Shannon Entropy Model Research Articles

Application of decision support system/remote sensing/GIS techniques in groundwater recharge assessment

ABSTRACT Managing water resources and storing water through the identification of groundwater recharge zones (GWRZs) are critical to water security in Egypt. Decision-support systems (DSSs), remote sensing, and GIS techniques have yielded significant data for water resources modeling. The geologic, geomorphic, climatic, and hydrologic features of the Toshka area, Western Lake Nasser, Egypt, have been generated by data from Shuttle Radar Topography Mission, Climatic Research Unit gridded Time Series, Sentinel 2 time series, and Landsat-8 OLI. Fifteen GIS thematic maps have been ranked and normalized using five DSS techniques: Analytical Hierarchy Process (AHP), Fuzzy Analytical Hierarchy process (FAHP), Frequency Ratio (FR), Shannon Entropy (SE), and Multi-Influencing Factor (MIF). To ensure the computational usefulness of these models, GWRZs have been extracted and compared. The outcomes showed that 83, 87.5, 99.1, 99.1, and 87.5% of the existing wells are in high to extreme GWRZs for AHP, FAHP, FR, SE, and MIF, respectively. The receiver operating characteristic curve (ROC) was used to assess the effectiveness of these models. It was found that the SE model had the highest predictive performance rates, as its ROC accuracy value was 91.1%, while the FR, AHP, FAHP, and MIF approaches had values of 91, 84.4, 81.9, and 89.9%, respectively.

Landslide Susceptibility Modelling of Central Highland Part of Chaliyar River Basin, Kerala, India with Integrated Algorithms of Frequency Ratio and Shannon Entropy

An integrated landslide susceptibility analysis is carried out for the central highland region of the Chaliyar River Basin in Kerala, India using bivariate statistical methods, namely the Frequency Ratio (FR) and Shannon Entropy (SE). The study addresses the complex nature of landslides, influenced by natural as well as anthropogenic factors, with specific focus on assessing the landslide likelihood of the study area. The methodology involves a systematic approach of collecting the inventory data, identifying various landslide causative factors and developing their corresponding thematic maps, spatial analysis of landslide occurrence and causative factors using GIS software and generation of Landslide Susceptibility Model (LSM) employing FR and SE algorithm, followed by model validation. Various causative factors considered for the study include slope angle, slope aspect, slope curvature, elevation, lithology, drainage density, landuse and landcover (LULC), Topographic Wetness Index (TWI) and Normalized Difference Vegetation Index (NDVI). The FR and SE algorithm enable the spatial classification of the study area into four landslide susceptibility categories namely Low, Moderate, High, and Very High. Validation of both the LSMs was carried out using Landslide Density Index (LDI) and Area Under the Curve (AUC) methods. LDI demonstrate a positive fit for both the models, which is indicative of reliability of the susceptibility predictions of the study area. A slightly higher AUC value of SE model is an indication of a high accuracy rate of SE model over FR model. This research brings out a robust methodology for predicting and identifying the landslide risks of the study area.The outcomes of this study will help in developing effective strategies to manage the landslide hazards in geologically vulnerable areas. Keywords: Landslide Susceptibility, Landslide Conditioning Factors, Frequency Ratio, Shannon Entropy, Landslide Density Index(LDI), Area Under the Curve (AUC)

Modeling of groundwater productivity in the Alfred Nzo District, South Africa, using relative frequency ratio and Shannon entropy models

Study regionAlfred Nzo District, Eastern Cape Province, South Africa. Study focusThe study aimed to identify groundwater potential zones in the Alfred Nzo District using advanced geographic information systems and remote sensing applications by integrating relative frequency ratio (RF) with predictor rate and Shannon entropy (SE) models. Seven influential factors that are thought to control groundwater availability in the Alfred Nzo District were considered to identify groundwater potential zones. These factors are drainage density, lineament density, rainfall, slope, land use and land cover, lithology, and soil class. The factors were weighted in the RF and SE approaches. About 70 % of boreholes were used as training datasets, and 30 % were used for validation. Groundwater potential zones were delineated by integrating all these factors and their corresponding weights in ArcGIS software. The accuracy of each model was evaluated using receiver operating characteristic curve and area under curve techniques. New hydrological insights for the regionFive groundwater potential zones were found in the research area, which includes very poor, poor, moderate, good, and very good, by RF and SE models. The results of the models revealed that the SE model, with a success rate of 79.80 %, performed better than the RF, with a success rate of 75.10 %. The results of this research could be helpful in adequately managing groundwater resources in the research area.

