Dynamics of Advantageous Mutant Spread in Spatial Death-Birth and Birth-Death Moran Models

The spread of an advantageous mutation through a population is of fundamental interest in population genetics. While the classical Moran model is formulated for a well-mixed population, it has long been recognized that in real-world applications, the population usually has an explicit spatial structure which can significantly influence the dynamics. In the context of cancer initiation in epithelial tissue, several recent works have analyzed the dynamics of advantageous mutant spread on integer lattices, using the biased voter model from particle systems theory. In this spatial version of the Moran model, individuals first reproduce according to their fitness and then replace a neighboring individual. From a biological standpoint, the opposite dynamics, where individuals first die and are then replaced by a neighboring individual according to its fitness, are equally relevant. Here, we investigate this death-birth analogue of the biased voter model. We construct the process mathematically, derive the associated dual process, establish bounds on the survival probability of a single mutant, and prove that the process has an asymptotic shape. We also briefly discuss alternative birth-death and death-birth dynamics, depending on how the mutant fitness advantage affects the dynamics. We show that birth-death and death-birth formulations of the biased voter model are equivalent when fitness affects the former event of each update of the model, whereas the birth-death model is fundamentally different from the death-birth model when fitness affects the latter event.

Analysis of spatial distribution characteristics and driving factors of ethnicminority villages in China using geospatial technology and statistical models

This study aims to reveal the spatial structural characteristics of 1,652 Ethnic-Minority Villages(EMV)in China and to analyze the mechanisms driving their spatial heterogeneity.EMV are a special type of settlement space that preserve a large number of historical traces of the ethnic culture of ancient China.They are important carriers of China’s excellent traditional culture and are key to the implementation of rural revitalization strategies.In this study,1652 EMV in China were selected as the research subjects.The Nearest Neighbor Index,kernel density,and spatial autocorrelation index were employed to reveal the spatial structural characteristics of minority villages.Neural network models,spatial lag models,and geographical detectors were used to analyze the formation mechanism of spatial heterogeneity in EMV.The results indicate that:(1)EMV exhibit significant spatial differentiation characterized by“single-core with multiple surrounding sub-centers,”“polarization between east and west,”“decreasing quantity from southwest to east coast to northeast to northwest,”and“large dispersion with small agglomeration.”(2)EMV are mainly distributed in areas rich in intangible cultural heritage,with high vegetation coverage and low altitude,far from central cities,and having limited arable land and an underdeveloped economy and transportation,particularly in shaded or riverbank areas.(3)Distance from the nearest river(X3),distance from central cities(X8),national intangible cultural heritage(X9),and NDVI(X10)were the main driving factors affecting the spatial distribution of EMV,whereas elevation(X1)and GDP(X5)had the weakest influence.As EMV are a relatively unique territorial spatial unit,the identification of their spatial heterogeneity characteristics not only deepens the research content of settlement geography,but also involves the assessment,protection,and development of Minority Villages,which is of great significance for the inheritance and utilization of excellent ethnic cultures in the era.

Drivers for Inter-city Innovation Networks Across Chinese Cities:Modelling Physical Versus Intangible Effects

Cross-region innovation is widely recognized as an important source of the long-term regional innovation capacity.In the recent past,a growing number of studies has investigated the network structure and mechanisms of cross-region innovation collaboration in various contexts.However,existing research mainly focuses on physical effects,such as geographical distance and high-speed railway connections.These studies ignore the intangible drivers in a changing environment,the more digitalized economy and the increasingly solidified innovation network structure.Thus,the focus of this study is on estimating determinants of innovation networks,especially on intangible drivers,which have been largely neglected so far.Using city-level data of Chinese patents(excluding Hong Kong,Macao,and Taiwan Province of China),we trace innovation networks across Chinese cities over a long period of time.By integrating a measure on Information and Communications Technology(ICT)development gap and network structural effects into the general proximity framework,this paper explores the changing mechanisms of Chinese innovation networks from a new perspective.The results show that the structure of cross-region innovation networks has changed in China.As mechanisms behind this development,the results confirm the increasingly important role of intangible drivers in Chinese inter-city innovation collaboration when controlling for effects of physical proximity,such as geographical distance.Since digitalization and coordinated development are the mainstream trends in China and other developing countries,these countries'inter-city innovation collaboration patterns will witness dramatic changes under the influence of intangible drivers.

