Natural Breaks Classification Method Research Articles

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.

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.

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.

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.

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.

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.

A River Channel Extraction Method Based on a Digital Elevation Model Retrieved from Satellite Imagery

The river border positioning is an important part of river surveys, which is crucial for water conservation project development, water resource use, water disasters, river regime monitoring, and many other applications related to water resources. Currently, satellite images or field measurements are used to extract river channels. However, satellite images are insufficiently precise, and field measurement requires significant manpower and cost. In this paper, a new method for river channel extraction is proposed, which is based on the combination of Jenks natural breaks classification method and digital elevation model (DEM), and then the river channel range is complemented by using the water range monitored by GF-1(Gaofen-1 satellite) in flood season. The overall precision is greater than 85%, and the Kappa values achieve moderate stability (0.41–0.60). Using this method, the extraction of river range is practicable and achievable, and the higher the DEM resolution, the better the extraction result.

Flood Susceptibility Assessment (FSA) Using GIS-Based Frequency Ratio (FR) Model in Kota Belud, Sabah, Malaysia

The flood is one of the most devastating natural disasters to strike Sabah, Malaysia, especially in the Kota Belud region. The Flood Susceptibility Analysis (FSA) was described using bivariate statistical analysis (the Frequency Ratio model) based on a Geographical Information System (GIS). Field surveys and formal reports from local authorities in the study area created the flood inventory map. The training dataset for statistical analysis consisted of 100 flood locations inundated in 2017, while the validation dataset consisted of 54 flood locations from the 2016 flood report. Eight (8) parameters (elevation, slope curvature, slope angle, topography wetness index, drainage density, drainage proximity, land use, and soil type) were extracted from the database and then converted into a raster format with a cell size of 5m x 5m. Finally, using the natural break classification method, the FSA was generated and classified into five classes: very low, low, moderate, high, and very high susceptibility. The area under the curve (AUC) analysis validated the flood susceptibility model's accuracy. The success rate AUC was calculated to be 0.89, while the prediction rate AUC was 0.82. The flood susceptibility analysis could be used to develop flood mitigation strategies in land use planning.

GIS-based multicriteria decision analysis for settlement areas: a case study in Canik.

In addition to global population growth due to migration from rural areas to urban areas, population density is constantly increasing in certain regions, thereby necessitating the introduction of new settlements in these regions. However, in the selection of settlement areas, no sufficient preliminary examinations have been conducted; consequently, various natural disasters may cause significant life and property losses. Herein, the most suitable settlement areas were determined using GIS (geographic information systems) in Canik District, where the population is continuously increasing. Therefore, this study aimed to incorporate a new perspective into studies on this subject. Within the scope of the study, landslide and flood risks, which are among the most important natural disasters in the region, were primarily evaluated, and high-risk areas were determined. Elevation, slope, aspect, curvature, lithology, topographic humidity index (TWI), and proximity to river parameters were used to produce flood susceptibility maps. A digital elevation model (DEM) of the study area was produced using contours on the 1/25,000 scaled topographic map. The elevation, slope, aspect, curvature, and TWI parameters were produced from the DEM using the relevant analysis routines of ArcGIS software. The raster map of each parameter was divided into 5 subclasses using the natural breaks classification method. In the reclassified raster maps, the most flood-sensitive or flood-prone subclasses were assigned a value of 5, and the least sensitive subclasses were assigned a value of 1. Then, the reclassified maps of the 7 parameters were collected using the "map algebra" function of ArcGIS 10.5 software, and the flood susceptibility index (FSI) map of the study area was obtained. The flood susceptibility map of the study area was obtained by dividing the FSI into 5 subclasses (very low, low, moderate, high, and very high) according to the natural breaks classification method. Thereafter, suitable and unsuitable areas in terms of biocomfort, which affects people's health, peace, comfort, and psychology and is significant in terms of energy efficiency, were determined. At the last stage of the study, the most suitable settlement areas that were suitable in terms of both biocomfort and low levels of landslide and flood risks were determined. The calculated proportion of such areas to the total study area was only 2.1%. Therefore, because these areas were insufficient for the establishment of new settlements, areas that had low landslide and flood risks but were unsuitable for biocomfort were secondarily determined; the ratio of these areas was calculated as 56.8%. The remaining areas were inconvenient for the establishment of settlements due to the risk of landslides and floods; the ratio of these areas was calculated as 41.1%. This study is exemplary in that the priority for the selection of settlement areas was specified, and this method can be applied for selecting new settlements for each region considering different criteria. Due to the risk of landslides or flooding in the study area, the areas unsuitable for establishing a settlement covered approximately 41.1% of the total study area. The areas that had low flood and landslide risks but were suitable for biocomfort constituted only 2.1% of the study area. In approximately 56.8% of the study area, the risk of landslides or floods was low, and these areas were unsuitable in terms of biocomfort. Therefore, these areas were secondarily preferred as settlement areas. The most suitable areas for settlements constituted only 0.19% of the total study area, and these areas will not be able to meet the increasing demand for settlement area. Therefore, it is recommended to select areas that do not have the risk of landslides and floods but are unsuitable for biocomfort. This study reveals that grading should be performed in the selection of settlement areas. When choosing a settlement area in any region, possible natural disasters in the region should be identified first, and these disasters should be ordered in terms of their threat potential. Moreover, biocomfort areas suitable for settlements should be considered. In the next stages of settlement area selection, the criteria that affect the peace and comfort of people, such as distance to pollution sources, distance to noise sources, and proximity to natural areas, should also be evaluated. Thus, a priority order should be created for the selection of settlement areas using various other criteria.

