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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. [ABSTRACT FROM AUTHOR]

Accurate monitoring and forecasting of drought are crucial. They play a vital role in the optimal functioning of irrigation systems, risk management, drought readiness, and alleviation. In this work, Artificial Intelligence (AI) models, comprising Multi-layer Perceptron Neural Network (MLPNN) and Co-Active Neuro-Fuzzy Inference System (CANFIS), and regression, model including Multiple Linear Regression (MLR), were investigated for multi-scalar Standardized Precipitation Index (SPI) prediction in the Garhwal region of Uttarakhand State, India. The SPI was computed on six different scales, i.e., 1-, 3-, 6-, 9-, 12-, and 24-month, by deploying monthly rainfall information of available years. The significant lags as inputs for the MLPNN, CANFIS, and MLR models were obtained by utilizing Partial Autocorrelation Function (PACF) with a significant level equal to 5% for SPI-1, SPI-3, SPI-6, SPI-9, SPI-12, and SPI-24. The predicted multi-scalar SPI values utilizing the MLPNN, CANFIS, and MLR models were compared with calculated SPI of multi-time scales through different performance evaluation indicators and visual interpretation. The appraisals of results indicated that CANFIS performance was more reliable for drought prediction at Dehradun (3-, 6-, 9-, and 12-month scales), Chamoli and Tehri Garhwal (1-, 3-, 6-, 9-, and 12-month scales), Haridwar and Pauri Garhwal (1-, 3-, 6-, and 9-month scales), Rudraprayag (1-, 3-, and 6-month scales), and Uttarkashi (3-month scale) stations. The MLPNN model was best at Dehradun (1- and 24- month scales), Tehri Garhwal and Chamoli (24-month scale), Haridwar (12- and 24-month scales), Pauri Garhwal (12-month scale), Rudraprayag (9-, 12-, and 24-month), and Uttarkashi (1- and 6-month scales) stations, while the MLR model was found to be optimal at Pauri Garhwal (24-month scale) and Uttarkashi (9-, 12-, and 24-month scales) stations. Furthermore, the modeling approach can foster a straightforward and trustworthy expert intelligent mechanism for projecting multi-scalar SPI and decision making for remedial arrangements to tackle meteorological drought at the stations under study. [ABSTRACT FROM AUTHOR]

• Monthly scale of rainfall is forecasted at the Himalaya region of India. • Novel intelligence machine learning model is proposed for this purpose. • Data time series filter is incorporated for data analysis prior prediction. • An enhanced version of deep learning is developed for the learning process. • New technological forecasting model is produced for reliable rainfall detection. Accurate forecasting of rainfall is extremely important due to its complex nature and enormous impacts on hydrology, floods, droughts, agriculture, and monitoring of pollutant concentration levels. In this study, a new multi-decomposition deep learning-based technique was proposed to forecast monthly rainfall in Himalayan region of India (i.e., Haridwar and Nainital). In the first stage, the original rainfall signals as the individual accessible datasets were decomposed into intrinsic mode decomposition functions (IMFs) through the time-varying filter-based empirical mode decomposition (TVF-EMD) technique, and then the significant lagged values were computed from the decomposed sub-sequences (i.e., IMFs) using the partial autocorrelation function (PACF). In the second stage, the PACF-based decomposed IMFs signals were again decomposed by the Singular Valued Decomposition (SVD) approach to reduce the dimensionality and enhance the forecasting accuracy. The machine learning approaches including the bidirectional long-short term memory reinforced with the Encoder-Decoder Bidirectional (EDBi-LSTM), Adaptive Boosting Regression (Adaboost), Generalized Regression Neural Network (GRNN), and Random Forest (RF) were used to construct the hybrid forecasting models. Also, several statistical metrics i.e., correlation coefficient (R), root mean square error (RMSE), and Nash-Sutcliffe efficiency (NSE) and graphical interpretation tools were employed to evaluate the hybrid (T VF-EMD -S VD -R F , T VF-EMD -S VD -E DBi-LSTM , T VF-EMD -S VD -A daboost , and T VF-EMD -S VD -G RNN) and standalone counterpart (EDBi-LSTM, Adaboost, RF, and GRNN) models. The outcomes of monthly rainfall forecasting ascertain that the T VF-EMD -S VD -E DBi-LSTM in the Haridwar (R = 0.5870, RMSE = 118.4782 mm, and NSE = 0.3116) and Nainital (R = 0.9698, RMSE = 44.3963 mm, NSE = 0.9388) outperformed the benchmarking models. [ABSTRACT FROM AUTHOR]