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In agroecosystems, drought is a critical climatic phenomenon that affects evapotranspiration and induces water stress in plants. The objective in this study was to characterize and forecast water stress in the Hyderabad region of India using artificial intelligence models. The monthly precipitation data for the period 1982–2021 was characterized by the standardized precipitation index (SPI) and modeled using the classical autoregressive integrated moving average (ARIMA) model and artificial intelligence (AI), i.e., artificial neural network (ANN) and support vector regression (SVR) model. The results show that on the short-term SPI3 time scale the studied region experienced extreme water deficit in 1983, 1992, 1993, 2007, 2015, and 2018, while on the mid-term SPI6 time scale, 1983, 1991, 2011, and 2016 were extremely dry. In addition, the prediction of drought at both SPI3 and SPI6 time scales by AI models outperformed the classical ARIMA models in both, training and validation data sets. Among applied models, the SVR model performed better than other models in modeling and predicting drought (confirmed by root mean square error—RMSE), while the Diebold–Mariano test confirmed that SVR output was significantly superior. A reduction in the prediction error of SVR by 48% and 32% (vs. ARIMA), and by 21% and 26% (vs. ANN) was observed in the test data sets for both SPI3 and SPI6 time scales. These results may be due to the ability of the SVR model to account for the nonlinear and complex patterns in the input data sets against the classical linear ARIMA model. These results may contribute to more sustainable and efficient management of water resources/stress in cropping systems.

Understanding groundwater geochemistry is crucial for water supply in arid regions. The present research was conducted in the arid Mo river watershed on the Tibetan plateau to gain insights into the geochemical characteristics, governing processes and quality of groundwater in arid endorheic watersheds. A total of 28 groundwater samples were collected from the phreatic and confined aquifers for hydrochemical analysis. The results showed that the groundwater was slightly alkaline in all aquifers of the watershed. The phreatic groundwater samples (PGs) and confined groundwater samples (CGs) had the TDS value in the ranges of 609.19–56,715.34 mg/L and 811.86–2509.51 mg/L, respectively. PGs were salter than CGs, especially in the lower reaches. Both the PGs and CGs were dominated by the Cl-Na type, followed by the mixed Cl-Mg·Ca type. The toxic elements of NO2− (0.00–0.20 mg/L for PGs and 0.00–0.60 mg/L for CGs), NH4+ (0.00–0.02 mg/L for PGs and 0.00–0.02 mg/L for CGs) and F− (0.00–4.00 mg/L for PGs and 1.00–1.60 mg/L for CGs) exceeded the permissible limits of the Chinese guidelines at some sporadic sites. Water–rock interactions, including silicates weathering, mineral dissolution (halite and sulfates) and ion exchange, were the main contributions to the groundwater chemistry of all aquifers. The geochemistry of PGs in the lower reach was also greatly influenced by evaporation. Agricultural sulfate fertilizer input was responsible for the nitrogen pollutants and salinity of PGs. All CGs and 73.91% of PGs were within the Entropy-weighted water quality index (EWQI) of below 100 and were suitable for direct drinking purposes. Precisely 8.70 and 17.39% of PGs were within the EWQI value in the range of 100–150 (medium quality and suitable for domestic usage) and beyond 200 (extremely poor quality and not suitable for domestic usage), respectively. The electrical conductivity, sodium adsorption ratio, sodium percentage and permeability index indicated that groundwater in most parts of the watershed was suitable for irrigation, and only a small portion might cause salinity, sodium or permeability hazards. Groundwater with poor quality was mainly distributed in the lower reaches. CGs and PGs in the middle-upper reaches could be considered as the primary water resources for water supply. Agricultural pollution should be paid more attention to safeguard the quality of groundwater.

Plug-in Hybrid Electric Vehicles (PHEVs) can help decarbonize road transport in urban India. To accelerate the diffusion of PHEVs, investigation of commuter preferences towards the attributes of PHEVs is necessary. Therefore, the present study analyzes prospective owners’ choice decisions towards PHEVs in a typical Indian context. A stated preference survey was designed to collect responses from the current owners of conventional vehicles (CVs) in Delhi, India, and Mixed Logit (ML) models were developed to estimate commuters’ Willingness To Pay (WTP) for a set of key PHEV-specific attributes. The decomposition effect of prospective owners’ sociodemographic characteristics and trip characteristics on the mean estimates of random parameters was investigated by developing ML models with heterogeneity. Subsequently, the influence of improvement of each PHEV-specific attribute on prospective owners’ choice probability was investigated by calculating marginal effects. Among the various PHEV-specific attributes considered in the present study, high WTPs are observed for decrease in battery recharging time, reduction in tailpipe emission and increase in electric range. Therefore, an added emphasis on these attributes by vehicle manufacturers is likely to enhance the attractiveness of PHEVs to Indian commuters. The results also highlight the importance of government subsidy for promoting PHEVs in the Indian market. Prospective owners’ income, availability of home-based parking space, and average daily trip length are found to significantly influence the choice decision of Indian commuters towards PHEVs.

