Accurate information about groundwater level prediction is crucial for effective planning and management of groundwater resources. In the present study, the Artificial Neural Network (ANN), optimized with a Genetic Algorithm (GA-ANN), was employed for seasonal groundwater table depth (GWTD) prediction in the area between the Ganga and Hindon rivers located in Uttar Pradesh State, India. A total of 18 models for both seasons (nine for the pre-monsoon and nine for the post-monsoon) have been formulated by using groundwater recharge (GW_R), groundwater discharge (GW_D), and previous groundwater level data from a 21-year period (1994–2014). The hybrid GA-ANN models’ predictive ability was evaluated against the traditional GA models based on statistical indicators and visual inspection. The results appraisal indicates that the hybrid GA-ANN models outperformed the GA models for predicting the seasonal GWTD in the study region. Overall, the hybrid GA-ANN-8 model with an 8-9-1 structure (i.e., 8: inputs, 9: neurons in the hidden layer, and 1: output) was nominated optimal for predicting the GWTD during pre- and post-monsoon seasons. Additionally, it was noted that the maximum number of input variables in the hybrid GA-ANN approach improved the prediction accuracy. In conclusion, the proposed hybrid GA-ANN model’s findings could be readily transferable or implemented in other parts of the world, specifically those with similar geology and hydrogeology conditions for sustainable planning and groundwater resources management.

This study examines the grassroot perceptions surrounding the adoption of Vetiver grass (Chrysopogon zizanioides) as a sustainable solution for soil and water conservation in the Garhwal Himalaya. The study is based on responses from 260 respondents across multiple villages in the region, obtained through a multistage sampling approach, and uses descriptive statistics (frequency counts, percentages) and Pearson correlation as statistical tools. It assesses awareness levels, perceived benefits, barriers to adoption, and the key socio-economic factors influencing the use of vetiver grass in the Garhwal region. The analysis revealed that only 18.85% of respondents were both aware of and using vetiver, while over half (51.53%) had no awareness of the plant and its conservation potential. Although some respondents recognized vetiver’s contributions to soil fertility, fodder provision, and erosion control, a substantial proportion indicated uncertainty or limited knowledge about its benefits. Key barriers identified include a lack of technical support (58.85%), limited awareness (51.54%), and restricted access to planting materials (45%), with a significant portion of respondents unsure about the challenges they faced. These findings highlight a pressing need for targeted intervention, awareness programs, improved extension services, and strengthened supply chains to enable wider adoption. By addressing these gaps, vetiver can play a transformative role in promoting ecological restoration and enhancing sustainable rural livelihoods in the vulnerable regions of the Himalaya.

Hydrological drought forecasting in semi-arid basins is challenging due to the combined influence of meteorological forcing, large-scale atmospheric teleconnections, and basin memory processes, which are rarely jointly analysed within a leakage-free predictive framework. This study addresses this gap by evaluating gradient-boosted trees and neural forecasting models for one-month-ahead prediction of the Standardized Runoff Index (SRI) in two sub-basins of the Wadi Sahaouat Basin, Algeria. The models include gradient-boosted regression trees (GBRT), A-N-BEATS, A-N-HiTS, and TiDE, representing distinct forecasting architectures. Predictors consist of the Standardised Precipitation Index (SPI), seven teleconnection indices (NAO, AO, EAWR, SCAND, MEI, SOI, WeMO), and their one- to three-month lags. Two scenarios are tested: Scenario 1 uses SPI and teleconnection lags only, while Scenario 2 additionally includes lagged SRI values (SRI_lag1–3) to represent hydrological memory. A train-only Variance Inflation Factor (VIF > 10) procedure is applied to remove multicollinearity without data leakage. In Basin 1, SRI lags were excluded due to strong collinearity with SPI lags (r = 0.984), resulting in identical inputs for both scenarios. In Basin 2, SRI lags were retained to assess their predictive contribution. GBRT achieved the best overall performance across both basins and scenarios, with mean RMSE, NSE, and KGE values of 0.0682, 0.9907, and 0.8945, respectively. TiDE ranked second overall, with a mean RMSE of 0.1166, followed by A-N-HiTS in third place with a mean RMSE of 0.1203 and A-N-BEATS with the weakest overall performance, with a mean RMSE of 0.2159. These results indicate that gradient-boosted trees remain highly competitive with neural models for small monthly hydrological datasets and that the value of hydrological memory is basin-dependent and varies according to its independence from concurrent meteorological forcing.

Migration is a defining process in mountain regions worldwide, reshaping lives, livelihoods, economies, and landscapes, yet its developmental impacts remain deeply contested. This study examines the scale, patterns, drivers, and implications of out-migration in the Uttarakhand Himalaya, with particular attention to its intersections with the broader agenda of sustainable mountain development. Drawing on two years of ethnographic fieldwork across all ten mountainous districts of Uttarakhand, the research combines complete household enumeration in ten villages with focus group discussions and key informant interviews. The survey data were analysed using descriptive statistics and a binary logistic regression model, while qualitative insights provided community perspectives on the drivers and impacts of out-migration in the region. The findings reveal stark altitudinal and district-level variations: low-lying, better-connected villages report the highest out-migration, while remote, high-altitude settlements show lower rates. Migration is strongly selective, dominated by working-age males, educated youth, socially advantaged groups, and individuals employed in the service sector, while disadvantaged groups such as Scheduled Castes and women face constrained mobility due to limited resources and restrictive gender norms. Although remittances are received, their scale is limited because of high rates of permanent out-migration and they are largely spent on household consumption, reducing their developmental potential. At the same time, mass out-migration has led to village depopulation, abandonment of agro-pastoral land, feminization of mountain agriculture, declining farm output, and rising human-wildlife conflict. Interpreted through Neo-classical migration theory, the Push-Pull Model, the Neo-Marxist perspective, and the Aspirations-Capabilities framework, the results show that migration embodies both agency and constraint, producing uneven outcomes across altitudes, districts, and social groups. Importantly, the study emphasizes that migration should not merely be viewed as a developmental problem but as a mechanism that, if strategically harnessed, can contribute to sustainable mountain development in the Indian Himalayan Region (IHR). Leveraging migration for the sustainable development requires channeling remittances into productive rural investments, supporting return migrants in entrepreneurship and knowledge transfer, and strengthening social remittances to improve education, governance, and climate resilience. The study concludes with region-specific policy recommendations that focus on short-term livelihood support and longer-term systemic reforms, aiming to align migration with the goals of sustainable mountain development in the Uttarakhand Himalaya in particular, and the IHR more broadly.