Natural Breaks Classification Research Articles

A method for evaluating ecological quality levels and trends using natural evolution time series.

Ecological quality assessment serves as a valuable tool for impartially measuring the effects of development and utilization activities on ecosystems, forming the bedrock of ecological governance. The ecological quality evaluation method, relying on a reference frame, takes into consideration the comparability of ecological quality, thus guaranteeing the presence of external benchmarks for the assessment results. However, present research on absolute assessment of ecological quality is limited, as the majority of existing studies focus on large-scale regions, and overlooking regional differences in smaller-scale regions to a certain extent. Therefore, to achieve a comprehensive, convenient, objective, and accurate ecological quality evaluation, this study established a model for regional ecological quality assessment through remote sensing. This model was based on the analysis of natural evolutionary features and was applied to assess the ecological quality in the largest ecosystem within the arid area of the Ningxia Hui Autonomous Region (NHAR) - the grassland ecosystem. For this assessment, the Modified soil-adjusted vegetation index (MSAVI), the wetness components of tasseled cap transformations (WET), and net primary productivity (NPP) were selected as indicators of soil and water conservation capacity, water source conservation capacity, and carbon sequestration capacity, respectively. The evaluation units were established according to the geomorphic types, and the improved derivative dynamic time warping (DDTW) algorithm was used to compare the conformity between the time series of evaluation units and natural evolution time series, pixel by pixel. The ecological quality was classified into five levels (levels 1, 2, 3, 4, and 5) using the natural breaks (Jenks) classification method. The Mann-Kendall trend test and the Sen's slope are used to analyze the trend of the conformity measurement results of the ecological quality each year at a significance level of 95%. The main results are as follows: (1) The indicators selected in the study are mostly independent of each other within each evaluation unit, with a correlation (0.552) observed only between MSAVI and WET in plain areas. (2) Spatial heterogeneity exists in the natural evolution characteristics, with significant variations in the similarity between the reference frames of different evaluation units. There is no strict rule linking the similarity results to the elevation of the terrain. (3) From the perspective of the evolutionary process and intensity, the soil and water conservation capacity, water source conservation capacity, and carbon sequestration capacity in the study area primarily fell within level 2. The advantageous level of each evaluation unit was also primarily at level 2. The overall trend of these three capacities demonstrated stability and remained constant. In summary, our assessment indicated that the overall quality of grassland ecosystems in the arid areas of the NHAR was relatively stable and superior. This method we employed has broad applicability and can serve as a general framework for ecological quality assessments. It can be adapted for ecological restoration assessments in other regions by fine-tuning regional parameters, offering valuable insights for regional ecological management.

Biomass Resources and Emission Reduction Potential of Agricultural and Livestock Residues in Mainland China from 2013 to 2022

Controlling carbon emissions is a global goal, and China is actively implementing carbon reduction measures. As a major agricultural nation, China has considerable potential for developing agricultural residues as renewable and environmentally friendly biomass energy. In this study, we obtained data on crop yields, crop-to-grain ratios, and livestock excretion coefficients to calculate the biomass resources of agricultural and livestock residues in Chinese provinces from 2013 to 2022. Crop residue biomass resources showed a distribution pattern with higher levels in the north than in the south and the east than in the west. Henan and Heilongjiang provinces consistently had the highest resource levels, exceeding 35 million tons annually for 10 years. The biomass resources from livestock residues were relatively abundant in Sichuan, Henan, Yunnan, Shandong, Hunan, and Inner Mongolia. Inner Mongolia, Sichuan, Shandong, and Henan had the greatest potential for CO2 emission reductions, primarily located in regions abundant in biomass resources and with high traditional energy consumption levels. ArcGIS was used to apply natural break classification to categorize the potential for emission reductions from agricultural and livestock residues across China from 2013 to 2022 into five classes. Based on factors such as crop planting area and livestock numbers, the spatiotemporal distribution of factors influencing the quantity of biomass resources was examined using Geographically and Temporally Weighted Regression. A tailored and integrated approach should be used for biomass, and the development of biomass energy should be promoted through policy support and technological innovation.

