental methods, and mathematical equations. The Food and Agriculture Organization recommended the Penman-Monteith (FAO-56 PM) method which was identified as the standard method of ET o estimation. However, this equation requires a large number of measured climatic data (maximum and minimum air temperature, relative humidity, solar radiation, and wind speed) that are not always available on meteorological stations. Over the decade, the artificial intelligence (AI) models have received more attention for estimating ET o on multi-time scales. This research explores the potential of new hybrid AI model, i.e., support vector regression (SVR) integrated with grey wolf optimizer (SVR-GWO) for estimating monthly ET o at Algiers, Tlemcen, and Annaba stations located in the north of Algeria. Five climatic variables namely relative humidity (RH), maximum and minimum air temperatures (T max and T min ), solar radiation (R s ), and wind speed (U s ) were used for model construction and evaluation. The proposed hybrid SVR-GWO model was compared against hybrid SVR-genetic algorithm (SVR-GA), SVR-particle swarm optimizer (SVR-PSO), conventional artificial neural network (ANN), and empirical (Turc, Ritchie, Thornthwaite, and three versions of Valiantzas methods) models by using root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC), and Willmott index (WI), and through graphical interpretation. Through the results obtained, the performance of the SVR-GWO provides very promising and occasionally competitive results compared to other data-driven and empirical methods at study stations. Thus, the proposed SVR-GWO model with five climatic input variables outperformed the other models (RMSE = 0.0776/0.0613/0.0374 mm, NSE = 0.9953/ 0.9990/0.9995, PCC = 0.9978/0.9995/0.9998 and WI = 0.9988/0.9997/0.9999) for estimating ET o at Algiers, Tlemcen, and Annaba stations, respectively. In conclusion, the results of this research indicate the suitability of the proposed hybrid artificial intelligence model (SVR-GWO) at the study stations. Besides, promising results encourage researchers to transfer and test these models in other locations in the world in future works.

Accurate and reliable prediction of streamflow is vital to the optimization of water resources management, reservoir flood operations, catchment, and urban water management. In this research, support vector regression (SVR) was optimized by six meta-heuristic algorithms, namely, Ant Lion Optimization (SVR-ALO), Multi-Verse Optimizer (SVR-MVO), Spotted Hyena Optimizer (SVR-SHO), Harris Hawks Optimization (SVR-HHO), Particle Swarm Optimization (SVR-PSO), and Bayesian Optimization (SVR-BO) to predict daily streamflow in Naula watershed, State of Uttarakhand, India. The significant inputs and parameter combinations for hybrid SVR models were extracted through Gamma Test before processing. The results obtained by hybrid SVR models during calibration (training) and validation (testing) periods, which were compared against observed streamflow using performance indicators of root mean square error (RMSE), scatter index (SI), coefficient of correlation (COC), Willmott index (WI), and by visual inspection (time-series plot, scatter plot and Taylor diagram). The results of comparison demonstrated that SVR-HHO during calibration/validation periods (RMSE = 92.038/181.306 m3/s, SI = 0.401/0.715, COC = 0.881/0.717, and WI = 0.928/0.777) had superior performance to the SVR-ALO, SVR-MVO, SVR-SHO, SVR-PSO, and SVR-BO models in predicting daily streamflow in the study basin. In addition, the new HHO algorithm outperformed the other meta-heuristic algorithms in terms of prediction accuracy.

Drought is a complex natural phenomenon, so, precise prediction of drought is an effective mitigation tool for measuring the negative consequences on agriculture, ecosystems, hydrology, and water resources. The purpose of this research was to explore the potential capability of support vector regression (SVR) integrated with two meta-heuristic algorithms i.e., Grey Wolf Optimizer (GWO), and Spotted Hyena Optimizer (SHO), for meteorological drought (MD) prediction by utilizing EDI (effective drought index). For this objective, the two-hybrid SVR–GWO, and SVR–SHO models were constructed at Kumaon and Garhwal regions of Uttarakhand State (India). The EDI was computed in both study regions by using monthly rainfall data series to calibrate and validate the advanced hybrid SVR models. The autocorrelation function (ACF) and partial-ACF (PACF) were utilized to determine the optimal inputs (antecedent EDI) for EDI prediction. The results produced by the hybrid SVR models were compared with the calculated (observed) values by employing the statistical indicators and through graphical inspection. A comparison of results demonstrates that the hybrid SVR–GWO model outperformed to the SVR–SHO models for all study stations located in Kumaon and Garhwal regions. Also, the results highlighted the better suitability, supremacy, and convergence behavior of meta-heuristic algorithms (i.e., GWO and SHO) for meteorological drought prediction in the study regions.

Recently, coupled Wavelet transform and Neural Networks models (WANN) were extensively used in hydrological drought forecasting, which is an important task in drought risk management. Wavelet transforms make forecasting model more accurate, by extracting information from several levels of resolution. The selection of an adequate mother wavelet and optimum decomposition level play an important role for successful implementation of wavelet neural network based hydrologic forecasting models.The main objective of this research is to look into the effects of various discrete wavelet families and the level of decomposition on the performance of WANN drought forecasting models that are developed for forecast drought in the Algerois catchment for long lead time. The Standard Precipitation Index (SPI) is used as a drought measuring parameter at three-, six- and twelve-month scales. Suggested WANN models are tested using 39 discrete mother wavelets derived from five families including Haar, Daubechies, Symlets, Coiflets and the discrete approximation of Meyer. Drought is forecasted by the best model for various lead times varying from 1-month lead time to the maximum forecast lead time. The obtained results were evaluated using three performance criteria (NSE, RMSE and MAE).The results show that WANN models with discrete approximation of Meyer have the best forecast performance. The maximum forecast lead times are 36-month for SPI-12, 18-month for SPI-6 and 7- month for the SPI-3. Drought forecasting for long lead times have significant values in drought risk and water resources management.

