Faculty Publications

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Author: search.filters.author.Apoorva, K.V.Has files: No

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    AN Integrated Analysis and Forecasting of Wildfires in the Nallamala Hills, India
    (Institute of Electrical and Electronics Engineers Inc., 2023) Kundapura, S.; Vishnu Vardhan, M.; Apoorva, K.V.
    Wildfires threaten ecosystems, human lives, and infrastructure, necessitating effective detection and prediction methods. In this study, an in-depth analysis of wildfire detection and forecasting is carried out over the Nallamala hills, which stretch across the states of Telangana and Andhra Pradesh. Our approach comprises three significant steps: Active fire analysis, pre-fire analysis, and post-fire analysis. Pre-fire maps were created using the Normalised Difference Vegetation Index (NDVI) during the pre-fire analysis, which involved time series analysis of significant components. For active fire analysis, the first dataset is created by using satellite imagery and its derived products. A dataset is used to train the five different machine-learning models for prediction. Among these models, the Random Forest classifier outperformed the remaining four models (Support vector Classifier, Gradient Boosting Classifier, Logistic Regression, and K-means algorithms) in accurately detecting and predicting active fires. This step enabled real-Time monitoring and prioritisation of firefighting efforts. The burnt area calculation uses the Normalised Burn Ratio (NBR) in the post-fire analysis. The analysis implemented post-fire rehabilitation and restoration efforts, giving essential information on the scope and severity of fire damage. The comprehensive study of all wildfires will provide a detailed picture of what occurred in the past (Timeseries), present (Prediction models), and future (Pre-fire maps), allowing people and government agencies to take precautions against future wildfires. © 2023 IEEE.
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    Feature Elimination and Comparative Assessment of Machine Learning Algorithms for Flood Susceptibility Mapping in Kerala, India
    (Institute of Electrical and Electronics Engineers Inc., 2023) Kundapura, S.; Aditya, B.; Apoorva, K.V.
    Floods are a catastrophic phenomenon with far-reaching consequences for infrastructure, the economy, and human lives, profoundly impacting regions globally. This study assesses flood susceptibility in four districts of Kerala: Ernakulam, Idukki, Kottayam, and Alappuzha. For the 2018 storm that caused flooding by Cyclone Ockhi, a flood map for the area was produced using Sentinel 1 satellite data in Google Earth Engine environment. The resulting map served as the foundation for further analysis. Based on the literature review, 16 potential flood causative factors were identified and incorporated into spatial maps in the Geographic Information System (GIS) environment. Analysis of the flood dataset was performed using Machine Learning (ML) algorithms, namely, Random Forest (RF), Decision Tree (DT), Gradient Boosting Machine (GBM), and XG Boost (XGB). Grid search was employed to identify the optimal hyperparameters for each algorithm, ensuring improved performance. Recursive Feature Elimination (RFE) was subsequently applied to select the most influential variables, resulting in a refined dataset. The chosen factors' feature importance scores were obtained, which were used to create the flood susceptibility map using the four ML models in a GIS environment. Evaluation metrics such as F1 score, accuracy, precision, recall, and ROC-AUC score were computed for each model, providing insights into the effectiveness of each algorithm in predicting the flood occurrence. The resulting flood susceptibility map for the best-performing ML model visually represents the varying levels of flood risk in the study area. © 2023 IEEE.
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    Synthesis and application of iron nanoparticles from scrap metal for triclosan degradation in water via Fenton and Sono-Fenton oxidation
    (Elsevier B.V., 2025) Bhaskar, S.; Apoorva, K.V.; Ashraf, S.; Athul Devan, T.