Analysis of urban sprawl dynamics using machine learning, CA-Markov chain, and the Shannon entropy model: a case study in Mbombela City, South Africa

Analysis of urban sprawl dynamics using machine learning, CA-Markov chain, and the Shannon entropy model: a case study in Mbombela City, South Africa

The entropy as a parameter of demographic dynamics: Case study of the population of Serbia

The aim of this article is to apply Shannon's entropy model in the study of demographic reproduction dynamics of the population in the Republic of Serbia from the regional aspect (NUTS 3 level) in the period from 1961 to 2021. Based on the values of absolute and relative entropy, the tendencies of demographic dynamics are determined, which is one of the most important parameters for building an optimal scheme of territorial organization and population distribution. In this context, explanations and conclusions were given about the homogeneity (or heterogeneity) of demographic dynamics and regional differentiation of demographic space in Serbia.

Urban Spatial Patterns in Banda Aceh City Using the Shannon's Entrophy Model

The rapid development of the city of Banda Aceh occurred in general in the suburbs. The impact is the occurrence of propagation which is a complex urban phenomenon and difficult to measure. Policy makers need a simple method to control and evaluate urban spread. This study integrates the Shannon's Entropy model with remote sensing and GIS to analyze spreading patterns in each sub-district in Banda Aceh City based on two aspects of distance, namely the distance to the city center and the road network to describe urban spreading patterns. The application of the Shannon's Entropy method in Banda Aceh City in 2010-2020 can be seen in the Relative Entropy Matrix which shows that the linear propagation pattern (Quadrant C) is more dominant in Banda Raya District, Baiturrahman District, Kuta Raja District, Kuta Alam District, Lueng District Bata, and the District of Syiah Kuala. Whereas the spreading pattern of Meuraxa District and Jaya Baru District is in quadrant B, namely the elongated (Radial) type of propagation pattern, but in 2020 Jaya Baru District there has been a change in the propagation pattern, namely the linear propagation pattern. In the Ulee Kareng sub-district, there was a pattern of propagation changes in 2020 which was in quadrant D, namely the leapfrog development pattern from the previous year, namely 2010 and 2015 in quadrant C with a linear propagation pattern. The spreading pattern of Banda Aceh City tends to lead to the East. This is mostly caused by the shape of the land which is very suitable to be used as a built-up area

Impact Assessment of Road Construction on Landslide Susceptibility in Mountainous Region using GIS-Based Statistical Modelling

Abstract Landslide susceptibility is crucial for effective hazard management, planning mitigation, and risk reduction strategies. The present study assessed the impact of road construction on landslide susceptibility for the Mandi district. 1723 landslides data for Mandi district was compiled from the various sources with eleven causative factors. The eleven landslide causative factors were selected from numerous hydrological, geological, and topographical factors. The landslide susceptibility analysis was carried out using three bivariate statistical models, namely, Frequency Ratio (FR), Certainty Factor (CF), and Shannon Entropy (SE). Two sets of Landslide Susceptibility Maps were generated to assess the impact of road construction and divided into five landslide susceptibility classes. The SE model had the highest prediction and validation accuracy of (83-86%). The percentage area in high and very high classes of landslide susceptibility increased by 2.67-4.17% due to road construction activities in the study area.

