Forest fires are a recurring issue in many parts of the world, including India. These fires can have various causes, including human activities (such as agricultural burning, campfires, or discarded cigarettes) and natural factors (such as lightning). The present study presents a comprehensive and advanced methodology for assessing wildfire susceptibility by integrating diverse environmental variables and leveraging cutting-edge machine learning techniques across Gujarat State, India. The primary goal of the study is to utilize Google Earth Engine to compare locations in Gujarat, India, before and after forest fires. High-resolution satellite data were used to assess the amount and types of changes caused by forest fires. The present study meticulously analyzes various environmental variables, i.e., slope orientation, elevation, normalized difference vegetation index (NDVI), drainage density, precipitation, and temperature to understand landscape characteristics and assess wildfire susceptibility. In addition, a sophisticated random forest regression model is used to predict land surface temperature based on a set of environmental parameters. The maps that result depict the geographical distribution of normalized burn ratio and difference normalized burn ratio and land surface temperature forecasts, providing valuable insights into spatial patterns and trends. The findings of this work show that an automated temporal analysis utilizing Google Earth Engine may be used successfully over a wide range of land cover types, providing critical data for future monitoring of such threats. The impact of forest fires can be severe, leading to the loss of biodiversity, damage to ecosystems, and threats to human settlements.

Groundwater quality assessment is crucial for sustainable water resource management and public health protection. This study evaluated the Water Quality Index (WQI) of groundwater in the southern part of Gujarat focusing on the Valsad District. Groundwater in this region occurs in porous, unconsolidated formations and fracture formations, both under groundwater table conditions and confined aquifers. Various parameters including Nitrate (NO₃¯), pH, Calcium (Ca²⁺), Electrical Conductivity (EC), Total Hardness (TH), Magnesium (Mg²⁺), Total Dissolved Solids (TDS), Potassium (K⁺), Sodium (Na⁺), Sulphate (SO₄²⁻), Chloride (Cl¯), Bicarbonate (HCO₃¯), Silicate (SiO₄⁴⁻), and Fluoride (F¯) were analyzed to assess groundwater quality. Results indicate that most of the parameters fell within acceptable permissible limits for drinking water, except for Muli and Nanaponda villages with the parameters Cl¯, EC, and TDS exceeding the permissible limit. The WQI analysis revealed that 31.25% of water samples from different villages were found in the excellent category (WQI < 25). About 68.75% of samples from 16 villages were classified as good quality category (WQI ∼ 25–50). Overall, the WQI ranged from 14.20 to 41.98, suggesting that groundwater in the Valsad district is suitable for drinking. The Piper diagram analysis of water samples collected from the field indicated unique geochemical compositions and good water. The diagram revealed that the Ca²⁺ was the predominant cation, followed by K⁺, Na⁺, and Mg²⁺. Among the anions, the HCO₃⁻ showed the highest concentrations, followed by SO₄²⁻, NO₃⁻, and Cl⁻. This dominance pattern demonstrated that the weathering of minerals significantly influenced the groundwater. This study recommends remediation for areas with reduced water quality to address geogenic and anthropogenic contamination.