Water Abundance Comprehensive Evaluation of Coal Mine Aquifer Based on Projection Pursuit Model

Abstract After coal mining, mining fissures develop, which may lead to an overlying aquifer and water inrush. Objectively and accurately evaluating and predicting the water abundance of the bottom aquifer of the Cenozoic are of great significance for ensuring safe mining of shallow coal seams and for the protection of water resources. The water richness evaluation index data has characteristics of high dimensionality, nonlinearity, and nonnormal distribution, and therefore, results often cannot objectively and truly reflect the water abundance of aquifers. To overcome these problems, a water abundance evaluation method is proposed that is based on the projection pursuit model. Taking the Cenozoic bottom aquifer in the Xutuan mine of Huaibei mining area, China, as a research object, a water abundance evaluation index system is constructed based on the law of sedimentary characteristics controlling groundwater. This system consists of four factors including aquifer thickness, sand and gravel layer thickness, number of sand and gravel layer, and ratio of sand and gravel content to total aquifer content. The projection pursuit comprehensive evaluation method is introduced to the water abundance evaluation of the aquifer. The method is used to optimize and solve the optimal projection direction, and the comprehensive projection value is calculated according to the optimal projection direction. The size of the comprehensive projection value is then used as basis for characterizing the water abundance degree of the Cenozoic bottom aquifer in the study area. Moreover, the Jenks natural breaks classification method is used to grade and evaluate the comprehensive projection value of water abundance. Finally, the evaluation results of water abundance are verified by the value of unit water inflow and the distribution of known water inrush points. The results show that it is feasible to employ the projection pursuit model for water abundance evaluation of mine aquifers, and optimal evaluation results can be obtained. The model projects high-dimensional data onto a one-dimensional subspace for data analysis and finds the optimal projection direction that can identify the data characteristics and mine the data to the greatest extent. Furthermore, this model avoids interferences by subjectivity and human factors and improves the scientificity and accuracy of the evaluation results. This study provides a new method and concept for the evaluation of aquifer water abundance involving multiple factors.

Modeling gully erosion susceptibility in Phuentsholing, Bhutan using deep learning and basic machine learning algorithms

