http://localhost:8080/xmlui/handle/123456789/2 2026-08-03T04:11:22Z 2026-08-03T04:11:22Z HAFIZA NAAZ SOUMYA S JACOB ANTONY Sathian K K, (Guide) http://localhost:8080/xmlui/handle/123456789/2416 2026-07-25T04:50:36Z 2026-01-01T00:00:00Z

ESTIMATION OF SOIL MOISTURE AT VARIOUS DEPTHS USING GEE RESOURCES AND ITS GROUND VALIDATION HAFIZA NAAZ; SOUMYA S; JACOB ANTONY; Sathian K K, (Guide)

2026-01-01T00:00:00Z Anupriya S Ganga S Biju Rahul Prasad Gowri Nanda P V Namitha M R, (Guide) http://localhost:8080/xmlui/handle/123456789/2409 2026-07-20T08:57:14Z 2026-01-01T00:00:00Z

GEOPHYSICAL TECHNIQUES FOR AQUIFER CHARACTERISTICS - A CASE STUDY OF KCAEFT CAMPUS, MALAPPURAM KERALA Anupriya S; Ganga S Biju; Rahul Prasad; Gowri Nanda P V; Namitha M R, (Guide) Groundwater is the primary source of fresh water for domestic, agricultural and industrial purposes, meeting over 85 per cent of India's drinking water and 60 per cent of its irrigation requirements (World Bank, 2012). Malappuram district, where the study area is located, records a groundwater extraction stage of 67.22 per cent with seven blocks classified as semi-critical (CGWB, 2019). In this context, a geophysical investigation was undertaken at the campus of Kelappaji College of Agricultural Engineering and Food Technology (KCAEFT), Tavanur, situated at 10.85° N latitude and 75.97° E longitude, to characterise subsurface aquifer formations and assess groundwater potential. Vertical Electrical Sounding (VES) surveys were conducted at seven locations (L1–L7) across the campus using a Signal Stacking Resistivity Meter (MODEL SSR-MP-ATS) with the Wenner electrode configuration. Current electrode spacing (AB) ranged from 6 m to 60 m, and potential electrode spacing (MN) from 2 m to 20 m. Field data were interpreted using IPI2WIN software, which employs an iterative forward-modelling approach to derive true subsurface resistivity, layer thickness and depth (Mohammad et al., 2013). Subsurface lithology was classified using resistivity ranges established by Azhar et al. (2017). Interpretation of sounding curves revealed two to three distinct subsurface layers, with H, K, A and Q-type curves identified across the study area. The H-type curve at L4 (Coconut Farm), with a conductive intermediate layer of 507.9 Ω-m and a bedrock depth of 0.89 m, indicated the most favourable aquifer conditions. K-type curves at L1, L5 and L7 reflected high-resistivity lateritic or hard rock in the middle layer. The A-type curve at L2 indicated a transition from clayey topsoil to hard rock, while Q-type curves at L3 and L6 represented progressively decreasing resistivity consistent with weathered formations. The depth to bedrock across all locations ranged from 0.89 m to 14.3 m, highlighting significant subsurface heterogeneity. The study demonstrates that the electrical resistivity method combined with IPI2WIN-based interpretation is an effective tool for delineating subsurface lithology, identifying potential aquifer zones, and supporting borehole site selection and sustainable groundwater management.

2026-01-01T00:00:00Z AMAYA P RAHANA SATHAR AVANTHIKA K SHEEJA P S, (Guide) http://localhost:8080/xmlui/handle/123456789/2407 2026-07-20T08:48:11Z 2026-01-01T00:00:00Z

WATER LEAKAGE DETECTION IN IRRIGATION PIPELINE USING IoT AMAYA P; RAHANA SATHAR; AVANTHIKA K; SHEEJA P S, (Guide) This project focuses on solving the problem of water wastage caused by undetected leaks in agricultural irrigation pipelines. Standard inspection methods are slow and often locate leaks only after significant amounts of water have been lost. To fix this delay, this study designs and tests a low-cost, automated Internet of Things (IoT) system that monitors pipelines continuously and catches leaks in real time. The system layout features a 4-meter PVC pipeline, a water pump, and two inexpensive YFS201 flow sensors installed at the pipeline's inlet and outlet. An ESP32 microcontroller constantly reads the water flow data from both sensors. When a leak occurs, it creates an imbalance between the inlet and outlet flow rates; if this difference crosses a pre-set limit, the ESP32 automatically rings a buzzer, turns on a warning LED, sends an text message to the operator using a GSM module, and trips a relay to shut off the pump immediately. The experimental prototype was tested under two different leak conditions using a bypass valve to simulate a pipe break. The system successfully achieved a 100% detection rate and caught every simulated leak in an average time of just 5.073 seconds. By shutting down the system so quickly, the automated setup tightly limited water loss to minor amounts (about 1 L per event), proving it is a highly dependable and practical solution for smart water conservation in farming

2026-01-01T00:00:00Z AHALYA R ANSA AUGUSTINE ASHNA S POOJA PRAKASH Abdul Hakkim, V.M (Guide) http://localhost:8080/xmlui/handle/123456789/2404 2026-07-20T07:17:34Z 2026-07-01T00:00:00Z

SPATIAL DISTRIBUTION OF SOIL EROSION USING GIS- BASED RUSLE MODELLING AHALYA R; ANSA AUGUSTINE; ASHNA S; POOJA PRAKASH; Abdul Hakkim, V.M (Guide) Soil erosion is a major environmental problem that leads to land degradation, loss of soil fertility, reduced agricultural productivity and deterioration of water resources. Accurate assessment of soil erosion is essential for sustainable watershed management and the implementation of effective conservation measures. This study evaluates the spatial distribution of soil erosion in the Kalpathy Puzha sub-basin of the Bharathapuzha River Basin, Kerala, using the Revised Universal Soil Loss Equation (RUSLE) integrated with Geographic Information System (GIS) and Remote Sensing (RS) techniques. The RUSLE model estimates average annual soil loss by integrating five factors: rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C) and conservation practice (P). Spatial data sets including Digital Elevation Model (DEM), rainfall data, soil maps and land use/land cover information were processed in a GIS environment to generate individual factor maps and estimate soil erosion across the study area. The estimated annual soil loss ranged from 0 to 28.99 t ha⁻¹ yr⁻¹, indicating considerable spatial variation in erosion intensity. Most parts of the basin were classified under low to moderate erosion risk, while high erosion was concentrated in the northern and north-eastern regions, mainly due to steep slopes, higher runoff potential and sparse vegetation cover. The study highlights the significant influence of topography and vegetation on soil erosion and identifies erosion-prone areas requiring priority conservation measures. The integration of RUSLE with GIS proved to be an effective and reliable approach for soil erosion assessment and mapping. The results provide valuable information for watershed planning and support the implementation of suitable soil and water conservation practices to promote sustainable land and water resource management in the Kalpathy Puzha sub-basin.

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