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This study investigates the spatial and temporal patterns of trends and magnitude of rainfall on monthly, seasonal and annual time scales of 13 districts of Uttarakhand State located in Central Himalayan region of India. The temporal trend was analyzed using Mann-Kendall (MK), Modified Mann-Kendall (MMK), and Kendall Rank Correlation (KRC) tests at 10%, 5%, and 1% significance levels. The magnitude (slope) of rainfall trend (mm/year) was determined using Theil-Sen’s Slope (TSS) and Simple Linear Regression (SLR) tests. The autocorrelation coefficient (ACC) of three different time series was calculated at one-time lag and were tested at 10%, 5%, and 1% levels of significance for the application of MMK test. The results of analysis revealed significant positive and negative trends were observed in monthly, seasonal, and annual rainfall time series in all 13 districts of Uttarakhand state. The spatial variation of the trends based on monthly, seasonal, and annual rainfall time series data was interpolated using the Thiessen polygon (TP) method in ArcGIS 10.2 environment. The maps of spatial variability of rainfall trends were developed to help local stakeholders and water resource managers to understand the risk and vulnerability related to climate change in the region.

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.

The rising salinity trend in the country’s coastal groundwater has reached an alarming rate due to unplanned use of groundwater in agriculture and seawater seeping into the underground due to sea-level rise caused by global warming. Therefore, assessing salinity is crucial for the status of safe groundwater in coastal aquifers. In this research, a rigorous hybrid neurocomputing approach comprised of an Adaptive Neuro-Fuzzy Inference System (ANFIS) hybridized with a new meta-heuristic optimization algorithm, namely Aquila optimization (AO) and the Boruta-Random forest feature selection (FS) was developed for estimating the salinity of multi-aquifers in coastal regions of Bangladesh. In this regard, 539 data samples, including ten water quality indices, were collected to provide the predictive model. Moreover, the individual ANFIS, Slime Mould Algorithm (SMA), and Ant Colony Optimization for Continuous Domains (ACOR) coupled with ANFIS (i.e., ANFIS-SMA and ANFIS-ACOR) and LASSO regression (Lasso-Reg) schemes were examined to compare with the primary model. Several goodness-of-fit indices, such as correlation coefficient (R), the root mean squared error (RMSE), and Kling-Gupta efficiency (KGE) were used to validate the robustness of the predictive models. Here, the Boruta-Random Forest (B-RF), as a new robust tree-based FS, was adopted to identify the most significant candidate inputs and effective input combinations to reduce the computational cost and time of the modeling. The outcomes of four selected input combinations ascertained that the ANFIS-OA regarding the best accuracy in terms of (R = 0.9450, RMSE = 1.1253 ppm, and KGE = 0.9146) outperformed the ANFIS-SMA (R = 0.9406, RMSE = 1.1534 ppm, and KGE = 0.8793), ANFIS-ACOR (R = 0.9402, RMSE = 1.1388 ppm, and KGE = 0.8653), Lasso-Reg (R = 0.9358), and ANFIS (R = 0.9306) models. Besides, the first candidate input combination (C1) by three inputs, including Cl − (mg/l), Mg 2+ (mg/l), Na + (mg/l), yielded the best accuracy among all alternatives, implying the role importance of (B-RF) feature selection. Finally, the spatial salinity distribution assessment in the study area ascertained the high predictability potential of the ANFIS-OA hybrid with B-RF feature selection compared to other paradigms. The most important novelty of this research is using a robust framework comprised of the non-linear data filtering technique and a new hybrid neuro-computing approach, which can be considered as a reliable tool to assess water salinity in coastal aquifers.

The primary objectives of modern agriculture includes the environmental sustainability, low production costs, improved plants’ resilience to various biotic and abiotic stresses, and high sowing seed value. Delayed and inconsistent field emergence poses a significant threat in the production of agri-crop, especially during drought and adverse weather conditions. To open new routes of nutrients’ acquisition and revolutionizing the adapted solutions, stewardship plans will be needed to address these questions. One approach is the identification of plant based bioactive molecules capable of altering plant metabolism pathways which may enhance plant performance in a brief period of time and in a cost-effective manner. A biostimulant is a plant material, microorganism, or any other organic compound that not only improves the nutritional aspects, vitality, general health but also enhances the seed quality performance. They may be effectively utilized in both horticultural and cereal crops. The biologically active substances in biostimulant biopreparations are protein hydrolysates (PHs), seaweed extracts, fulvic acids, humic acids, nitrogenous compounds, beneficial bacterial, and fungal agents. In this review, the state of the art and future prospects for biostimulant seedlings are reported and discussed. Biostimulants have been gaining interest as they stimulate crop physiology and biochemistry such as the ratio of leaf photosynthetic pigments (carotenoids and chlorophyll), enhanced antioxidant potential, tremendous root growth, improved nutrient use efficiency (NUE), and reduced fertilizers consumption. Thus, all these properties make the biostimulants fit for internal market operations. Furthermore, a special consideration has been given to the application of biostimulants in intensive agricultural systems that minimize the fertilizers’ usage without affecting quality and yield along with the limits imposed by European Union (EU) regulations.

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.