Spatial Distribution Pattern and Influencing Factors of Bed-and-breakfasts(B&Bs)from the Perspective of Urban-rural Differences:A Case Study of Jiaodong Peninsula,China

There are significant differences between urban and rural bed-and-breakfasts(B&Bs)in terms of customer positioning,economic strength and spatial carrier.Accurately identifying the differences in spatial characteristics and influencing factors of each type,is essential for creating urban and rural B&B agglomeration areas.This study used density-based spatial clustering of applications with noise(DBSCAN)and the multi-scale geographically weighted regression(MGWR)model to explore similarities and differences in the spatial distribution patterns and influencing factors for urban and rural B&Bs on the Jiaodong Peninsula of China from 2010 to 2022.The results showed that:1)both urban and rural B&Bs in Jiaodong Peninsula went through three stages:a slow start from 2010 to 2015,rapid development from 2015 to 2019,and hindered development from 2019 to 2022.However,urban B&Bs demonstrated a higher development speed and agglomeration intensity,leading to an increasingly evident trend of uneven development between the two sectors.2)The clustering scale of both urban and rural B&Bs continued to expand in terms of quantity and volume.Urban B&B clusters characterized by a limited number,but a higher likelihood of transitioning from low-level to high-level clusters.While the number of rural B&B clusters steadily increased over time,their clustering scale was comparatively lower than that of urban B&Bs,and they lacked the presence of high-level clustering.3)In terms of development direction,urban B&B clusters exhibited a relatively stable pattern and evolved into high-level clustering centers within the main urban areas.Conversely,rural B&Bs exhibited a more pronounced spatial diffusion effect,with clusters showing a trend of multi-center development along the coastline.4)Transport emerged as a common influencing factor for both urban and rural B&Bs,with the density of road network having the strongest explanatory power for their spatial distribution.In terms of differences,population agglomeration had a positive impact on the distribution of urban B&Bs and a negative effect on the distribution of rural B&Bs.Rural B&Bs clustering was more influenced by tourism resources compared with urban B&Bs,but increasing tourist stay duration remains an urgent issue to be addressed.The findings of this study could provide a more precise basis for government planning and management of urban and rural B&B agglomeration areas.

Combining spatial modelling and radar to identify and protect avian migratory hot-spots

Migrating landbirds are known to follow coast lines and concentrate on peninsulas prior to crossing water bodies, es- pecially during daylight but also at night, creating enhanced potential collision hazards with man-made objects. Knowing where these avian migration ＂hot-spots＂ occur in time and space is vital to improve flight safety and inform the spatial planning process （e.g. environmental assessments for offshore windfarms）. We developed a simple spatial model to identify avian migration hot- spots in coastal areas based on prevailing migration orientation and coastline features known, from visual and radar observations, to concentrate migrating landbirds around land masses. Regional scale model validation was achieved by combining nocturnal passerine movement data gathered from two tier radar coverage （long-range dual-polarization Doppler weather radar and short- range marine surveillance radar） and standardised bird ringing. Applied on a national scale, the model correctly identified the ten most important Danish coastal hot-spots for spring migrants and predicted the relative numbers of birds that concentrated at each site. These bird numbers corresponded well with historical observational data. Here, we provide a potential framework for the es- tablishment of the first three-dimensional avian airspace sanctuaries, which could contribute to more effective conservation of long-distance migratory birds [Current Zoology 60 （5）： 680-691, 2014].

Spatial spillover effects of official development assistance on environmental pressure in sub-Saharan African(SSA)countries

Impoverished sub-Saharan Africa(SSA)is under increasing environmental pressure from global environmental changes.It is now generally accepted in academic circles that economic development in SSA countries can cause environmental pressure in other countries.However,there is research gap on the impact of economic assistance on environmental pressure in SSA countries and whether economic assistance causes spatial spillovers of environ-mental pressure between SSA countries.To better understand the impact of economic assistance on environmental pressures in SSA,a dynamic spatial Dubin panel model was developed.It helped us explore the spatial spillover effects of economic assistance on environmental pressures in recipient countries based on the panel data from 34 SSA countries.The results show that economic assistance had a positive stimulating effect on environmen-tal pressures of recipient countries,which means that the degree of human disturbance to the environment has deepened.Due to the regional correlation effect,neighboring countries were saddled with environmental pres-sures from the target country.Moreover,environmental pressures have time inertia,which can easily produce a snowball effect.The decomposition of effects shows that the impact of economic assistance on environmental pressures is relatively minor.Environmental pressures have spillover effects,so to deal with diffuse risks,joint regional prevention and control policies should be developed.

