GIS-based Model Research Articles

GIS-based frequency ratio model for flood susceptibility zonation in the state of Meghalaya, Northeast India

GIS-based frequency ratio model for flood susceptibility zonation in the state of Meghalaya, Northeast India

GIS and Machine Learning Models Target Dynamic Settlement Patterns and Their Driving Mechanisms from the Neolithic to Bronze Age in the Northeastern Tibetan Plateau

Traditional GIS-based statistical models are intended to extrapolate patterns of settlements and their interactions with the environment. They contribute significantly to our knowledge of past human–land relationships. Yet, these models are often criticized for their empiricism, lopsided specific factors, and for overlooking the synergy between variables. Though largely untested, machine learning and artificial intelligence methods have the potential to overcome these shortcomings comprehensively and objectively. The northeastern Tibetan Plateau (NETP) is characterized by diverse environments and significant changes to the social system from the Neolithic to Bronze Age. In this study, this area serves as a representative case for assessing the complex relationships between settlement locations and geographic environments, taking full advantages of these new models. We have explored a novel modeling case by employing GIS and random forests to consider multiple factors, including terrain, vegetation, soil, climate, hydrology, and land suitability, to construct classification models identifying environmental variation across different cultural periods. The model exhibited strong performance and a high archaeological prediction value. Potential living maps were generated for each cultural stage, revealing distinct environmental selection strategies from the Neolithic to Bronze Age. The key environmental parameters of elevation, climate, soil erosion, and cultivated land suitability were calculated with high weights, influencing human environmental decisions synergistically. Furthermore, we conducted a quantitative analysis of temporal dynamics in climate and subsistence to understand driving mechanisms behind environmental strategies. These findings suggest that past human environmental strategies were based on the comprehensive consideration of various factors, coupled with their social economic scenario. Such subsistence-oriented activities supported human beings in overcoming elevation limitation, and thus allowed them to inhabit wider pastoral areas. This study showcases the potential of machine learning in predicting archaeological probabilities and in interpreting the environmental influence on settlement patterns.

A GIS-based model for determining safety indices to evaluate the post-disaster usability of urban open and green spaces in Amasya City Center, Türkiye

A GIS-based model for determining safety indices to evaluate the post-disaster usability of urban open and green spaces in Amasya City Center, Türkiye

Species distribution modelling of Baya Weaver Ploceus philippinus in Nagaon District of Assam, India: a zoogeographical analysis

Identification and mapping of the spatial distribution of species is an important aspect of zoogeographical enquiry. The habitats of many species are facing the threat of depletion in increasingly human-influenced environments. This has already led to the extinction of many species in different localities, making understanding the linkages between anthropogenic threats and species distribution of utmost importance. A GIS-based model was applied to gain an overall picture of the potential distribution of Ploceus philippinus (Baya Weaver) in and around Nagaon District in Assam. The used maxent model in the GIS environment gives a highly significant Area Under Curve (AUC) validation statistic of 0.99. Out of the total area of 3,975 km2, 596.86 km2 (15%) is demarcated as a high-potential area. Such predictions are highly useful in assisting in the conservation of threatened species under current and future climatic conditions.

Computerized constructed wetlands allocation model (based on ecosystem services demand)

Egypt’s urban communities face many threats, including, pluvial floods, heat waves, and lack of publicly accessible urban green spaces. Nature-based solutions such as constructed wetlands (CWs) present a promising solution that could offer a wide range of ecosystem services (ES). However, the adoption of CWs is challenged by the lack of local planning guides and uncertainty about potential benefits. There are various models and tools available for quantifying and valuing ES, however, many of them are either highly complex or require extensive data and expertise. The aim of this paper is to develop a GIS-based multi-criteria decision model to select CW sites based on the supply and demand of ES. The model is to operate on three main stages: (i) demand: based on the need for risk reduction or benefit provisioning, (ii) potential sites (PSs): identify and score suitable sites for establishing a CW, and (iii) supply: define the service benefiting areas (SBA). An experimental approach is used, where the model is tested on New Damietta, an Egyptian Mediterranean city, proving the model is a reliable decision-making tool during preliminary urban planning stages due to its practicality, flexibility, and reasonable data requirements.