Urban growth trend analysis of proposed Greater Silchar City, India, using landscape metrics and Shannon entropy model

Urban growth trend analysis of proposed Greater Silchar City, India, using landscape metrics and Shannon entropy model

Groundwater vulnerability assessment for drinking water suitability using Fuzzy Shannon Entropy model in a semi-arid river basin

Groundwater protection against geogenic and anthropogenic diffuse pollutants are of preeminent importance for convoying groundwater-dependent hydro-aquatic ecosystems and safeguarding human health by securing a safe and sustainable water security plan. Mapping groundwater vulnerability to pollution represents a topical area of research where traditional models suffer from inherent prejudices. A novel approach to identify groundwater vulnerable zones is thus presented in this study by combining Fuzzy Shannon Entropy (FSE) with a decision support algorithm to replace DRASTIC, GOD and other related methods. We proposed a contemporary “FSE-GVI″ framework for assessing aquifer vulnerability, which for the first time expands and redesigns the seven original geological and hydrogeological parameters with geochemical and anthropogenic factors. Applying such technique weights of 13 geo-environmental and hydrogeological factors (slope, soil, pH, recharge), and anthropogenic factors (land use land cover, population density, groundwater based schemes for irrigation) has been optimized. The computed map has been categorized into very high (10.82%), high (23.20%), moderate (29.29%), low (25.02%) and very low (11.64%).The groundwater of the river basin is rated on a scale of excellent, good, poor, very poor and unfit for consumption. The model is compared to 74 groundwater quality data, and the findings demonstrate an excellent correlation between water quality index (r = 0.84), Total dissolve solids (70%), iron (72%), hardness (85%) and alkalinity levels (75%).The findings of the research reveals 27.3 % of the sampling sites (villages) under poor water quality and 23.21% of very high vulnerable zone which requires competent planning, development, managerial technique to safeguard groundwater resources.

Landslide susceptibility assessment Using Shannon Entropy Model in Folded Zagros, Case Study: Varansara basin

Landslide susceptibility assessment Using Shannon Entropy Model in Folded Zagros, Case Study: Varansara basin

Analysis of the spatial inequality in residential quality indicators: the 22 urban regions of Tehran Metropolis

Research objective: the present study seeks to assess and analyze the status of residential quality indicators in the 22 urban regions of the Tehran metropolis.
 Methodology: 79 residential quality variables are classified into the four indicators of physical, economic, ecological, and socio-cultural indices. Shannon entropy model, the coefficient of variation, TOPSIS, hotspot analysis, and Moran spatial autocorrelation were used for data analysis.
 Results: Results of the present study indicated a TOPSIS score of 0.58 for the integrated indicators which revealed region 1 had the best and region 15 had the worst status. In fact, a sort of divergence and heterogeneity rules residential quality indicators in Tehran urban regions, so that among the 22 urban regions of the city, around 4.5% were advantaged while 13.6% were relatively advantaged, 77.3% were semi- advantaged, and 4.5% were less advantaged. Results of statistical spatial analysis also indicate that the advantaged clusters were located in northern and northeastern parts of the city while the less advantaged and extremely less advantaged clusters were more focused in the southern and southeastern parts of the city, and residential quality indicators followed a cluster pattern.
 Conclusion: A spatial difference and inequality in residential quality index distribution were observed in the 22 urban regions of Tehran so that the regions located in the southern and southeastern parts of the city had an unfavorable status in terms of residential quality indicators while the northern and northeastern regions were more advantaged in this regard.

Spatio‐temporal analysis of land use/land cover change and urban growth dynamics of Silchar City, India using very high‐resolution satellite data and the Shannon entropy model

AbstractSilchar City and its surrounding areas have witnessed massive population growth in the last few decades due to local and migrated peoples, which led to the rapid transformation of its land use/land cover (LU/LC) pattern. The spatio‐temporal very high‐resolution satellite data (VHRS) and on‐screen visual interpretation techniques were used for detailed large‐scale LU/LC analysis. The LU/LC classification of the City has been prepared for 2005, 2011, and 2018 with five primary and a further 17 sub‐classes. In the present study use was made of the Census of India reports from 1991 to 2011, and the geometrical increase method was used to evaluate the ward‐level population, population density, and land requirements for residential purposes; these have been predicted from 2021 to 2051 with every 10 years interval. The ity's wards have been classified into five zones, and the Shannon entropy model has been applied to analyze the degree of randomness and trends of urban built‐up growth. The obtained results indicate that Silchar City is compact near the center, which follows the infill built‐up growth trend. However, as the distance increased from its center, the scattering with an infill growth pattern was observed. The accuracy assessment techniques are used to validate the results of the classification. The results of this study can provide detailed information for land‐use planners, researchers, policy‐decision makers, and municipal authorities.