Spatial Analysis and Modelling of Wind Farm Site Suitability in Nasarawa State, North-Central Nigeria

There has been an increasing global and local interest in developing renewable, clean, and cheap energy towards achieving Goal number 7 of the Sustainable Development Goals (SDG). However, decisions involving suitable and sustainable locations for renewable energy projects remain an important task. This study employed Geographic Information System (GIS) and Multi-Criteria Decision Analysis (MCDA) to spatially analyze and model wind farm site suitability in Nasarawa State. The aim is to integrate the environmental, social, and economic aspects of decision-making for identifying sustainable wind farm sites. The study distinguished between two sets of decision criteria: decision constraints and decision factors. The former defined the exclusion zones while the latter were standardized based on fuzzy logic to depict varying degrees of suitability across the State. The MCDA applied the weighted linear combination method, with relative weights generated through pairwise comparisons of the analytic hierarchy process to analyze three policy scenarios: equal weights, environmental/social priority, and economic priority scenario. A combination of resulting composite maps from the constraints and the factors gave the final suitability maps. The resulting suitability index (SI) for the respective policy scenario describes the degrees of suitability: Ideal locations were denoted by one (1) and the not suitable locations by zero (0), with values in-between depicting varying degrees of wind farm site suitability. Based on the SI, priority locations indicating areas with good prospects, in addition to the most suitable parcels of land, were identified and delineated. The composite decision constraint revealed that wind farm projects would not be viable in more than half (57.58%) of the State. Wind speed was the major constraint and accounted for the exclusion of 46.25%, with a mean fuzzy membership value of 0.2008 indicating low suitability across the State. Also, the average acceptable wind farm location for the three-policy scenario was 33.33% of the entire study area. Lafia, Obi, Keana, Awe, Nasarawa-Eggon, Wamba and Kokona LGAs were the identified priority Local Government Areas (LGAs). However, only Lafia, Obi, and Nasarawa-Eggon were consistent with changes in the policy objectives. All the priority LGAs have one or more of the most suitable parcels within their administrative boundaries except for Wamba. Despite the severe limitations of wind speed, substantial parts of Nasarawa State still provide great development potentials for wind energy. The “most suitable” locations in Lafia, Nasarawa-Eggon, and Obi LGAs should have first consideration for the development of wind energy in the State.

Analysis on the Spatial Agglomeration and Coupling of Socioeconomic Factors in Qinghai Tourism Under the Background of“Belt and Road”

Qinghai is the strategic base and important fulcrum of the Belt and Road Initiative while tourism is a strategic pillar industry in Qinghai Province.Due to its rich tourism resources and unique ecological environment,the integration of tourism in Qinghai into the Belt and Road has attracted great attention of the Asian Development Bank(ADB).With the spatial data of tourism elements POI and the statistical data of 44 counties in Qinghai to analyze the characteristics and influencing factors of the spatial agglomeration of tourism in Qinghai,the paper conducts research on spatial coupling and concludes with the following results:The spatial agglomeration of tourism in Qinghai presents the distribution pattern of“one circle and one belt”;economic density and population density play an important role in the formation of the spatial agglomeration pattern of tourism with some spatial spillovers;Belt and Road has a significant impact on the promotion of tourism agglomeration in Qinghai.The paper suggests that tourism in Qinghai should fully integrate into the Belt and Road,giving full play to the guiding role of Belt and Road in the allocation of social and economic resources,and optimizing the spatial layout.