GIS-Based Spatial Analysis Model for Assessing Impact and Cumulative Risk in Road Traffic Accidents via Analytic Hierarchy Process (AHP)—Case Study: Romania

On a global scale, traffic incidents are a leading cause of mortality and material damage. Romania exhibits the highest rate of road traffic fatalities both in the European Union and worldwide, requiring a comprehensive examination of its overall influence on a national level. The current study uses an extensive approach centering on a spatial analysis model based on the Analytic Hierarchy Process (AHP). Employing a series of spatial databases, this model delineates the geographical distribution and characteristics of road accidents to establish both their cumulative national impact and the identification of high-risk areas. The spatial database, containing traffic incident data, is constructed using geolocation techniques and integrated through network analysis to evaluate the impact in relation to distance. The AHP framework is applied in analyzing the impact across five key dimensions: accident severity, occurrence mode, prevailing weather conditions, traffic restrictions, and road markings. This multi-level AHP analysis not only identifies high-risk hotspots but also confirms the effectiveness of the proposed spatial model.

A GIS-Based Top-Down Approach to Support Energy Retrofitting for Smart Urban Neighborhoods

Energy and environmental challenges are a major concern across the world and the urban residential building sector, being one of the main stakeholders in energy consumption and greenhouse gas emissions, needs to be more energy efficient and reduce carbon emissions. While it is easier to design net zero energy homes, existing home stocks are a major challenge for energy retrofitting. Two key challenges are determining the extent of retrofitting required, and developing knowledge-based effective policies that can be applied en-masse to housing stocks and neighborhoods. To overcome these challenges, it is essential to gather critical data about qualities of existing buildings including their age, geo-location, construction type, as well as electro-mechanical and occupancy parameters of each dwelling. The objective of this study was to develop a GIS-based model embedded with critical data of residential buildings to facilitate evidence-based retrofit programs for urban neighborhoods. A model based on a bottom-up approach was proposed in which information gathered from all stakeholders was inputted into one database that can be used for decision-making. A geo-located case study to validate a proposed GIS-based residential retrofitting model sample size of 74 residential buildings in the city of Riyadh was statistically analyzed and used. The results indicate behavior-based patterns, with a strong positive correlation (r = 0.606) between the number of occupants and number of household appliances, while regression analysis showed high occupancy rates do not necessarily result in high utility costs at the end of the month, and there is no statistical difference in the average monthly cost of gas between partial and fully occupied houses. Furthermore, neither the type of building, height, age, nor occupancy status play a significant role in the average energy consumed. Additionally, the GIS-based model was validated and found to be effective for energy-use mapping and gathering critical data for analyzing energy consumption patterns at neighborhood scale, making it useful for municipalities to develop effective policies aimed at energy efficient and smart neighborhoods, based on a recommended list of most effective energy-saving retrofit measures.

A Gis-Based Local Species Distribution Model and Population Monitoring of the Endangered Mission Blue Butterfly, Icaricia icarioides missionensis (Lycaenidae), at the San Francisco Peninsula Watershed

A Gis-Based Local Species Distribution Model and Population Monitoring of the Endangered Mission Blue Butterfly, Icaricia icarioides missionensis (Lycaenidae), at the San Francisco Peninsula Watershed

Applicability Comparison of GIS-Based RUSLE and SEMMA for Risk Assessment of Soil Erosion in Wildfire Watersheds

The second-largest wildfire in the history of South Korea occurred in 2022 due to strong winds and dry climates. Quantitative evaluation of soil erosion is necessary to prevent subsequent sediment disasters in the wildfire areas. The erosion rates in two watersheds affected by the wildfires were assessed using the revised universal soil loss equation (RUSLE), a globally popular model, and the soil erosion model for mountain areas (SEMMA) developed in South Korea. The GIS-based models required the integration of maps of the erosivity factor, erodibility factor, length and slope factors, and cover and practice factors. The rainfall erosivity factor considering the 50-year and 80-year probability of rainfall increased from coastal to mountainous areas. For the LS factors, the traditional version (TV) was initially used, and the flow accumulation version (FAV) was additionally considered. The cover factor of the RUSLE and the vegetation index of the SEMMA were calculated using the normalized difference vegetation index (NDVI) extracted from Sentinel-2 images acquired before and after the wildfire. After one year following the wildfire, the NDVI increased compared to during the year of the wildfire. Although the RUSLE considered a low value of the P factor (0.28) for post-fire watersheds, it overestimated the erosion rate by from 3 to 15 times compared to the SEMMA. The erosion risk with the SEMMA simulation decreased with the elapsed time via the vegetation recovery and stabilization of topsoil. While the FAV of RUSLE oversimulated by 1.65~2.31 times compared to the TV, the FAV of SEMMA only increased by 1.03~1.19 times compared to the TV. The heavy rainfall of the 50-year probability due to Typhoon Khanun in 2023 generated rill and gully erosions, landslides, and sediment damage in the post-fire watershed on forest roads for transmission tower construction or logging. Both the RUSLE and SEMMA for the TV and FAV predicted high erosion risks for disturbed hillslopes; however, their accuracy varied in terms of the intensity and extent. According to a comparative analysis of the simulation results of the two models and the actual erosion situations caused by heavy rain, the FAV of SEMMA was found to simulate spatial heterogeneity and a reasonable erosion rate.