The present study attempts to demarcate the areas susceptible to gully erosion in Phuentsholing, Bhutan, using Deep Learning CNN (convolution neural network) and artificial neuron network (ANN), Support Vector Machine (SVM) and maximum entropy, three basic machine learning techniques in the GIS setting. Application of deep learning technique is new in the field of gully erosion. Considering the 240 gully pixels and seventeen gully erosion conditioning factors (GECFs), the gully erosion susceptibility maps (GESMs) were prepared. Out of the 240 gully pixels, 70% were used as training datasets and 30% were used as validation datasets for modeling and judging the GESMs. The GECFs were selected based on the previous literatures and multi-collinearity test. The importance of the GECFs was assessed by the chi-square attribute evaluation (CSEA) and random forest (RF) methods. Finally, applying the receiver operating characteristics’ area under curve (AUC-ROC), RMSE, MAE and R-index, the robustness of the GESMs was evaluated and compared. The GESMs were classified using natural break classification method into very high, high, moderate, low and very low susceptible classes. Nearly, 20% of the study area has very high susceptibility to gully erosion. As per the results of CSEA and RF methods, sand concentration, land use\cover and altitudes have the largest contribution in making the area very susceptible to gully erosion. Results of the validation techniques recognized the entire selected model as accurate and robust. Among the selected models, the capability of CNN model (AUC = 0.910, MAE = 0.029, RMSE = 0.171 for training data and AUC = 0.929, MAE = 0.089, RMSE = 0.299 for testing data) in predicting the gully erosion susceptibility is higher than other models. The produced GESMs will be helpful to the researchers as well as decision makers in establishing gully erosion management strategies.

Economic, Social, and Ecological Impact Evaluation of Traffic Network in Beijing–Tianjin–Hebei Urban Agglomeration Based on the Entropy Weight TOPSIS Method

In recent years, with the rapid development of urban transportation network in China, many problems have been exposed, especially in the Beijing–Tianjin–Hebei (BTH) region. Under the call of sustainable development, it is of great significance to evaluate the economic, social, and ecological (ESE) impact of transportation network in BTH urban agglomeration for promoting the sustainable development of transportation ESE in BTH urban agglomeration. In this paper, 12 indicators in the field of transportation are selected to build the evaluation index system of ESE effects of transportation network in BTH urban agglomeration. By using entropy weight TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model and the Jenks natural breaks classification method, the ESE impacts of transportation network in 13 cities of BTH from 2013 to 2017 are analyzed from the temporal and spatial dimensions. The research shows that: (1) From 2013 to 2017, the economic impact degree of traffic network shows an annual fluctuation trend, the social impact degree increases year by year, and the ecological impact degree decreases year by year; (2) For the cities of BTH, the ESE impact assessment results of transportation network from 2013 to 2017 can be divided into seven clusters. Except Handan City, the ESE impact assessment categories of other cities’ transportation network have been improved, but the proportion of cities in the transition period is still large, especially the “Low-Low-Low” cities. The types of cities in the transitional period need to be focused. It is still a heavy burden to realize the ESE coordination and sustainable development of BTH urban agglomeration transportation network.

Regional Sustainable Assessment at City Level Based on CSDIS (China Sustainable Development Indicator System) Concept in the New Era, China

The core issue of sustainable development refers to the coordinated development of economic-social-environmental issues. In the present study, by complying with the China Sustainable Development Indicator System (CSDIS) concept, a comprehensive index system was built; besides, Natural Breaks (Jenks) Classification Method, Exploratory Spatial Data Analysis and Geographic Detector Analysis were conducted to delve into the sustainability and coordinated degree at city level in China from 2007 to 2017. The achieved results are presented as follows. First, for spatial differentiation, the overall spatial distribution pattern was characterized by the high-value units in eastern China and the low-value units in western China from 2007 to 2017. To be specific, the high-value units were radiated along the Beijing-Guangdong Axis (Jing-Guang Axis) centered on the core of Beijing-Tianjin-Hebei Region, the middle-value units were distributed in strips along the coast, and the low-value units were vastly gathered in western China and gradually break via the Hu Huanyong line (Hu Line) in south China from 2007 to 2017. More specifically, based on the five subsystems, the pattern of each system was consistent with the whole, whereas the degree of concentration was different. Second, for spatial correlation, the significant High-High (HH) areas were primarily distributed in the core of Beijing-Tianjin-Hebei, Yangtze River Delta and Pearl River Delta Regions. The significant Low-Low (LL) areas were continuously distributed in the southwest China and broke through the Hu Line from 2007 to 2017. There were insufficient number of significant High-Low (HL) and significant Low-High (LH) areas, whereas the spatial agglomeration of them was less obvious. Third, for internal coupling coordination, the spatial differentiation between the coupling degree and the coupling coordinated degree was significantly consistent in 2007 and 2017. The Beijing-Tianjin-Hebei, Yangtze River Delta and Pearl River Delta Regions have demonstrated a high level of coordinated evolution, and the pattern of western mountainous areas exhibited a low degree of coordinated growth. Lastly, based on the combination of quantitative and qualitative, its factors were underpinned by robust economic strength, the vitality support of the information level and the basic support function of the topography, active guidance of national policies and path dependence and industrial transfer.

