DSpace Repository :: Browsing by Author "Tiwari, A"

Browsing by Author "Tiwari, A"

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Assessment and Mapping of Riverine Flood Susceptibility (RFS) in India through Coupled Multicriteria Decision Making Models and Geospatial Techniques
(2023-11) Kumar, R; Kumar, M; Tiwari, A
Abstract: Progressive environmental and climatic changes have significantly increased hydrometeo rological 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.
Covering assisted intuitionistic fuzzy bi‑selection technique for data reduction and its applications
(2024) Saini, R; Tiwari, A; Nath, A; Singh, P; Maurya, S; Shah, M
The dimension and size of data is growing rapidly with the extensive applications of computer science and lab based engineering in daily life. Due to availability of vagueness, later uncertainty, redundancy, irrelevancy, and noise, which imposes concerns in building efective learning models. Fuzzy rough set and its extensions have been applied to deal with these issues by various data reduction approaches. However, construction of a model that can cope with all these issues simultaneously is always a challenging task. None of the studies till date has addressed all these issues simultaneously. This paper investigates a method based on the notions of intuitionistic fuzzy (IF) and rough sets to avoid these obstacles simultaneously by putting forward an interesting data reduction technique. To accomplish this task, frstly, a novel IF similarity relation is addressed. Secondly, we establish an IF rough set model on the basis of this similarity relation. Thirdly, an IF granular structure is presented by using the established similarity relation and the lower approximation. Next, the mathematical theorems are used to validate the proposed notions. Then, the importance-degree of the IF granules is employed for redundant size elimination. Further, signifcance-degree-preserved dimensionality reduction is discussed. Hence, simultaneous instance and feature selection for large volume of high-dimensional datasets can be performed to eliminate redundancy and irrelevancy in both dimension and size, where vagueness and later uncertainty are handled with rough and IF sets respectively, whilst noise is tackled with IF granular structure. Thereafter, a comprehensive experiment is carried out over the benchmark datasets to demonstrate the efectiveness of simultaneous feature and data point selection methods. Finally, our proposed methodology aided framework is discussed to enhance the regression performance for IC50 of Antiviral Peptides.
Groundwater Quality assessment for Drinking Purpose using Traditional and Fuzzy-GIS based Water Quality Index in Gurugram District of Haryana, India
(2023-05) Tiwari, A; Kumar, M; Thakur, S
Primarily groundwater is consumed for the drinking and irrigation purpose. However, due to increasing anthropogenic activities, its quality and quantity have substantially declined over time. The focus of this study is to evaluate the pre-monsoonal groundwater quality and its spatial variability for drinking purposes in the Gurugram, Haryana, India. Ground Water Quality Index (GWQI) developed on the basis of the Geographical Information System is effective in the assessment of groundwater quality and its spatial variability, but it is unable to account for uncertainties related to environmental problems. Thus, a Hybrid Fuzzy-GIS based Water Quality Index (FGQI) has been proposed for the assessment of groundwater quality. The study conducted factor analysis to identify the prime factors responsible for groundwater contamination and collected pre-monsoonal groundwater samples through primary sampling. The groundwater quality was assessed based on eight hydro geochemical parameters (pH, TDS, Calcium, Chloride, Sulphate, Fluoride, Potassium, and Sodium). The spatial interpolation of each parameter was performed using appropriate techniques, selected based on a normality test. The guidelines of the World Health Organization (WHO) and Bureau of Indian Standard (BIS) were incorporated in the development of GWQI and FGQI, respectively. Correlation analysis was performed to determine the best fuzzy overlay technique for FGQI, and the Fuzzy GAMMA technique with gamma equal to 0.9 was selected. Finally, the GWQI and FGQI were classified into three classes: unsuitable, moderate suitable, and suitable using "natural break." A higher index indicates a higher water quality. The results show that the groundwater in the central part of Gurugram is suitable for drinking, while it is not suitable in the extreme north, south-east, and western regions. In conclusion, this study finds that FGQI effectively assesses the groundwater quality in the region better than GWQI.
Groundwater Quality assessment for Drinking Purpose using Traditional and Fuzzy-GIS based Water Quality Index in Gurugram District of Haryana, India
(2023-05) Tiwari, A; Kumar, M; Thakur, S