WATER EROSION RISK AND FOOD SECURITY: A CASE STUDY

Water erosion reduces soil fertility and productivity, negatively impacting crop yields. The transported material not only affects water quality but also significantly shortens the lifespan of water infrastructure. The Catamayo-Chira transboundary basin, spanning an area of 17,199 km2, serves as the primary water source for agriculture in the Ecuador-Peru border. Ongoing sediment production within the basin has drastically decreased the volume of the Poechos reservoir, northern Peru's largest irrigation system, raising concerns about its potential complete filling in the near future. To identify vulnerable areas to water erosion, the Revised Universal Soil Loss Equation (RUSLE) was utilized. By integrating meteorological, topographic, land use, and soil type data, a GIS-based model was developed to map the spatial variation of soil loss. This approach facilitated the identification of critical areas necessitating soil conservation actions. Within the basin, approximately 65% of the land exhibits high to critical erosion due to a combination of intense rainfall, steep slopes, limited vegetation cover, and erodible soils. Soil losses of around 200 tons per year have been observed. In the absence of effective soil loss management practices, sediment production, and transportation continue to pose significant challenges for the expansive agricultural lands in northern Peru, thereby jeopardizing regional food security.

Integrated study of GIS and Remote Sensing to identify potential sites for rainwater harvesting structures

Water accessibility is necessary for agricultural and socioeconomic development in the area. One of the best solutions to the water crisis in the Kohat region is to build rainwater harvesting structures (RWH) to collect water from local watersheds for artificial groundwater recharge. Our main objective was to identify suitable RWH locations in the study area. However, the optimum site selection for RWH in regional areas is crucial. In such a case, geospatial techniques have revealed decisive in optimum site selection for RWH structures. In the current study, we have used GIS-based models like the Analytical Hierarchy Process (AHP) and Weight of Evidence (WOE) to identify suitable sites for rainwater harvesting. Seven influential parameters, i.e., LULC, rainfall, drainage density, slope, lineament density, soil, and lithology, were considered, reclassified, and assigned ranks according to literature for analysis to produce potential site maps for RWH structures. The findings of the current study indicate that, based on the Analytic Hierarchy Process (AHP), 44% (1320 km2) of the study area is classified as highly suitable for rainwater harvesting, while 40% (1200 km2) is considered moderately appropriate, and 16% (470 km2) is deemed unsuitable. In contrast, when utilizing the Weight of Evidence (WOE) approach, the high suitability class encompasses 32% (956.8 km2) of the area, the moderate suitability class covers 43% (1285.7 km2), and the low suitability class accounts for 25% (748 km2) of the study area. The outcome of the current study aims to contribute and assist the planner, manager, and officials in finding sites for RWH structures to overcome the water table, irrigation demand, and agricultural issues in the study area.

Optimizing locations of emergency medical stations for rural areas: A case study in Iran

Given the dearth of emergency medical services in rural areas, offering these services is among the most important things that should be done right to save the lives of people who have suffered severe injuries. Rapid access to EMSs, which includes a shorter response time and the availability of all intensive care services, including the ICU, CCU, NICU, and PICU, is crucial in this regard. In this regard, a GIS-based ACO-QAP model is proposed for rural areas to optimize locations for EMS stations. This model takes into account the standards of EMS in rural areas, and the standards of out-of-city roads, reducing the response times from EMS stations to the scene of damage and from the damage scene to the closest hospital that offers the required intensive care services. Additionally, to increase the availability of intensive care services and to optimal use of available resources, the model determines which types of intensive care services should be provided in the EMS stations. As a result of the integration for setting up the out-of-city EMS, 614 optimized locations were identified. The identified EMS stations should provide single or multiple intensive care services. There were identified as 544 (88.6%) multiservice stations and 70 (11.4%) single service stations. Among the multiservice stations, 113 (20.8%) stations were identified as two-service, 150 (27.5%) as three-service, and 281 (51.7%), as four-service stations. This model generates a chain of EMS stations that improves spatial accessibility and emergency medical availability while reducing resource usage.