The Role of Socioeconomic and Female Indicators on Infant Mortality in West Nusa Tenggara: A Panel VECM Analysis

West Nusa Tenggara Province has an infant mortality rate that surpasses the national average. Additionally, it is also characterized as having a high Gini ratio and gender inequality index. Therefore, this study aims to examine the differences in classification among different regions, the long-term and short-term impact, and the causal relationship between socio-economic factors and female indicators in relation to infant mortality. This study used the co-integration method of the panel VECM and applied the natural breaks (Jenks) classification method based on panel data from 10 regencies/cities in West Nusa Tenggara Province between 2012 and 2022. This study discovered two instances of co-integration where the life expectancy of women was found to have a negative impact, while the percentage of women working full-time was found to have a positive impact on the long-term infant mortality rate. Infant mortality rates in the short term showed a significant relationship with the cointegration coefficient, mean years of schooling of women, life expectancy of women, and percentage of women working full-time. There is a direct causal relationship between the mean years of schooling of women and the percentage of people living in poverty and the infant mortality rate. This study is expected to serve as a basis to guide the Government of West Nusa Tenggara Province in promoting equity in education, equal job opportunities, adequate healthcare facilities, and increased investment to decrease infant mortality.

Characteristics of Coupling between Multi-scale Community Living Circles Public Service Facilities Supply and Spatial Centers

In response to changing demand structures due to slower population growth and economic shifts, this study examines the coordination between public service facilities and urban spatial centers, crucial for people-centered community development and spatial agglomeration laws. Utilizing crystal-growth algorithms and spatial autocorrelation, the research algorithmically interprets spatial centers, and conducts kernel density and natural break classification analyses on public service facilities. The case of Guangzhou's central urban area reveals: 1) The spatial centers are in Liwan, Yuexiu, and Haizhu districts; 2) Centers are dispersed within a five-minute range but cluster over thirty minutes; 3) Analysis of coupling at different scales highlights areas needing more public service facilities. This methodology elucidates the relationship between spatial elements and structure, enriching community living circle theory and contributing to urban high-quality development.

Enhancing reliability in climate projections: A novel approach for selecting global climate models

Reliable climate projections are essential for informed decision-making in the context of climate change. However, selecting suitable Global Climate Models (GCMs) for such projections remains challenging, especially when computational resources are limited. This study introduced an innovative approach to GCM selection, emphasizing the identification of models that exhibit consistency in projecting future climate changes and skill in representing current climate conditions, including average climate, seasonal patterns, and climatic variations. GCM performance in simulating these critical properties was evaluated for three major climatic variables: rainfall, maximum temperature, and minimum temperature. This assessment results in a structured 3 × 3 performance matrix for each GCM, encapsulating its ability to capture these essential climate features. The matrix distances, quantifying the disparities between each GCM's performance matrix and the ideal reference matrix, were used to collectively represent the overall model performance. Finally, GCMs were ranked based on these differences using the Jenks natural break classification method, a robust statistical technique, to aid in identifying the top-performing GCMs, making them ideal candidates for ensemble model construction. The newly developed method was tested by applying it to select GCMs for Nigeria from a pool of 19 CMIP6 GCMs. The results indicate that 15 of 19 GCMs consistently projected future climate within a 95% confidence interval. Further evaluation of matrix distances and natural classification reveals a subset of GCMs, ACCESS.ESM1.5, BCC.CSM2.MR, CMCC.ESM2 and MRI.ESM2.0 are the most suitable choice for simulating Nigeria's climate. The multimodel ensemble mean of the selected GCMs projected a notable increase in rainfall by 10–40% over most of the country and the maximum and minimum temperatures by 1.0–3.5 °C and 0.5–4.0 °C across the country. The method introduced in this study can be an effective tool for GCM selection to enhance climate projection reliability.

Mapping the potential spatial distribution for offshore finfish aquaculture in China under climate change

Offshore aquaculture plays an important role in alleviating the pressure of coastal aquaculture and promoting the sustainable development of fisheries. And changes in ocean conditions caused by climate change will have a huge impact on mariculture. Considering the conditions of climate change, this study took the main mariculture fishes in China as the research object, used a species distribution model (SDM) to predict the potential mariculture areas of each fish under the current and different future (2040–2050) climate scenarios, divided the mariculture areas into three potential levels by natural breaks classification method, and calculated the area of different levels potential areas of each fish through the grid number and grid unit area. Additionally, this paper compares the potential area variation of different fishes under different climate scenarios. The results indicate that: (1)In the current climate, the potential area of Yellowtail kingfish(Seriola lalandi) is the largest among seven fishes, estimated at 1,288,500 km2. And the number of varieties suitable for aquaculture in the East China Sea and the South China Sea is more than that in the Bohai and Yellow Seas. (2)Compared with the high potential area of current and future climate scenarios, Large yellow croaker(Larimichthys crocea) has the largest increase, Yellowtail kingfish(Seriola lalandi) decreases under all four future climate scenarios, and both Cobia(Rachycentron canadum) and Allied Kingfish(Seriola dumerili) has small changes. (3)In the future climate scenarios, the number of species potentially suitable for culturing increases significantly in the Bohai and Yellow Sea area, and in the South China Sea, the suitable areas for cultured Large yellow croaker(Larimichthys crocea), gold pompanos(Trachinotus ovatus), Cobia(Rachycentron canadum) and Allied Kingfish(Seriola dumerili) decrease significantly. Overall, the potential areas of each fish show a northward trend.