Construction and Evaluation of the Integrated Perception Ecological Environment Indicator (IPEEI) Based on the DPSIR Framework for Smart Sustainable Cities

Ecological environment evaluation is of great significance to achieve the Sustainable Development Goals (SDGs) and promote the harmonious development of economy, society, and environment. To evaluate environmental SDGs, single environmental indicators have been analyzed at national or large regional scale in some literature, while the urban integrated environment is ignored. Therefore, it is necessary to systematically and quantically evaluate the sustainability of ecological environment integrating the water, soil, and air environment at the urban scale. This study aims to construct the Integrated Perception Ecological Environment Indicator (IPEEI) based on the Driver-Pressure-State-Impact-Response (DPSIR) framework to solve the above-mentioned problems. The IPEEI model was proposed based on the three-level association mechanism of the Domain-Theme-Element, and the DPSIR framework conforming to the relevant standards for indicator determination. Moreover, the multi-dimensional, multi-thematic, and multi-urban quantitative evaluations were conducted using the entropy weight method, and the comprehensive evaluation grades by the Jenks natural breaks classification method of the geospatial analysis. Nine cities in the Wuhan Metropolitan Area were selected as the experimental areas. The results were consistent with the Ecological Index and local government’s planning and measures, which demonstrated that IPEEI can be effectively verified and applied for the evaluation of urban ecological environment sustainability.

Impact of ecological security on urban sustainability in Western China—A case study of Xi’an

China has experienced rapid urbanization for over 40 years, posing a significant challenge to the ecological environment and urban sustainability, which is especially critical in cities in Western China. To critically measure the impact of ecological security on urban sustainability, we developed a quantitative approach to obtain evaluation results for decision-making. Taking Xi’an, one of the central cities in Western China, as an example, we used the methodology to conduct a quantitative analysis of the impact of regional ecological security on urban sustainability. The driving force–pressure–state–response framework has been used to construct a comprehensive assessment system and the distribution of ecological security index was analyzed using Geographic Information System (GIS) software. To understand the level of urban ecological security, the natural breaks classification method was adopted to divide the results into five categories: highly safe, satisfactorily safe, safe, low value safe, unsafe. The results have shown that the ecological security in Xi’an is basically stable, and the overall status is close to the safe status, but there is a significant difference within the research area. According to the assessment results, the impact of ecological security on sustainability of Xi’an is mainly concerning high-density population, high-density water consumption, high per capita energy consumption, low vegetation coverage, and low-density river corridors. In addition, through the quantitative analysis of the relationship between ecological security and terrain, it is observed that the ecological security level distribution of Xi’an decreases from mountains to tablelands to hills and plains. The paper shows that the comprehensive assessment system of urban ecological security established here is effective to identify natural and artificial ecological security factors that threaten urban sustainability.

Novel Ensemble Approaches of Machine Learning Techniques in Modeling the Gully Erosion Susceptibility

