AI-Based Spring Revival and Recharge Planning for Tribal Areas

• I.Description
• A.Background

Springs are an important source of water for communities in hilly and tribal regions. The availability and sustainability of spring water depends on the recharge of the underlying aquifer. However, the recharge zone from which rainfall contributes to a particular spring is often difficult to identify through surface observations alone. Groundwater movement is influenced by terrain, geological structures, slope, drainage patterns, rainfall and land-use characteristics. Identification of the recharge zone and selection of suitable locations for recharge interventions therefore requires integration of multiple spatial and hydrogeological parameters along with field-based assessment. At present, identification of recharge zones and suitable locations for recharge interventions often requires site-specific field surveys and specialised hydrogeological expertise. This can be time-consuming and difficult to scale across remote and geographically challenging tribal areas. There is a need for a technology-enabled decision-support mechanism that can help field agencies identify and prioritise potential recharge areas and intervention sites using available data.

B. Description The above problem statement envisages development of an AI/ML-enabled geospatial decision-support system to assist in delineating the probable recharge zone of a spring and prioritising suitable locations for spring-revival interventions. The system should be capable of integrating spatial, geological, hydrogeological, climatic and field datasets, including: Geological structures such as strike, dip, fractures, faults and joints Spring characteristics such as location, elevation, discharge and seasonal behaviour Terrain parameters derived from Digital Elevation Models (DEM), slope, aspect, elevation and drainage Geological formations, rock types and geological contacts Rainfall, runoff and spring-discharge trends Land-use/land-cover information Existing water conservation and recharge structures Field observations and community-generated information, wherever available The system should analyse these datasets to identify spatial patterns associated with potential recharge areas and generate probability-based estimates of recharge suitability. It should be capable of working with varying levels of data availability and quality, while indicating the level of confidence associated with its outputs. The system should also support field verification of identified recharge areas and proposed intervention sites, allowing field observations and subsequent spring-discharge information to be incorporated for assessment and improvement of the model.

C. Expected Solution An AI-powered Spring Recharge Zone Identification and Decision Support System should be developed to support evidence-based planning and prioritisation of spring-revival interventions in tribal and hilly areas. The solution should: Identify probable recharge zones/springsheds using available spatial, geological, hydrogeological, climatic and field datasets, with appropriate indication of confidence or uncertainty. Generate recharge suitability and priority maps to identify and rank areas with higher potential for groundwater recharge and spring revival interventions. Identify suitable intervention locations and provide indicative recommendations for appropriate recharge measures based on relevant site characteristics. Flag potentially unsuitable or high-risk locations, including areas where interventions may have limited effectiveness or potential geological/landslide risks. Provide an interactive GIS-based interface and field-validation mechanism to enable field agencies to visualise outputs, validate identified locations and incorporate subsequent observations and spring-discharge data. The overarching goal is to improve the efficiency, scalability and evidence base of spring-revival planning by enabling government agencies to identify probable recharge zones and prioritise technically suitable groundwater recharge interventions, particularly in remote and tribal areas. II. Ministry/ Department: Ministry of Tribal Affairs III. Category: Software IV. Theme: Agriculture, Food and Rural Technology

V. YouTube Link/ Video Link: NA VI. Data Link Set: NA