Identification of the Tropopause Using the Jenks Natural Breaks Classification From 205-MHz Stratosphere–Troposphere Wind Profiler Radar

Identification of the Tropopause Using the Jenks Natural Breaks Classification From 205-MHz Stratosphere–Troposphere Wind Profiler Radar

The Impact of Climate Change on the City of Padang, Indonesia

The impact of global warming is climate change which affects elements of society. This condition causes a decrease in the level of community welfare and increases the level of community vulnerability. Some climate change impacts are floods, droughts, landslides, and shoreline changes. In this study, we will focus on landslides. Landslides are among the most dangerous natural disasters that often occur in mountainous areas, especially during the rainy season. Various factors influence events involving landslides. This study aims to utilize GIS to identify landslide-prone areas in Padang. The method used in this study is the Zuidam and Concelado criteria overlay method for the level of landslide hazard and the broken method (jenks). The natural break (jenks) classification method reduces within-class variation and maximizes between-class variation. This study shows that the level of landslide vulnerability in Padang City is low, with a total area of 288854.38173 ha with a percentage of 42.21%. We need to consider more factors and experiment with training and validating data in more detail to gain insight into the physical contributions of the factors to landslide occurrences.

Appraisal of irrigation land suitability of semi-arid climatic zone of Rajasthan, India

Abstract Planning how to use the land resources that are available for irrigation is crucial for reducing the problem of food security. Analytical hierarchy process methodologies were used to undertake an analysis of the appropriateness of land using the GIS approach. Each parameter's weight was assessed using a 6 × 6 pairwise comparison matrix because six parameters, including slope, distance to the nearest road, land use land cover, population density, soil texture and rainfall deficit, are taken into account. After all, the weighted overlay approach of the GIS application was used to create the ultimate land suitability map for irrigation. Using the natural break classification approach, the final map was divided into land suitability classes. Of the entire region, 12.9% fell into the highly suitable (S1) class, 85.14% fell into the moderately suitable (S2) class, and 2.57% fell into the least suitable (S3) class. The finding of the study will play a significant role in performing irrigation taking into account the input parameters and ultimately defining suitable land classes and lands that may be used in accordance with current irrigation technology.

Application of Compound Terrain Factor LSW in Vegetation Cover Evaluation

Aiming at the limited degree of revealing vegetation growth pattern by simple topographic factors, it is essential to find an index that can explain the vegetation cover mechanism to a greater extent. By introducing a certainty factor into vegetation cover evaluation, LSW, LS, topographic wetness index, and aspect were also chosen to carry out control group experiments. The grid digital elevation model of 10 disaster-stricken counties (cities) in Sichuan Province was used as the basis to obtain LSW, while vegetation susceptibility levels were categorized using the natural breaks classification. The results of the multi-factor analysis demonstrated that the certainty factor corresponding to LSW climbed linearly from negative to positive values as the susceptibility level rose, indicating that it was highly correlated with vegetation cover and had an obvious advantage in revealing the vegetation growth potential. Statistically, the vegetation occurrence rate increased regularly with the improvement of the susceptibility level, in which the high and very high susceptibility zones corresponded to 83.66% and 89.95% of vegetation coverage respectively, suggesting a good consistency between the delineated high susceptibility areas and the actual vegetation cover. The findings proved that LSW has a substantial advantage in evaluating vegetation cover, with an evaluation model accuracy of 72.9%. It has been established that LSW will serve as a promising new model for assessing vegetation restoration and planning.