Modelling of shallow landslides with machine learning algorithms

This paper introduces three machine learning(ML)algorithms,the‘ensemble'Random Forest(RF),the‘ensemble'Gradient Boosted Regression Tree(GBRT)and the Multi Layer Perceptron neural network(MLP)and applies them to the spatial modelling of shallow landslides near Kvam in Norway.In the development of the ML models,a total of 11 significant landslide controlling factors were selected.The controlling factors relate to the geomorphology,geology,geo-environment and anthropogenic effects:slope angle,aspect,plan curvature,profile curvature,flow accumulation,flow direction,distance to rivers,water content,saturation,rainfall and distance to roads.It is observed that slope angle was the most significant controlling factor in the ML analyses.The performance of the three ML models was evaluated quantitatively based on the Receiver Operating Characteristic(ROC)analysis.The results show that the‘ensemble'GBRT machine learning model yielded the most promising results for the spatial prediction of shallow landslides,with a 95%probability of landslide detection and 87%prediction efficiency.

GPS probe map matching algorithm based on spatial data model

To improve the performance of the traditional map matching algorithms in freeway traffic state monitoring systems using the low logging frequency GPS （global positioning system） probe data, a map matching algorithm based on the Oracle spatial data model is proposed. The algorithm uses the Oracle road network data model to analyze the spatial relationships between massive GPS positioning points and freeway networks, builds an N-shortest path algorithm to find reasonable candidate routes between GPS positioning points efficiently, and uses the fuzzy logic inference system to determine the final matched traveling route. According to the implementation with field data from Los Angeles, the computation speed of the algorithm is about 135 GPS positioning points per second and the accuracy is 98.9%. The results demonstrate the effectiveness and accuracy of the proposed algorithm for mapping massive GPS positioning data onto freeway networks with complex geometric characteristics.

Brent vs.West Texas Intermediate in the US petro derivatives price formation

In this paper,we apply the spatial panel model to explore the relationship between the dynamic of two types of crude oil prices(WTI and Brent crude oil)and their refined products over time.Considering the turbulent months of 2011,when Cushing Oklahoma had reached capacity and the crude oil export ban removal in 2015 as breakpoints,we apply this method both in the full sample and the three resultant regimes.First,results suggest our results show that both WTI and Brent display very similar behaviour with the refined products.Second,when attending to each regime,results derived from the first and third regimes are quite similar to the full sample results.Therefore,during the second regime,Brent crude oil became the benchmark in the petrol market,and it influenced the distillate products.Furthermore,our model can let us determine the price-setters and price-followers in the price formation mechanism through refined products.These results possess important considerations to policymakers and the market participants and the price formation.

Artificial Intelligence:A new era for spatial modelling and interpreting climate-induced hazard assessment

The application of Artificial Intelligence in various fields has witnessed tremendous progress in the recent years.The field of geosciences and natural hazard modelling has also benefitted immensely from the introduction of novel algorithms,the availability of large quantities of data,and the increase in computational capacity.The enhancement in algorithms can be largely attributed to the elevated complexity of the network architecture and the heightened level of abstraction found in the network's later layers.As a result,AI models lack transparency and accountability,often being dubbed as"black box"models.Explainable AI(XAI)is emerging as a solution to make AI models more transparent,especially in domains where transparency is essential.Much discussion surrounds the use of XAI for diverse purposes,as researchers explore its applications across various domains.With the growing body of research papers on XAI case studies,it has become increasingly important to address existing gaps in the literature.The current literature lacks a comprehensive understanding of the capabilities,limitations,and practical implications of XAI.This study provides a comprehensive overview of what constitutes XAI,how it is being used and potential applications in hydrometeorological natural hazards.It aims to serve as a useful reference for researchers,practitioners,and stakeholders who are currently using or intending to adopt XAI,thereby contributing to the advancements for wider acceptance of XAI in the future.

Synergistic dance of digital economy and green finance on carbon emissions:Insights from China

China has recently implemented a dual-carbon strategy to combat climate change and other environmental issues and is committed to modernizing it sustainably.This paper supports these goals and explores how the digital economy and green finance intersect and impact carbon emissions.Using panel data from 30 Chinese provinces over the period 2011-2021,this paper finds that the digital economy and green finance can together reduce carbon emissions,and conducts several robustness tests supporting this conclusion.A heterogeneity analysis shows that these synergistic effects are more important in regions with low levels of social consumption Meanwhile,in the spatial dimension,the synergistic effect of the local digital economy and green finance adversely impacts the level of carbon emissions in surrounding areas.The findings of this paper provide insights for policymakers in guiding capital flow and implementing carbon-reduction policies while fostering the growth of China’s digital economy and environmental sustainability.