MEASURING LAND USE CHANGES AND QUANTIFYING URBAN EXPANSION USING REMOTE SENSING AND GIS TECHNIQUES – A CASE STUDY OF QOM

Abstract. Rapid urban sprawl necessitates solid urban planning tactics, which requires assessing and quantifying the sustainable or unsustainable encroachment of urban settings towards the urban periphery. Like many cities in developing countries, Iranian cities have witnessed tremendous changes in recent decades. The process of urbanization following economic and social developments has caused the intractable and unrestrained growth of cities with a national and regional role. Remote sensing and geospatial information system technology give urban planners the appropriate and pertinent information they need to guarantee the sustainable management of urban environments. This study explored the changes in Qom metropolis, Iran during four time periods, from 1985, 2000, 2010, and 2021, and assessed how the city expanded using Shannon's entropy model and the Urban Expansion Intensity Index. In this study, Landsat satellite images from the selected years were employed, and three land use/land cover classification types, including agricultural, built-up, and others, were derived using the maximum likelihood classification approach. The relative Shannon’s entropy result for the study years (1985, 2000, 2010, and 2021) are 0.66, 0.68, 0.69, and 0.86 respectively, which demonstrate a dispersed expansion pattern, with the maximum value in 2021. Also, the Urban Expansion Intensity Index, with the values 0.33, 0.33, and 0.51 for three periods (1985–2000, 2000–2010, 2010–2021), indicates that the city's expansion rate was low-speed throughout the chosen periods, despite having reached its peak between 2010 and 2021.

Using Shannon Entropy to Improve the Identification of MP-SBM Models with Undesirable Output.

In the context of the COVID-19 global epidemic, it is particularly important to use limited medical resources to improve the systemic control of infectious diseases. There is a situation where a shortage of medical resources and an uneven distribution of resources in China exist. Therefore, it is important to have an accurate understanding of the current status of the healthcare system in China and to improve the efficiency of their infectious disease control methods. In this study, the MP-SBM-Shannon entropy model (modified panel slacks-based measure Shannon entropy model) was proposed and applied to measure the disposal efficiency of the medical institutions responding to public health emergencies (disposal efficiency) in China from 2012 to 2018. First, a P-SBM (panel slacks-based measure) model, with undesirable outputs based on panel data, is given in this paper. This model measures the efficiency of all DMUs based on the same technical frontier and can be used for the dynamic efficiency analysis of panel data. Then, the MP-SBM model is applied to solve the specific efficiency paradox of the P-SBM model caused by the objective data structure. Finally, based on the MP-SBM model, undesirable outputs are considered in the original efficiency matrix alignment combination for the deficiencies of the existing Shannon entropy-DEA model. The comparative analysis shows that the MP-SBM-Shannon model not only solves the problem of the efficiency paradox of the P-SBM model but also improves the MP-SBM model identification ability and provides a complete ranking with certain advantages. The results of the study show that the disposal efficiency of the medical institutions responding to public health emergencies in China shows an upward trend, but the average combined efficiency is less than 0.47. Therefore, there is still much room for improvement in the efficiency of infectious disease prevention and control in China. It is found that the staffing problem within the Center for Disease Control and the health supervision office are two stumbling blocks.

Evaluating Urban Vitality Based on Geospatial Big Data in Xiamen Island, China

Urban vitality generally refers to the ability to attract lively businesses and human activities, which largely depends on the urban built environment and human flows. Previous studies that applied the empirical approach to analyze urban vitality from the feature of the urban built environment and the behavior of residents are scarce. This study developed a novel framework for exploring urban vitality on the street scale with geographic big data in Xiamen Island—a livable city in southern China. We measured urban vitality and analyzed its characteristics using the Shannon entropy model based on four dimensions of GIS-based data on the point of interest density, taxi flow density, building density, and road density. We then apply the regression model to explore the relationship between land-use diversity and urban vitality. Results show that four dimensions are associated with urban vitality. Furthermore, the dimensions of the taxi flow density and the road density in low vitality blocks can be distinguishable from other blocks. Results showed a stronger relationship between land-use diversity and urban vitality. The urban expansion direction of Xiamen Island is from the old town around the Xiamen Port to the new district around Yundang Lake to form a continuous covered area, which significantly impacts urban vitality. The approach of this study not only measures urban vitality through human activity but also sheds light on guidelines for sustainable urban development, which can plan and policy in practice for cities in China and many other countries.