Drought forecasting plays a vital role in managing drought and reducing its effects on agricultural systems and water resources. In the present study, three machine learning models including Gaussian Process Regression (GPR), Cascade Neural Network (Cascade-NN), and Multilayer Perceptron (MLP) neural network and their combination with the discrete wavelet transform were used to forecast Multi-scalar Standardized Precipitation Evapotranspiration Index (SPEI) (SPEI3, SPEI12, and SPEI24) 1 to 6 months ahead. It was done in Synoptic Station of Zanjan in Iran. Those meteorological data that was collected during 57 years (1961–2017) was used. The data related to the early 38 years (67%) was considered as train data, and the data related to the last 19 years (33%) was considered as test data. The results that have been obtained from this study showed that models based on wavelet have caused a high improvement in model performance in case of anticipating multi-scalar SPEI. Comparing different mother wavelets (db4, db8, sym8, coif5, and dmey) proved the dmey wavelet’s superiority. Also, a comparison of wavelet-GPR, wavelet-MLP, and wavelet-Cascade-NN models showed that in most cases, the GPR-based model could provide better results in forecasting. By increasing the forecasting interval from 1 to 6 months ahead, the accuracy of the model decreased. In the SPEI3 index, the R2 (determination coefficient) value decreased from 0.992 in the 1-month ahead forecast to 0.797 in the 6 months ahead forecast. In the SPEI12 index, the R2 value decreased from 0.996 in the 1 month ahead forecast to 0.940 in 6 months ahead forecast, and in the SPEI24 index, R2 values decreased from 0.993 in the 1 month ahead forecast to 0.962 in 6 months ahead forecast.

A new version of the fuzzy logic model, called the co-active neuro fuzzy inference system (CANFIS), is introduced for predicting standardized precipitation index (SPI). Multiple scales of drought information at six meteorological stations located in Uttarakhand State, India, are used. Different lead times of SPI were computed for prediction, including 1, 3, 6, 9, 12, and 24 months, with inputs abstracted by autocorrelation function (ACF) and partial-ACF (PACF) analysis at 5% significance level. The proposed CANFIS model was validated against two models: classical artificial intelligence model (e.g., multilayer perceptron neural network (MLPNN)) and regression model (e.g., multiple linear regression (MLR)). Several performance evaluation metrices (root mean square error, Nash-Sutcliffe efficiency, coefficient of correlation, and Willmott index), and graphical visualizations (scatter plot and Taylor diagram) were computed for the evaluation of model performance. Results indicated that the CANFIS model predicted the SPI better than the other models and prediction results were different for different meteorological stations. The proposed model can build a reliable expert intelligent system for predicting meteorological drought at multi-time scales and decision making for remedial schemes to cope with meteorological drought at the study stations and can help to maintain sustainable water resources management.

Food-based components represent major sources of functional bioactive compounds. Milk is a rich source of multiple bioactive peptides that not only help to fulfill consumers ‘nutritional requirements but also play a significant role in preventing several health disorders. Understanding the chemical composition of milk and its products is critical for producing consistent and high-quality dairy products and functional dairy ingredients. Over the last two decades, peptides have gained significant attention by scientific evidence for its beneficial health impacts besides their established nutrient value. Increasing awareness of essential milk proteins has facilitated the development of novel milk protein products that are progressively required for nutritional benefits. The need to better understand the beneficial effects of milk-protein derived peptides has, therefore, led to the development of analytical approaches for the isolation, separation and identification of bioactive peptides in complex dairy products. Continuous emphasis is on the biological function and nutritional characteristics of milk constituents using several powerful techniques, namely omics, model cell lines, gut microbiome analysis and imaging techniques. This review briefly describes the state-of-the-art approach of peptidomics and lipidomics profiling approaches for the identification and detection of milk-derived bioactive peptides while taking into account recent progress in their analysis and emphasizing the difficulty of analysis of these functional and endogenous peptides.

The forecasting of lake water level is one of the complex problems in the hydrology field owing to the incorporating with various hydrological and morphological characteristics. In this research, newly hybrid data intelligence (DI) model based on the integration of the Multilayer Perceptron (MLP) and Whale Optimization Algorithm (WOA) is developed for lake water level forecasting. The potential of the proposed hybrid MLP_WOA model is validated against several well-established DI models over the literature including the Cascade-Correlation Neural Network Model (CCNNM), Self-Organizing Map (SOM), Decision Tree Regression (DTR), Random Forest Regression (RFR), and classical MLP. The applied predictive models are examined to forecast the Van Lake water level fluctuation with monthly scale over seven-decade time period (1943–2016). The input variables are abstracted using statistical correlation analysis procedure. The modeling is diagnosed using multiple statistical metrics (i.e., root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe coefficient (NSE), Willmott’s Index (WI), Legate and McCabe’s Index (LMI), determination coefficient (R2)). In addition, graphical distribution data such as the Taylor diagram, violin plot, and point density are investigated. Results indicated that the MLP_WOA model performed superior prediction results over the comparable models based on forecasting performance. Five-month lead times performed the best results for the prediction procedure. In quantitative terms, the RMSE and MAE are reduced by 29.8% and 33.9%, 48.3% and 52%, 57.6% and 59.7%, 53.9% and 58.3%, and 25.3% and 23.9% using the MLP_WOA model over CCNNM, SOM, DTR, RFR, and MLP models, respectively. In comparison with the literature studies, using longer span of historical data elevated the forecasting accuracy. In summary, MLP_WOA model provided an applicable and simple methodology for Van Lake water level forecasting owing to its simple learning procedure.