Unveiling the impact and mechanism of digital technology on agricultural economic resilience

Enhancing the economic resilience of agriculture is essential for promoting sustainable and high-quality agricultural development.The emergence of digital technology has created new opportunities in this field.However,existing research predominantly focuses on traditional agricultural factors and technologies.Therefore,the impact of digital technology on agricultural economic resilience within the broader context of the“production-operation-industry”system in agriculture has not been comprehensively explored.To bridge this gap,this study analyzes panel data from 30 Chinese provinces from 2011 to 2020.It employs the static Van Dorn’s law and a dynamic spatial panel model to examine how digital technology empowers agricultural resilience.The findings indicate a continuous strengthening of digital technology development in China,albeit with significant polarization and spatial imbalances.Moreover,the resilience of the agricultural economy undergoes notable fluctuations,initially narrowing and subsequently displaying an upward trend.Digital technology clearly plays a pivotal role in empowering resilience through agricultural scale operation,industrial transformation,and technological progress.Its impact,particularly on the promotion of resilience in the eastern region and non-grain-producing areas and on high-level agricultural economies,also shows regional and technological variations.

Socio-Economic Vulnerability to COVID-19: The Spatial Case of Greater Kampala Metropolitan Area (GKMA)

COVID-19 has presented itself with an extreme impact on the resources of its epi-centres. In Uganda, there is uncertainty about what will happen especially in the main urban hub, the Greater Kampala Metropolitan Area (GKMA). Consequently, public health professionals have scrambled into resource-driven strategies and planning to tame the spread. This paper, therefore, deploys spatial modelling to contribute to an understanding of the spatial variation of COVID-19 vulnerability in the GKMA using the socio-economic characteristics of the region. Based on expert opinion on the prevailing novel Coronavirus, spatially driven indicators were generated to assess vulnerability. Through an online survey and auxiliary datasets, these indicators were transformed, classified, and weighted based on the BBC vulnerability framework. These were spatially modelled to assess the vulnerability indices. The resultant continuous indices were aggregated, explicitly zoned, classified, and ranked based on parishes. The resultant spatial nature of vulnerability to COVID-19 in the GKMA sprawls out of major urban areas, diffuses into the peri-urban, and thins into the sparsely populated areas. The high levels of vulnerability (24.5% parishes) are concentrated in the major towns where there are many shopping malls, transactional offices, and transport hubs. Nearly half the total parishes in the GKMA (47.3%) were moderately vulnerable, these constituted mainly the parishes on the outskirts of the major towns while 28.2% had a low vulnerability. The spatial approach presented in this paper contributes to providing a rapid assessment of the socio-economic vulnerability based on administrative decision units-parishes. This essentially equips the public health domain with the right diagnosis to subject the highly exposed and vulnerable communities to regulatory policy, increase resilience incentives in low adaptive areas and optimally deploy resources to avoid the emancipation of high susceptibility areas into an epicentre of Covid-19.

Spatial Heterogeneity Association of HIV Incidence with Socio-economic Factors in Zimbabwe

This study examined the spatial heterogeneity association of HIV incidence and socio-economic factors including poverty severity index,permanently employed females and males,unemployed females,percentage of poor households i.e.,poverty prevalence,night lights index,literacy rate,household food security,and Gini index at district level in Zimbabwe.A mix of spatial analysis methods including Poisson model based on original log likelihood ratios(LLR),global Moran’s I,local indicator of spatial association-LISA were employed to determine the HIV hotspots.Geographically Weighted Poisson Regression(GWPR)and semi-parametric GWPR(s-GWPR)were used to determine the spatial association between HIV incidence and socio-economic factors.HIV incidence(number of cases per 1000)ranged from 0.6(Buhera district)to 13.30(Mangwe district).Spatial clustering of HIV incidence was observed(Global Moran’s I=-0.150;Z score 3.038;p-value 0.002).Significant clusters of HIV were observed at district level.HIV incidence and its association with socio-economic factors varied across the districts except percentage of females unemployed.Intervention programmes to reduce HIV incidence should address the identified socio-economic factors at district level.