Primarily groundwater is consumed for the drinking and irrigation purpose. However, due to increasing anthropogenic activities, its quality and quantity have substantially declined over time. The focus of this study is to evaluate the pre-monsoonal groundwater quality and its spatial variability for drinking purposes in the Gurugram, Haryana, India. Ground Water Quality Index (GWQI) developed on the basis of the Geographical Information System is effective in the assessment of groundwater quality and its spatial variability, but it is unable to account for uncertainties related to environmental problems. Thus, a Hybrid Fuzzy-GIS based Water Quality Index (FGQI) has been proposed for the assessment of groundwater quality. The study conducted factor analysis to identify the prime factors responsible for groundwater contamination and collected pre-monsoonal groundwater samples through primary sampling. The groundwater quality was assessed based on eight hydro geochemical parameters (pH, TDS, Calcium, Chloride, Sulphate, Fluoride, Potassium, and Sodium). The spatial interpolation of each parameter was performed using appropriate techniques, selected based on a normality test. The guidelines of the World Health Organization (WHO) and Bureau of Indian Standard (BIS) were incorporated in the development of GWQI and FGQI, respectively. Correlation analysis was performed to determine the best fuzzy overlay technique for FGQI, and the Fuzzy GAMMA technique with gamma equal to 0.9 was selected. Finally, the GWQI and FGQI were classified into three classes: unsuitable, moderate suitable, and suitable using "natural break." A higher index indicates a higher water quality. The results show that the groundwater in the central part of Gurugram is suitable for drinking, while it is not suitable in the extreme north, south-east, and western regions. In conclusion, this study finds that FGQI effectively assesses the groundwater quality in the region better than GWQI.
Hybrid Approach of Cotton Disease Detection for Enhanced Crop Health and Yield
(2024-07) Kumar, R; Kumar, A; Bhatia, K; Nisar, K; Chouhan, S; Maratha, P; Tiwari, A
The well-being of cotton crops is of utmost importance for maintaining agricultural productivity, and the early detection of diseases plays a critical role in achieving this objective. This study introduces a comprehensive approach for creating a machine learning-based system capable of identifying diseases in cotton plants through the analysis of leaf images. The research encompasses stages such as acquiring the dataset, pre-processing the data, training the model, developing an ensemble model, evaluating the models, and analyzing the results. Several machine-learning models are trained and evaluated to determine how well they can classify cotton leaves as "Healthy" or "Diseased." These models include Random Forest, Support Vector Machine (SVM), Multi-Class SVM, and an Ensemble model. This investigation yields a practical and visually informative system for disease detection, which can contribute to disease prevention, thereby enhancing both crop yield and quality. This work underscores the significance of continuous improvement by periodically updating the models and explores the potential of advanced techniques such as deep learning.
Hybrid similarity relation based mutual information for feature selection in intuitionistic fuzzy rough framework and its applications
(2024) Tiwari, A; Saini, R; Nath, A; Singh, P; Shah, M
Fuzzy rough entropy established in the notion of fuzzy rough set theory, which has been efectively and efciently applied for feature selection to handle the uncertainty in real-valued datasets. Further, Fuzzy rough mutual information has been presented by integrating information entropy with fuzzy rough set to measure the importance of features. However, none of the methods till date can handle noise, uncertainty and vagueness simultaneously due to both judgement and identifcation, which lead to degrade the overall performances of the learning algorithms with the increment in the number of mixed valued conditional features. In the current study, these issues are tackled by presenting a novel intuitionistic fuzzy (IF) assisted mutual information concept along with IF granular structure. Initially, a hybrid IF similarity relation is introduced. Based on this relation, an IF granular structure is introduced. Then, IF rough conditional and joint entropies are established. Further, mutual information based on these concepts are discussed. Next, mathematical theorems are proved to demonstrate the validity of the given notions. Thereafter, signifcance of the features subset is computed by using this mutual information, and corresponding feature selection is suggested to delete the irrelevant and redundant features. The current approach efectively handles noise and subsequent uncertainty in both nominal and mixed data (including both nominal and category variables). Moreover, comprehensive experimental performances are evaluated on real-valued benchmark datasets to demonstrate the practical validation and efectiveness of the addressed technique. Finally, an application of the proposed method is exhibited to improve the prediction of phospholipidosis positive molecules. RF(h2o) produces the most efective results till date based on our proposed methodology with sensitivity, accuracy, specifcity, MCC, and AUC of 86.7%, 90.1%, 93.0% , 0.808, and 0.922 respectively.
Integrated Spatial Analysis of Forest Fire Susceptibility in the Indian Western Himalayas (IWH) Using Remote Sensing and GIS-Based Fuzzy AHP Approach
(2023-09) Pragya; Kumar, M; Tiwari, A; Majid, SI; Bhadwal, S
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.
Integrated Spatial Analysis of Forest Fire Susceptibility in the Indian Western Himalayas (IWH) Using Remote Sensing and GIS-Based Fuzzy AHP Approach
(2023-09) Pragya; Kumar, M; Tiwari, A
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.

Browsing by Author "Tiwari, A"

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