Syed Irtiza Majid

Food security and the sustainability of ecosystems are seriously threatened by land degradation, especially in the Indo‐Gangetic Plains where soil erosion, salinization, and waterlogging are the main causes. This study integrated these three forms into a unified, versatile, and globally adaptable Land Degradation Index (LDI) for comprehensive land degradation assessment, using Sultanpur district, Uttar Pradesh, India as a case study. Soil erosion susceptibility was initially assessed using the Revised Universal Soil Loss Equation, whilst salinization and waterlogging were independently evaluated with a Frequency Ratio using 25 and 21 factors, respectively. The resulting susceptibility maps demonstrated high predictive accuracies, with Area Under the Curve (AUC) values of 97.3%, 94.5%, and 90.1%, respectively. LDI was subsequently calculated using these maps as inputs to integrate the three degradation processes into a unified index representing overall land degradation intensity. It identified and mapped the most severely affected areas, revealing that approximately 20% of the agricultural land in the study area was impacted by land degradation. This study also developed novel random forest regression models integrated with SHapley Additive exPlanations (SHAP)‐based Game theory to examine the behaviour of conditioning factors in high and low susceptibility zones of Sultanpur district. The models fitted with MSE < 0.0046, RMSE < 0.07, MAE < 0.04, RMSLE < 0.05, and MRE < 0.04 and R ² > 0.86. Soil salinization was found to be the primary driver of soil fertility loss in this study. This salinization is primarily driven by vegetation loss, changes in soil pH, overuse of nitrogen‐rich fertilisers, and proximity to canals. Identifying the key drivers of land degradation and understanding their localised impacts provides vital insights for promoting sustainable agriculture and guiding evidence‐based policymaking. These findings further highlight the broader global relevance of adopting site‐specific land management strategies, particularly through vegetation restoration, balanced fertiliser use, and efficient irrigation, to sustain the productivity and resilience of agro‐ecosystems like Sultanpur district.

Rapid and uncontrolled urban growth in the Kumaun Himalayas in absence of proper land use policy has pushed built-up areas towards the tectonically and ecologically sensitive regions, reducing the availability of suitable built-up land while simultaneously increasing the vulnerability of both communities and environment. The identification of areas for sustainable built-up growth is of paramount importance to address the challenges arising from unregulated urban expansion. In this study GIS-based Fuzzy-AHP technique and machine learning algorithms (SVM and BN) were employed to delineate the potential built-up sites selection in Hawalbagh Block, Uttarakhand (India) using nine socio-physical drivers, including slope, aspect, LU/LC, distance to road, distance to drainage, distance to lineament, distance to landslide, distance to settlement, and lithology. The suitability maps generated by the three methods were validated using AU-ROC analysis, which demonstrated that each approach produces outstanding results with AU-ROC values more than 0.90. The comparison of the approaches shows that SVM (AUROC=0.99) outperforms BN (0.95) and GIS-based Fuzzy-AHP (0.90). The suitability maps were classified into five suitability classes. Assuming that very high and high suitability classes are acceptable for built-up expansion, the study identified potential built-up locations in the study region covering an area of 148.86 km2, 85.23 km2, and 55.25 km2 according to the Fuzzy-AHP technique, SVM model, and BN model, respectively. The suitability zonation in this study can serve as a foundation for the development of land-use policy or the formulation of master plans aimed at achieving a sustainable mountain ecology in the Kumaun Himalayas.

Progressive environmental and climatic changes have significantly increased hydrometeorological threats all over the globe. Floods have gained global significance owing to their devastating impact and their capacity to cause economic and human loss. Accurate flood forecasting and the identification of high-risk areas are essential for preventing flood impacts and implementing strategic measures to mitigate flood-related damages. In this study, an assessment of the susceptibility to riverine flooding in India was conducted utilizing Multicriteria Decision making (MCDM) and an extensive geospatial database was created through the integration of fourteen geomorphological, meteorological, hydroclimatic, and anthropogenic factors. The coupled methodology incorporates a Fuzzy Analytical Hierarchy Process (FAHP) model, which utilizes Triangular Fuzzy Numbers (TFN) to determine the Importance Weights (IWs) of various parameters and their subclasses based on the Saaty scale. Based on the determined IWs, this study identifies proximity to rivers, drainage density, and mean annual rainfall as the key factors that contribute significantly to the occurrence of riverine floods. Furthermore, as the Geographic Information System (GIS) was employed to create the Riverine Flood Susceptibility (RFS) map of India by overlaying the weighted factors, it was found that high, moderate, and low susceptibility zones across the country span of 15.33%, 26.30%, and 31.35% of the total area of the country, respectively. The regions with the highest susceptibility to flooding are primarily concentrated in the Brahmaputra, Ganga, and Indus River basins, which happen to encompass a significant portion of the country’s agricultural land (334,492 km²) potentially posing a risk to India’s food security. Approximately 28.13% of built-up area in India falls in the highly susceptible zones, including cities such as Bardhaman, Silchar, Kharagpur, Howrah, Kolkata, Patna, Munger, Bareilly, Allahabad, Varanasi, Lucknow, and Muzaffarpur, which are particularly susceptible to flooding. RFS is moderate in the Kutch-Saurashtra-Luni, Western Ghats, and Krishna basins. On the other hand, areas on the outskirts of the Ganga, Indus, and Brahmaputra basins, as well as the middle and outer portions of the peninsular basins, show a relatively low likelihood of riverine flooding. The RFS map created in this research, with an 80.2% validation accuracy assessed through AUROC analysis, will function as a valuable resource for Indian policymakers, urban planners, and emergency management agencies. It will aid them in prioritizing and executing efficient strategies to reduce flood risks effectively.

Forest fires have significant impacts on economies, cultures, and ecologies worldwide. Developing predictive models for forest fire probability is crucial for preventing and managing these fires. Such models contribute to reducing losses and the frequency of forest fires by informing prevention efforts effectively. The objective of this study was to assess and map the forest fire susceptibility (FFS) in the Indian Western Himalayas (IWH) region by employing a GIS-based fuzzy analytic hierarchy process (Fuzzy-AHP) technique, and to evaluate the FFS based on forest type and at district level in the states of Jammu and Kashmir, Himachal Pradesh, and Uttarakhand. Seventeen potential indicators were chosen for the vulnerability assessment of the IWH region to forest fires. These indicators encompassed physiographic factors, meteorological factors, and anthropogenic factors that significantly affect the susceptibility to fire in the region. The significant factors in FFS mapping included FCR, temperature, and distance to settlement. An FFS zone map of the IWH region was generated and classified into five categories of very low, low, medium, high, and very high FFS. The analysis of FFS based on the forest type revealed that tropical moist deciduous forests have a significant vulnerability to forest fire, with 86.85% of its total area having very high FFS. At the district level, FFS was found to be high in sixteen districts and very high in seventeen districts, constituting 25.7% and 22.6% of the area of the IWH region. Particularly, Lahul and Spiti had 63.9% of their total area designated as having very low FSS, making it the district least vulnerable to forest fires, while Udham Singh Nagar had a high vulnerability with approximately 86% of its area classified as having very high FFS. ROC-AUC analysis, which provided an appreciable accuracy of 79.9%, was used to assess the validity of the FFS map produced in the present study. Incorporating the FFS map into sustainable development planning will assist in devising a holistic strategy that harmonizes environmental conservation, community safety, and economic advancement. This approach can empower decision makers and relevant stakeholders to take more proactive and informed actions, promoting resilience and enhancing long-term well-being.