    Triclosan, a widely used antimicrobial agent in water known for its adverse effects was treated with Fenton and Sono Fenton oxidation. This study investigates the extraction of iron from scrap metal utilising acid digestion techniques and explores the production of iron nanoparticles for use as catalysts in Fenton and Sono-Fenton oxidation processes to degrade. Iron nanoparticles (FeSNPs) were synthesised using Mangifera indica plant extracts and characterized using scanning electron microscopy, X-ray diffraction, and electron diffusion spectroscopic spectrophotometry. Fenton and Sono-Fenton oxidation experiments were conducted with varying ratios of H2O2 to FeSNPs, and the maximum removal of triclosan was 59 % and 73 % for Fenton and Sono-Fenton oxidation, respectively, with rate constants of 0.0067 min?1 and 0.0210 min?1. The oxidation–reduction potential and pH played crucial roles in the efficiency of the oxidation processes. The total iron leached from the nanoparticles was 74.0 mg/L and 186.7 mg/L for Fenton and Sono-Fenton oxidation, respectively. At pH 3, the most effective ratio for triclosan removal by conventional Fenton oxidation was 1:4, whereas for Sono-Fenton oxidation it was 1:5. Sono-Fenton oxidation enhanced the production of hydroxyl radicals, resulting in a 14 % higher removal efficiency and a shorter treatment time compared to classical Fenton oxidation. Catalyst reusability studies demonstrated that Sono-Fenton oxidation maintained higher efficiency levels throughout multiple reuse cycles compared to Fenton oxidation. The results indicate the potential of utilizing iron nanoparticles derived from scrap metal as effective catalysts for the degradation of triclosan in water treatment applications. To recommend the most efficient Fenton oxidation method at an industrial scale, the study should be extended to evaluate the potential of these nanoparticles in both photo-Fenton and dark Fenton oxidation processes. © 2025 The Authors
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    Impact of sulfate supplement on bioleaching of iron from fly ash residue using isolated Acidithiobacillus ferrooxidans strain: A Box-Behnken process optimisation
    (Korean Society of Environmental Engineers, 2025) Bhaskar, S.; Apoorva, K.V.; Ashraf, S.; Shruthi, R.; Manoj, A.
    Fly ash, a residue from coal combustion contains significant iron content (10-40%), has potential applications in various fields. Present study investigated the impact of sulfate on bioleaching of iron from fly ash, using a novel Acidithiobacillus ferrooxidans strain. Iron dissolution obtained was 95.5 mg/L with 100 rpm shake flask speed, 3% pulp density, pH 3.0, and 5.5 g/L sulphate supplement, compared to 74.5 mg/L without sulphate over 15 days. The study employed Box-Behnken design for Design of Experiments. Variables ranged from 50 rpm – 150 rpm for shake flask speed, 2.5 – 3.5 for pH, 1% – 5% for pulp density, and 1.0 g/L – 10 g/L for sulfate concentration. In the experiment with sulfate supplement, the concentration of sulfate was treated as a variable parameter, as opposed to the pulp density, while taking into account other relevant characteristics. Iron dissolution was taken as a response. Pulp density and sulfate concentration significantly affected iron dissolution. A quadratic regression model was fit and an ANOVA was performed. According to the model, sulfate concentration has a positive linear influence with sulfate supplement, while for no sulfate supplement, shake flask speed and pulp density have a positive effect on the bioleaching of iron from fly ash. © 2025 Korean Society of Environmental Engineers.
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    Fenton and Sono-Fenton degradation of selective herbicides in water using bioleached Fe-Cu bimetallic nanoparticles (BFe-CuNPs)
    (Springer Science and Business Media Deutschland GmbH, 2025) Bhaskar, S.; Ashraf, S.; Apoorva, K.V.
    Bimetallic nanoparticles offer an innovative solution for treating water and wastewater systems using a heterogeneous Fenton-like process. This study investigates the synthesis of iron-copper bimetallic nanoparticles using bioleached iron and copper as precursors and evaluates their performance in the degradation of selective herbicides ametryn and dicamba by Fenton’s oxidation and Sono-Fenton’s oxidation. Bioleaching experiments were conducted separately for iron and copper leaching from laterite ore and chalcocite ore, respectively, using isolated Acidithiobacillus ferrooxidans bacterial strain. Acidothiobacillus ferrooxidans, a chemolithoautotrophic bacterium oxidizes ferrous iron and reduced sulfur compounds, generating sulfuric acid playing a crucial role in the solubilization of iron from laterite ore and copper from chalcocite. In the case of laterite ore, the bacterium’s iron oxidation activity helps release iron from the mineral matrix, making it more accessible for extraction. Similarly, with chalcocite, A. ferrooxidans facilitates the dissolution of copper from chalcocite (Cu2S) through its sulfur-oxidizing capabilities. The synthesized bimetallic nanoparticles were characterized using various techniques, including SEM, XRD, EDS, FTIR, and BET analysis. Fenton’s oxidation and Sono-Fenton’s oxidation of mixture ametryn and dicamba in a solution catalyzed by the bioleached Fe-Cu bimetallic nanoparticles were found to be effective, with ametryn degradation reaching 96.4% and 94.2%, and dicamba degradation reaching 98.1% and 99.3%, respectively, at a catalyst loading of 0.5 g/L. The removal efficiency increased with increasing catalyst loading up to 0.5 g/L and increasing H2O2 dosage up to 500 mg/L. Sono-Fenton’s oxidation led to higher COD reduction of 78.41% compared to conventional Fenton oxidation 70.42% with a reaction rate of 0.039/Min and 0.0053/Min respectively. The study demonstrates the potential of bioleached iron-copper bimetallic nanoparticles as a sustainable replacement for commercial catalysts in the oxidative degradation of herbicides. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.