Drought mitigation and prevention require a broader knowledge of the spatio-temporal characteristics and return periods of droughts over several years. In this research, drought characteristics (severity, duration, frequency and areal extent) have been analysed in northern Algeria by using the Standardized Precipitation Index to identify drought events from 194 precipitation stations. For frequency analysis, three Archimedean copula families were used to find a relationship between drought duration and severity. The severity–duration–frequency (SDF) and the severity–area–frequency (SAF) curves were obtained. The SDF and SAF curves are then used to build three-dimensional surfaces of drought severity, drought duration and cumulated percentage of the affected area (SDA) for each return period. It has been shown that the return periods of maximum drought events severity vary according to their durations. To address the issue of long-term droughts, a new classification of dry events based on drought severities is proposed. The obtained results show that the western part of Algeria is the most sensitive to severe/extreme droughts of short durations and high probabilities of exceedance. For long-term durations, the study area was sensitive to mild droughts with lower probabilities.

For effective planning of irrigation scheduling, water budgeting, crop simulation, and water resources management, the accurate estimation of reference evapotranspiration (ET o ) is essential. In the current study, the hybrid support vector regression (SVR) coupled with Whale Optimization Algorithm (SVR-WOA) was employed to estimate the monthly ET o at Algiers and Tlemcen meteorological stations positioned in the north of Algeria under three different optimal input scenarios. Monthly climatic parameters, i.e., solar radiation ( R s ), wind speed ( U s ), relative humidity (RH), and maximum and minimum air temperatures ( T max and T min ) of 14 years (2000–2013), were obtained from both stations. The accuracy of the hybrid SVR-WOA model was appraised against hybrid SVR-MVO (Multi-Verse Optimizer), and SVR-ALO (Ant Lion Optimizer) models through performance measures, i.e., mean absolute error (MAE), root-mean-square error (RMSE), index of scattering (IOS), index of agreement (IOA), Pearson correlation coefficient (PCC), Nash-Sutcliffe efficiency (NSE), and graphical interpretation (time-variation and scatter plots, radar chart, and Taylor diagram). The results showed that the SVR-WOA model performed superior to the SVR-MVO and SVR-ALO models at both stations in all scenarios. The SVR-WOA-1 model with five inputs (i.e., T min, T max, RH, U s , R s : scenario-1) had the lowest value of MAE = 0.0658/0.0489 mm/month, RMSE = 0.0808/0.0617 mm/month, IOS = 0.0259/0.0165, and the highest value of NSE = 0.9949/0.9989, PCC = 0.9975/0.9995, and IOA = 0.9987/0.9997 for testing period at both stations, respectively. The proposed hybrid SVR-WOA model was found to be more appropriate and efficient in comparison to SVR-MVO and SVR-ALO models for estimating monthly ET o in the study region.

The most critical wastewater quality indicators (WQIs) for diagnosing the performance of wastewater treatment plants are the biological oxygen demand (BOD) and chemical oxygen demand (COD). Measuring the five-day of biological oxygen demand (BOD5) level in wastewater requires significant consumption of energy and. This research unprecedentedly develops a new hybrid machine learning (ML) technique to predict BOD 5 based on the neural architecture search (NAS) algorithm coupled with deep neural network (DNN) and random forest regression (RFR) models for the first time. In order to calibrate and validate the proposed models, various wastewater quality variables including wastewater potential hydrogen (pH), specific conductance (SC), total suspended solids (TSS), and COD were selected. The performance accuracy of these hybrid models was compared with traditional multilayer perceptron neural network (MLPNN), RFR and multiple linear regression (MLR) models. The results have been compared and evaluated based on graphical inspection (Boxplot, Violin plot, Spider plot, and Taylor diagram) and statistical indicators such as correlation coefficient (R), Willmott's index of agreement (WI), root mean square error (RMSE) and mean absolute error (MAE). Notably, our study demonstrated that the accuracy of BOD5 prediction increased using only pH, SC, TSS and COD. It can also be concluded that the best accuracy was obtained using the NAS-RFR with an R, WI, RMSE and MAE of 0.953, 0.967, 4.775mg/L and 2.944mg/L, respectively at the Baraki plant and using the NAS-DNN model with an R, WI, RMSE and MAE of 0.934, 0.953, 1.886 mg/L and 1.400 mg/L, respectively at the Reghaia plant. Our results underline the promising potential of the NAS-DNN and NAS-RFR hybrid models for accurate prediction of BOD5 in Algeria. This model, which outperforms traditional models, can significantly help decision-makers in wastewater treatment plants and water quality indices.

The knowledge of the climatic behavior especially that one of semi-arid regions is required to optimize the management of water resources. Here climate variability is directly related to water resources that are of a high socio-economic and environmental significance. This work deals mainly with a statistical analysis of the precipitation regime to assess its spatial distribution and temporal variation in north-western Algeria. For this, a time series and a principal component analysis are performed on rainfall series representing annual precipitations of twenty-one meteorological stations for the period 1914 to 2004, the most complete and longest of West Algeria, in order to detect patterns and trends in the region. A spectral analysis of the time series revealed the existence of a period of roughly 30 years for all stations. Furthermore, the trend of a wide part of the obtained spectra suggests the existence of another period longer than the samples size.