The Multi-Scale Spatial Heterogeneity of Ecosystem Services’ Supply–Demand Matching and Its Influencing Factors on Urban Green Space in China

As population growth and urbanization continue to accelerate, city dwellers are increasingly conscious of the demand for urban green space (UGS) and the ecosystem services (ESs) it provides. Great efforts are made for the supply of certain ESs in UGS. However, less is known about the residents’ preferences and the supply–demand matching of UGS types, as well as the various ESs it provides at different spatial scales. Given this, our research establishes a research framework to reveal the heterogeneity of USG types and the supply–demand matching degree (SDM) of ESs from municipal, provincial, and national spatial scales, and examines the correlation between the influencing factors and demands of residents for UGS. This study mainly used the Gini coefficient, the Lorenz curve, Z-scores, the Jenks natural breaks classification method, Pearson correlation analysis, and spatial analysis. The main findings are that (1) the Gini coefficients are 0.433 and 0.137 at the municipal and provincial scales, respectively, indicating that the supply of UGS is more unequal at the municipal scale than provincial scale; (2) the multi-scale demand for ESs between residents has no significant difference. At the provincial scale, the area with low demand is larger than that of high demand, while at the municipal scale, the contrary is the case; (3) the SDM was in a deficit at both the provincial and municipal scales. And as the scaling-up occurred, the spatial heterogeneity of the SDM decreased; (4) the number of influencing factors that significantly affected the UGS type and ESs grew as the scale increased. Among them, the impact of age and COVID-19 on three scales deserves attention. These results identify regions with deficits and surpluses in ESs provided by UGS in China at different scales. This research also advises that attention should be paid to the distribution of UGS between cities within provinces, and future UGS planning should focus on building regional green spaces to promote the well-being of an aging society. The findings in this study would offer insights for managers to improve UGS construction and urban forestry planning in the future.

Desertification simulation using wavelet and box-jenkins time series analysis based on TGSI and albedo remote sensing indices

Desertification has been listed as one of the most critical global environmental issues, posing a significant threat to life, particularly in arid and semiarid regions. Therefore, gaining a comprehensive understanding of the present and future desertification trends becomes imperative. This study employs a feature space model, which effectively captures land surface changes related to desertification, enabling the extraction of pertinent information. Subsequently, time series models are used to determine the most accurate desertification simulation. Twenty-one ETM + sensor images were utilized to calculate the Topsoil Grain Size (TGSI) and Albedo remotely sensed indexes. Constructing the Albedo-TGSI feature space, the Desertification Degree Index (DDI) was extracted for each year. Different levels of desertification were identified by applying a natural break classification on the DDI values, and corresponding break values were obtained. The representative desertification degree for each year was determined by calculating the average of the minimum and maximum break values, resulting in the generation of five distinct time series for five desertification degrees. Different ARIMA models and wavelet transforms were selected to simulate the various desertification degrees based on the analysis of autocorrelation and partial autocorrelation functions and trial and error, respectively. The most suitable ARIMA models with the lowest errors were identified as follows: ARIMA (1,0,7) for severe desertification, ARIMA (0,1,6) for high desertification, ARIMA (0,0,7) for moderate desertification, and ARIMA (3,0,6) for non-desertification degrees. Among the various wavelet transform families tested, the Symlet family proved to be the most effective, except for the low desertification degree. The following wavelet transforms yielded the best results for each degree of desertification: Symlet3 for severe desertification, Symlet7 for high desertification, Symlet7 for moderate desertification, Daubechies 5 (db5) for low desertification, and Symlet7 for non-desertification degree simulations, all exhibiting the minimum error rates.

ALLOCATION OF COHESION POLICY FUNDS IN RURAL AREAS IN POLAND

The article examines the scale of absorption and spatial distribution of European Union (EU) funds for the implementation of the Cohesion Policy (CP) in rural areas in Poland. The study covered all 2,173 rural and urban-rural municipalities in Poland in 2007-2018 during two financial perspectives of the EU: 2007-2013 and 2014-2020. The hypothesis was verified, i.e. municipalities located in provincial capitals functional urban areas (FUA) are characterized by a higher level of CP funds absorption than municipalities located in the rest of the country. Data for the study were obtained from the databases KSI-SIMIK 07-13 (2007-2013 perspective) and SL2014 (2014-2020 perspective). In the analyzed period, approximately PLN 500 billion was allocated in Poland from CP, of which 38% was to rural areas. Most funds were spent in rural areas on transport investments and infrastructure, while the least spent were on environmental protection and digitization projects. The spatial distribution of CP funds per capita has been presented using the Jenks Natural Breaks Classification optimization method. Three classes of municipalities were obtained according to the level of absorption: low (57% of communes), average (28% of communes), and high (15% of communes). The study confirmed that municipalities located in provincial capitals FUAs, despite the fact that they constitute 10% of the surveyed communes and 17% of the surveyed population, are characterized by 2.5 times higher absorption of CP funds per capita compared to other municipalities.