GTP-based Integral Real-3D Spatial Model for Engineering Excavation GIS

Engineering excavation GIS (E 2 GIS) is a real-3D GIS serving for geosciences related to geo-engineering, civil engineering and mining engineering based on generalized tri-prism (GTP) model. As two instances of GTP model, G\|GTP is used for the real\|3D modeling of subsurface geological bodies, and E\|GTP is used for the real\|3D modeling of subsurface engineering excavations.In the light of the discussions on the features and functions of E 2 GIS, the modeling principles of G\|GTP and E\|GTP are introduced. The two models couple together seamlessly to form an integral model for subsurface spatial objects including both geological bodies and excavations. An object\|oriented integral real\|3D data model and integral spatial topological relations are discussed.

Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression(SVR)with GWO,BAT and COA algorithms

In this study,we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models.We created a geographic information system database,and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth,aerial photographs,and other validated sources.A support vector regression(SVR)machine-learning model was used to divide the landslide inventory into training(70%)and testing(30%)datasets.The landslide susceptibility map was produced using 14 causative factors.We applied the established gray wolf optimization(GWO)algorithm,bat algorithm(BA),and cuckoo optimization algorithm(COA)to fine-tune the parameters of the SVR model to improve its predictive accuracy.The resultant hybrid models,SVR-GWO,SVR-BA,and SVR-COA,were validated in terms of the area under curve(AUC)and root mean square error(RMSE).The AUC values for the SVR-GWO(0.733),SVR-BA(0.724),and SVR-COA(0.738)models indicate their good prediction rates for landslide susceptibility modeling.SVR-COA had the greatest accuracy,with an RMSE of 0.21687,and SVR-BA had the least accuracy,with an RMSE of 0.23046.The three optimized hybrid models outperformed the SVR model(AUC=0.704,RMSE=0.26689),confirming the ability of metaheuristic algorithms to improve model performance.

SPATIALIZATION MODEL OF POPULATION BASED ON DATASET OF LAND USE AND LAND COVER CHANGE IN CHINA

The spatialization of population of counties in China is significant. Firstly, we can gain the estimated values of population density adaptive to different kinds of regions. Secondly, we can integrate effectively population data with other data including natural resources, environment, society and economy, build 1km GRIDs of natural resources reserves per person, population density and other economic and environmental data, which are necessary to the national management and macro adjustment and control of natural resources and dynamic monitoring of population. In order to establish population information system serving national decision making, three steps ought to be followed:1) establishing complete geographical spatial data foundation infrastructure including the establishment of electric map of residence with high resolution using topographical map with large scale and high resolution satellite remote sensing data, the determination of attribute information of housing and office buildings, and creating complete set of attribute database and rapid data updating; 2) establishing complete census systems including improving the transformation efficiency from census data to digital database and strengthening the link of census database and geographical spatial database, meanwhile, the government should attach great importance to the establishment and integration of population migration database; 3) considering there is no GIS software specially serving the analysis and management of population data, a practical approach is to add special modules to present software system, which works as a bridge actualizing the digitization and spatialization of population geography research.

Deep learning neural networks for spatially explicit prediction of flash flood probability

Flood probability maps are essential for a range of applications,including land use planning and developing mitigation strategies and early warning systems.This study describes the potential application of two architectures of deep learning neural networks,namely convolutional neural networks(CNN)and recurrent neural networks(RNN),for spatially explicit prediction and mapping of flash flood probability.To develop and validate the predictive models,a geospatial database that contained records for the historical flood events and geo-environmental characteristics of the Golestan Province in northern Iran was constructed.The step-wise weight assessment ratio analysis(SWARA)was employed to investigate the spatial interplay between floods and different influencing factors.The CNN and RNN models were trained using the SWARA weights and validated using the receiver operating characteristics technique.The results showed that the CNN model(AUC=0.832,RMSE=0.144)performed slightly better than the RNN model(AUC=0.814,RMSE=0.181)in predicting future floods.Further,these models demonstrated an improved prediction of floods compared to previous studies that used different models in the same study area.This study showed that the spatially explicit deep learning neural network models are successful in capturing the heterogeneity of spatial patterns of flood probability in the Golestan Province,and the resulting probability maps can be used for the development of mitigation plans in response to the future floods.The general policy implication of our study suggests that design,implementation,and verification of flood early warning systems should be directed to approximately 40%of the land area characterized by high and very susceptibility to flooding.