Evaluation of Shannon Entropy and Weights of Evidence Models in Landslide Susceptibility Mapping for the Pithoragarh District of Uttarakhand State, India

Landslide susceptibility mapping is considered a useful tool for planning, disaster management, and natural hazard mitigation of a region. Although there are different methods for predicting landslide susceptibility, the bivariate statistical analysis method is considered to be simple and popular. In this study, the main aim is to evaluate the performance of Shannon entropy (SE) and weights of evidence (WOE) statistical models in landslide susceptibility mapping of Pithoragarh district of Uttarakhand state, India. For this purpose, ten landslide affecting factors, namely, slope degree, aspect, curvature, elevation, land cover, slope forming materials, geomorphology (landforms), distance to rivers, distance to roads, and overburden depth were used for the development of landslide susceptibility maps using the SE and WOE methods. Data extracted from the Google Earth images, Aster Digital Elevation Model, and Geological Survey of India report were used for the construction and evaluation of landslide susceptibility models and maps. The landslide data of 91 locations were randomly divided into two parts in the ratio of 70 : 30 using GIS software that is 70% data was used for training the models and 30% data was used for testing and validating the models. Performance of the applied models was evaluated using area under the AUC (area under the curve) ROC (receiver operating characteristics) curve. Results indicated that the WOE model is having better accuracy (AUCWOE = 68.75%) than the SE model (AUCSE = 52.17%) in the development of landslide susceptibility maps. Hence, WOE model can be used for the development of accurate landslide susceptibility maps which can provide useful information to decision maker and policy planner in better development of landslide prone areas.

Formulation of Shannon entropy model averaging for groundwater level prediction using artificial intelligence models

Formulation of Shannon entropy model averaging for groundwater level prediction using artificial intelligence models

GIS-based landslide susceptibility mapping method and Shannon entropy model: a case study on Sakaleshapur Taluk, Western Ghats, Karnataka, India

GIS-based landslide susceptibility mapping method and Shannon entropy model: a case study on Sakaleshapur Taluk, Western Ghats, Karnataka, India

A Large-Scale Evaluation Of Shape-Aware Neighborhood Weights And Neighborhood Sizes

In this paper, we define and evaluate a weighting scheme for neighborhoods in point sets. Our weighting takes the shape of the geometry, i.e., the normal information, into account. This causes the obtained neighborhoods to be more reliable in the sense that connectivity also depends on the orientation of the point set. We utilize a sigmoid to define the weights based on the normal variation. For an evaluation of the weighting scheme, we turn to a Shannon entropy model for feature classification that can be proven to be non-degenerate for our family of weights. Based on this model, we evaluate our weighting terms on a large scale of both clean and real-world models. This evaluation provides results regarding the choice of optimal parameters within our weighting scheme. Furthermore, the large-scale evaluation also reveals that neighborhood sizes should not be fixed globally when processing models. Finally, we highlight the applicability of our weighting scheme within the application context of denoising.

Three decades of Indian power-sector reform:A critical assessment

The accumulated financial loss of the Indian state-owned electricity utilities (SOEUs) has crossed over USD 6.25 billion and 80% of the SOEUs are consistently incurring losses and hence financially unsustainable in long term. In this article, the impact of three decades of Indian policy reform towards reviving the techno-commercial performance of the SOEUs is critically assessed. To quantify the relative performance of SOEUs and bring out the reason behind the inefficiencies of individual SOEUs, data envelopment analysis improved by Shannon's entropy model is applied. The benchmark SOEUs are identified for each inefficient ones, and corrective policy measures are recommended.