Geographic Information System Based Economic And Financial Risk Analysis: The Case Of Europe And Central Asia

The study has two purposes. The first purpose is, using the economic and financial risk factors determined by the International Country Risk Guide (ICRG) rating agency, to reclassify the scores that countries get from these factors with the Jenks Natural Breaks (JNB) classification technique and to compare countries by creating their thematic maps according to this classification. The second purpose of our study is to create economic and financial risk maps of countries by using the Geographical Weighted Regression (GWR) method, which is one of the Geographical Information System (GIS) analysis techniques, based on the economic and financial risk factors determined by the ICRG. Before the GWR analysis for the variables included in the research, Moran Index analysis was performed as the main measurement of spatial autocorrelation. As a result of the Moran analysis, it was determined that there was a statistically significant positive autocorrelation between the countries. In other words, it has been seen that the countries examined have spatial dependence on each other in terms of economic and financial risk. According to the results of the GWR analysis, risk maps of the examined countries were created and more dynamic, more meaningful or more sensitive, more specific and visually easier to understand results were revealed. And according to these results, it has been seen that the GWR technique can also be used in the fields of economy and finance. In addition, the study brought a different interdisciplinary perspective by bringing together the fields of economy, finance and geography.

Crime Risk Analysis of Tangible Cultural Heritage in China from a Spatial Perspective

Tangible cultural heritage is vulnerable to various risks, particularly those stemming from criminal activity. Through analyzing the distribution and flow of crime risks from a spatial perspective based on quantitative methods, risks can be better managed to contribute to the protection of cultural heritage. This paper explores and summarizes the spatial characteristics of crime risks from 2011 to 2019 in China. Firstly, the average nearest neighbor (ANN) and the Jenks Natural Breaks Classification method showed that the national key protected heritage sites (NPS) and crime risks exhibit clustering features in space, and most of the NPS were located in the middle and lower reaches of the Yangtze River and the Yellow River. Secondly, the economy has no impact on crime risks in the spatial statistical analysis. However, the population density, distribution of NPS, and tourism development influenced specific types of crime risks. Finally, Global Moran’s I was used to examine the strong sensitivity between crime risks and cultural relics protection policies. The quantitative results of this study can be applied to improve strategies for crime risk prevention and the effectiveness of heritage security policy formulation.

Settlement suitability analysis of a riverine floodplain in the perspective of GIS-based multicriteria decision analysis

Riverine floodplains are highly dynamic and the most vulnerable space on Earth particularly in flat alluvial plains of major river systems. Suitable site selection for sustainable human settlements in active floodplain areas is a critical task for decision-makers in terms of quality of lithology, ecologically fragile landmass, climate-induced hazards, immense population pressure, and environmental conservation issues. This research introduces a methodology for settlement suitability zone (SSZ) that employs GIS-based multi-criteria decision-making (MCDM) techniques. As a case study, an altered hydrological regime of the lower Ganga riparian zone was chosen to identify the SSZ as these areas are the most susceptible to riverine hazards. Twelve significant variables reflecting on topography, climate, landscape, and environment have been selected in the multi-criteria evaluation platform. The CRiteria Importance Through Intercriteria Correlation (CRITIC) method is adopted to specify the weights of the criteria and utilize an inverse distance-weighted (IDW) spatial interpolation technique to generate an SSZ map in a GIS environment. The study zone is spatially quantified into five categories, from unsuitable to high-suitable with a natural breaks (Jenks) classification method. Subsequently, the final results are validated through a receiver operating characteristics (ROC) curve using randomly selected 56 hazard-exposed location points. The outcome revealed that 8.45% of the riparian area falls under unsuitable, 21.87% under low-suitable, and 33.27% under moderate-suitable for locating human settlements. The generally suitable and high-suitable categories account for 36.40% of the total study zone. A spatial sensitivity analysis is also applied to gauge the influence of each parameter on the MCDM outcomes. The SSZ mapping results from this study can help local authorities to plan for sustainable settlement development in environmentally fragile areas.

