Drought is a fundamental physical feature of the climate pattern worldwide. Over the past few decades, a natural disaster has accelerated its occurrence, which has significantly impacted agricultural systems, economies, environments, water resources, and supplies. Therefore, it is essential to develop new techniques that enable comprehensive determination and observations of droughts over large areas with satisfactory spatial and temporal resolution. This study modeled a new drought index called the Combined Terrestrial Evapotranspiration Index (CTEI), developed in the Ganga river basin. For this, five Machine Learning (ML) techniques, derived from artificial intelligence theories, were applied: the Support Vector Machine (SVM) algorithm, decision trees, Matern 5/2 Gaussian process regression, boosted trees, and bagged trees. These techniques were driven by twelve different models generated from input combinations of satellite data and hydrometeorological parameters. The results indicated that the eighth model performed best and was superior among all the models, with the SVM algorithm resulting in an R² value of 0.82 and the lowest errors in terms of the Root Mean Squared Error (RMSE) (0.33) and Mean Absolute Error (MAE) (0.20), followed by the Matern 5/2 Gaussian model with an R² value of 0.75 and RMSE and MAE of 0.39 and 0.21 mm/day, respectively. Moreover, among all the five methods, the SVM and Matern 5/2 Gaussian methods were the best-performing ML algorithms in our study of CTEI predictions for the Ganga basin.

This study represents the first attempt to chemically modify wheat straw (WS) using 3-chloro propyl trimethoxysilane (CPTMS) and (1,4-diazabicyclo[2.2.2]octane) (DABCO). Field emission scanning electron micrographs (FESEM), energy dispersive spectroscopy (EDS), Thermogravimetric analysis (TGA) and Fourier transform infrared (FTIR) spectra confirmed the successful morphological and structural modification of WS and the thermal stability of the modified WS (MWS). The MWS was used to remove nitrate from water. The optimum conditions of nitrate adsorption onto MWS were examined by conducting batch experiments. The results indicated that 85% of nitrate was removed under the conditions of initial nitrate concentration = 20 mg L⁻¹, initial solution pH = 7, contact time = 10 min, MWS dosage = 2 g L⁻¹ and temperature ≈ 25 °C. The kinetic adsorption data were best fitted to general order model and the adsorption process occurred at three distinct stages. The equilibrium adsorption data were well described by Langmuir isotherm. Additionally, separation factor values were smaller than 1, implying that the adsorption process was favorable. The presence of competing anions impeded the nitrate adsorption in the order of sulfate > chloride > bicarbonate> phosphate. Thermodynamic parameters suggested that the adsorption process was exothermic, feasible and spontaneous in nature. Overall, the MWS could achieve efficient removal of nitrate under the simplest operating conditions.