The long-run relationship between economic growth and environmental quality has been estimated within the framework of the environmental Kuznets Curve (EKC). Several studies have estimated this relationship by using statistical models such as panel regression and time series regression. The current study argues that there is a nonlinear relationship between environmental quality indicators and economic and non-economic predictors and hence an appropriate nonlinear model is required to predict it. An adaptive and nonlinear model, namely radial basis function neural network (RBFNN) has been developed in this study. CO2 emission is used as the target output and renewable energy consumption share, real GDP, trade openness, urban population ratio, and democracy index are used as the predictors to estimate the EKC relationship for nineteen major CO2 emitting countries that account for 78% of the global emissions. The model developed in this study could predict the CO2 emissions of all the countries with more than 95% accuracy. This finding underlines the usefulness of the RBFNN model which can be used to predict emission levels of other pollution indicators at the global level. Further, comparing two models, one with all the predictors and the other excluding the renewable energy share, it was found that the model with renewable energy share predicts CO2 emissions more accurately. This reinforces the already strengthening campaign to encourage industries and governments to increase the share of renewable energy in total energy use.

Education is the cornerstone of improving people’s lives and achieving global sustainability. Intelligent systems assist sustainable education with various benefits, including recommending a personalized learning environment to learners. The classroom learning environment facilitates human tutors to interact with every learner and obtain the opportunity to understand the learner’s psychology and then provide learning material (access learner previous knowledge and well-align the learning material as per learner requirement) to them accordingly. Implementing this cognitive intelligence in Intelligent Tutoring System is quite tricky. This research focused on mimicking human tutor cognitive intelligence in the computer-aided system of offering an exclusive curriculum or quality education for sustainable learners. The prime focus of this research article was to evaluate the proposed SeisTutor using Kirkpatrick four-phase evaluation model. The experimental results depict the enhanced learning gained through intelligence incorporated SeisTutor against the intelligence absence, as demonstrated.

The objectives of this study were to identify the breeding objectives of Begaria cattle keepers. A semi-structured questionnaire, live animal ranking, and focus group discussions were employed to identify the breeding objectives of the farmers. The study was conducted on a total of 75 conveniently sampled households. Among the total cattle population of the sampled households, 506 heads of cattle (252 male and 254 female animals) were ranked as first best, second best, third best, and/or the worst animals. Owners were asked to choose the first best, second best, third best, and worst animals from their herd for both sexes. Reasons for ranking, important morphometric measurements, and (re)production performance of the ranked animal were taken. The R Commander package version 2.6-0 was used to analyze qualitative data and analysis of variance in R software was used to analyze quantitative data. Reasons for ranking of cows in the study area were milk yield (39.14%), calving interval (15.14), temperament (12), and body size (10.86), whereas for bulls, reasons were body size (30.39%), growth (30.66), body condition score (16.85), and color (15.19). Survival rate (Sur), calving interval (CI), milk yield (MY), and lactation milk yield (LMY) for the cows and body condition score (BCS), weaning age (WA), yearling weight (YW), and weaning weight (WW) for the bulls were significantly different (p < 0.05) mainly between the first- and last-ranked animals. Farmers were willing to pay up to Ethiopian birr (ETB) 11,500, 10,700, 10,400, and 9500 on average for first-, second-, and third-ranked breeding females and up to ETB 6300, 5800, 5200, and 4500 for breeding males. Lactation milk yield, calving interval, mothering ability, growth rate, and age at first calving were determined as breeding objective traits of the community. with suggested selection criteria of lactation milk yield, calving interval, survival rate, yearling weight, and age at first calving, respectively.

The water level in a river defines the nature of flow and is fundamental to flood analysis. Extreme fluctuation in water levels in rivers, such as floods and droughts, are catastrophic in every manner; therefore, forecasting at an early stage would prevent possible disasters and relief efforts could be set up on time. This study aims to digitally model the water level in the Kabul River to prevent and alleviate the effects of any change in water level in this river downstream. This study used a machine learning tool known as the automatic autoregressive integrated moving average for statistical methodological analysis for forecasting the river flow. Based on the hydrological data collected from the water level of Kabul River in Swat, the water levels from 2011–2030 were forecasted, which were based on the lowest value of Akaike Information Criterion as 9.216. It was concluded that the water flow started to increase from the year 2011 till it reached its peak value in the year 2019–2020, and then the water level will maintain its maximum level to 250 cumecs and minimum level to 10 cumecs till 2030. The need for this research is justified as it could prove helpful in establishing guidelines for hydrological designers, the planning and management of water, hydropower engineering projects, as an indicator for weather prediction, and for the people who are greatly dependent on the Kabul River for their survival.