Establishing a Disaster Risk Assessment System Based on Grid Data by Jenks Natural Breaks Classification

This paper proposes a disaster risk assessment system for the natural disaster with Jenks Natural Breaks Classification. The exposure indicators and vulnerability indicators are organized to disaster risk factors for the each receptors. The receptors evaluated the risk assessment are 9 and composed of people, industry, public facilities, educational and research facilities, medical and welfare facilities, amenity facilities, agriculture, livestock industry, and roads. All indicators are composed of 107,555 grid-based data having the codes of the region in the country. With Jenks natural breaks classification, the disaster risk assessment criteria for each receptors were presented per the disaster risk grade (Level 1 ~ Level 4). All of the criteria were evaluated with the grid-base data per the receptors and the evaluated results were presented by the maps of Korea. Through this study, the disaster risk assessment criteria can be used as the reference and forecasting for the natural disaster in Korea.

An improved spatio-temporal clustering method for extracting fire footprints based on MCD64A1 in the Daxing’anling Area of north-eastern China

Background Understanding the spatio-temporal dynamics associated with a wildfire event is essential for projecting a clear profile of its potential ecological influences. Aims To develop a reliable framework to extract fire footprints from MODIS-based burn products to facilitate the understanding of fire event evolution. Methods This study integrated the Jenks natural breaks classification method and the density-based spatial clustering of applications with noise (DBSCAN) algorithm to extract the fire footprints in Daxing’anling region of China between 2001 and 2006 from MCD64A1 burned area data. Key results The results showed that the fire footprints extracted by the model gained an overall accuracy of 80% in spatial and temporal domains after an intensive validation by using the historical fire records provided by the local agency. The agreement of burned area between the extracted fire patches and the historical fire records for those matched fire points was characterised by an overall determination coefficient R2 at 0.91. Conclusions The proposed framework serves as an efficient and convenient wildfire management tool for areas requiring large-scale and long-term wildfire monitoring. Implications The current framework can be used to create a reliable large-scale fire event database by providing an important alternative for the improvement of field investigation.

A new avenue to improve the performance of integrated modeling for flash flood susceptibility assessment: Applying cluster algorithms

Flash flood is one of the most severe natural disasters around the world, and has caused sizeable economic losses and countless death. Assessing flash flood susceptibility by hybrid models of statistical and machine learning methods is essential for flood mitigation strategies and disaster preparedness. Although classifying the flash flood conditioning factors becomes a crucial step before applying these hybrid models, their impact on the accuracy of integrated modeling is still unclear. Most previous studies used natural break classification (NBC) and quantile classification methods by default to conduct the classification, but more classification methods have not been tried. In this context, this study introduced three clustering algorithms of K-Means, Expectation Maximization, and ISOMaximum likelihood algorithm (ISOMax) into the classification of factors, and compared them to NBC and quantile classification. To test the impact of classification methods on integrated modeling, these classification results were applied into the construction of three hybrid models (i.e., the integrating of frequency ratio with support vector machines, random forest, and bayesian-regularization neural networks). Then, the accuracy of these hybrid models was evaluated by using ROC curves and statistical indicators. The classification results show that the clustering intervals in the same factor varied with classification algorithms. It can be found from the model performance evaluation results that different classification algorithms will lead to discrepancies in accuracy of integrated modeling. Compared to NBC, the ISOMax allows a better fitting and prediction ability of hybrid models in this study. The application of clustering algorithm provides a new perspective for improving the accuracy of integrated modeling.

Assessment of Flood Susceptibility Analysis Using Analytical Hierarchy Process (AHP) in Kota Belud Area, Sabah, Malaysia

This study aims to assess the flood susceptibility analysis using a Geographical Information System (GIS) based-heuristic analysis, namely the Analytical Hierarchy Process (AHP) model. Eight relevant physical parameters have been selected, namely, drainage density, drainage proximity, elevation, slope angle, slope curvature, land use, soil type, and topography wetness index. The relative importance of these factors has been compared in the pairwise matrix to gain weight values during the process of the Analytical Hierarchy Process (AHP). The flood susceptibility zones have been mapped according to their weightage value. Finally, the flood susceptibility map was prepared and classified into six classes as very low, low, moderate, high, and very high susceptibility using the natural break classification method. The accuracy of the flood susceptibility model was validated using the Area Under the Curve (AUC) analysis. The AUC for success rate was estimated at 82.13%.7.