Gully erosion has become one of the major environmental issues, due to the severity of its impact in many parts of the world. Gully erosion directly and indirectly affects agriculture and infrastructural development. The Golestan Dam basin, where soil erosion and degradation are very severe problems, was selected as the study area. This research maps gully erosion susceptibility (GES) by integrating four models: maximum entropy (MaxEnt), artificial neural network (ANN), support vector machine (SVM), and general linear model (GLM). Of 1042 gully locations, 729 (70%) and 313 (30%) gully locations were used for modeling and validation purposes, respectively. Fourteen effective gully erosion conditioning factors (GECFs) were selected for spatial gully erosion modeling. Tolerance and variance inflation factors (VIFs) were used to examine the collinearity among the GECFs. The random forest (RF) model was used to assess factors’ effectiveness and significance in gully erosion modeling. An ensemble of techniques can provide more accurate results than can single, standalone models. Therefore, we compared two-, three-, and four-model ensembles (ANN-SVM, GLM-ANN, GLM-MaxEnt, GLM-SVM, MaxEnt-ANN, MaxEnt-SVM, ANN-SVM-GLM, GLM-MaxEnt-ANN, GLM-MaxEnt-SVM, MaxEnt-ANN-SVM and GLM-ANN-SVM-MaxEnt) for GES modeling. The susceptibility zones of the GESMs were classified as very-low, low, medium, high, and very-high using Jenks’ natural break classification method (NBM). Subsequently, the receiver operating characteristics (ROC) curve and the seed cell area index (SCAI) methods measured the reliability of the models. The success rate curve (SRC) and predication rate curve (PRC) and their area under the curve (AUC) values were obtained from the GES maps. The results show that the ANN model combined with two and three models are more accurate than the other combinations, but the ANN-SVM model had the highest accuracy. The rank of the others from best to worst accuracy is GLM, MaxEnt, SVM, GLM-ANN, GLM-MaxEnt, GLM-SVM, MaxEnt-ANN, MaxEnt-SVM, GLM-ANN-SVM-MaxEnt, GLM-MaxEnt-ANN, GLM-MaxEnt-SVM and MaxEnt-ANN-SVM. The resulting gully erosion susceptibility models (GESMs) are efficient and powerful and could be used to improve soil and water conservation and management.

Landslide susceptibility mapping by fuzzy gamma operator and GIS, a case study of a section of the national road n°11 linking Mateur to Béja (Nortshern Tunisia)

The purpose of this paper is to map the landslide susceptibility using the fuzzy gamma operator and GIS for the section of the national road n°11 linking Mateur to Beja. To minimize the subjectivity of the fuzzy logic approach, the frequency ratio was used to calculate the fuzzy membership. To locate the 147 landslides, Google Earth extracts analysis, and field survey has been done. In this cartography, nine factors controlling landslides were used: slope, aspect, curvature plane, curvature profile, distance to faults, distance to rivers, land use, rainfall, and lithology. Once the fuzziness is calculated in each factor using the frequency ratio (FR), the fuzzy gamma operator can be applied to these nine factors to produce the landslide susceptibility map. Finally, the choice of the nearest susceptibility map of reality is made by applying the ROC curve. By the analysis of the area under curve (AUC), it is seen that the prediction accuracy of model proves that the gamma value of 0.90 yielded the better prediction of landslide susceptibility map (0.89). Finally, our area of interest was classified based on the Jenks natural breaks classification method into five classes, such as non-susceptible zone, low susceptible zone, moderate susceptible zone, high susceptible zone, and very high susceptible zone. Landslide susceptibility mapping based on the fuzzy gamma operator presents a very acceptable result with a reliability of 89%.

Geospatial analysis in groundwater resources management as a tool for reclamation areas of New Valley (El-Oweinat), Egypt

The purpose of this paper is the application of geospatial data and techniques on the evaluation of potential groundwater management and uses in south New Valley (East El-Oweinat area), western desert of Egypt. An integration of geophysical, hydrogeological and remote sensing data into a Geographic Information System (GIS) has been used to determine groundwater potential zones for sustainable development. In the hydrogeophysical study, correlation of subsurface lithofacies has been carried out in different directions based on well logging data (Gamma ray, SP and normal short and long resistivity logs). The aquifer and aquitard thicknesses have been determined in the intensive study area. Moreover, a relationship between borehole resistivity and the hydraulic characteristics has been constructed in order to calculate transmissivity and hydraulic conductivity. In the hydrogeological study, analyses of pumping tests have been carried out especially the step drawdown tests for the groundwater wells. These pumping tests will determine the well performance criteria such as well loss, aquifer loss, well efficiency and optimum pumping rates. In the Remote Sensing and GIS study, Landsat imagery and Digital Elevation Model (DEM) have been processed, analyzed and managed in layers of data set. These data were used to gather current information for the surface topographic features such as elevation, slope, land use/cover and sand dunes in the study area. Sand dunes are located in the northwestern direction and are considered as a very low potentiality due to the effect of these dunes on the agricultural activities. Finally, an integration of these surface and subsurface geospatial characteristic into GIS environment has been carried out. Natural breaks classification method has been used in order to classify each thematic map. The analytic hierarchy process (AHP) has been used in order to create the final groundwater potentiality map. Four zones A, B, C and D have been selected to be the most potentiality zones with different capacities which can be used for drilling new groundwater wells.