A GIS-based Binary Logistic Regression Model for the Inundation Analysis; A Case Study on Elapatha DS Division, Ratnapura District, Sri Lanka


 
 
 Flood susceptibility analysis (FSA) is a prerequisite for adopting flood mitigation and adaptation techniques. A number of technologies and models have emerged through time, and this study focuses on the Binary Logistic Regression Model (BLRM) to investigate flood vulnerability of Elapatha Divisional secretariat (DS), Ratnapura District, Sri Lanka where has high impact from inundation. Rainfall, land use and land cover (LULC), elevation, slope, slope aspect, distance to the river, topographic wetness index (TWI), and stream power index (SPI) were the factors used in the model construction. These components were investigated in terms of their contribution to flood susceptibility using all location data and field plotting of responsible parameters in the study area using Geographic Information System (GIS) software, and all extracted data points were 96489, of which 50% were used for BLRM development using SPSS statistical software and remaining 50% for model validation. The coefficient of rainfall parameters, log value of elevation in meters, the tan value of slope in degrees, radiant value of aspect in degrees, (distance from the river)0.1, the ratio between SPI and TWI (SPI/TWI) and LULC band values of built-up area, water bodies and vegetations were 0.023, -2.254, -1.018, -0.005, -0.164, -0.003, 2.707, -.067 and -0.004 respectively. The accuracy was validated using Mean standard error (MSE) and area under curve (AUC) analysis, with values of 0.031 and 0.724 respectively. The Built-up area, elevation, and slope had the most impact on the inundation Elapatha DS division, and model performance represents 72.4% accuracy. Therefore, mitigation of inundation problems can be achieved through proper landscaping in the area.
 Keywords: Binary logistic Regression, Flood susceptibility Analysis, GIS software, Spatial analysis
 
 

Estimation of Water Yield and Soil Erosion in Samanalawewa Watershed in Sri Lanka using GIS-based InVEST Model

Ecosystem services (ES) are a group of tasks performed by the earth's ecosystems that are required to support life and offer benefits to humanity. Thus, this study explores two of the water-related ecosystem services, water yield, and soil retention that were distributed in the Samanalawewa Watershed (SW) and its eight sub-watersheds in Sri Lanka, over 20 years (2000-2020) with five- year time intervals. Soil and water conservation and water yield play an important role in ecosystem management. Hydrological balance is a crucial component of the hydrological ecosystem services. Assessing water yield and soil erosion are pivotal for watershed management. Thus, this study estimated and mapped the water yield and soil erosion, using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST 3.9.2) Annual Water Yield (AWY) and Sediment Delivery Ratio (SDR) model respectively. According to the obtained results, in 2000, 2005, 2010, 2015 and 2020, the estimated mean annual water yield was 2.62x105, 2.6x105, 3.5x105, 3.58x105 and 2.75x105 m3 ha-1year-1, respectively. Furthermore, the estimated mean annual soil erosion rates for the above- mentioned years were 53.2, 52.9, 69.7, 87.7 and 70.2 t ha-1 year-1. The estimated soil loss values of the SW are 10 to 18 times greater than the soil erosion tolerance (5 t ha-1 year-1) in Sri Lanka. The results show that the years 2015 and 2005 have the highest and the lowest water yield and total soil loss values, respectively. The upper part of the watershed has relatively moderate water yield and soil loss values than the lower part. These findings would be beneficial in developing watershed management strategies and in the implementation of suitable soil and water conservation techniques within the watershed. 
 Keywords: Ecosystem services, InVEST, Samanalawewa watershed, Soil erosion, Water yield

Assessing the Spatial Variability of Soil Quality Index of Ganjigatti Sub-Watershed Using GIS-Based Geostatistical Modeling

Assessing the Spatial Variability of Soil Quality Index of Ganjigatti Sub-Watershed Using GIS-Based Geostatistical Modeling

A Conceptual Framework for Modeling Spatiotemporal Dynamics of Diesel Attenuation Capacity: A Case Study across Namyangju, South Korea

The vadose zone acts as a natural buffer against groundwater contamination, and thus, its attenuation capacity (AC) directly affects groundwater vulnerability to pollutants. A regression model from the previous study predicting the overall AC of soils against diesel was further expanded to the GIS-based overlay-index model. Among the six physicochemical parameters used in the regression model, saturation degree (SD) is notably susceptible to climatological and meteorological events. To accommodate the lack of soil SD historical data, a series of infiltration simulations were separately conducted using Phydrus code with moving boundary conditions (i.e., rainfall records). The temporal variation of SD and the resulting AC under transient conditions are captured by building a space–time cube using a temporal raster across the study area within the designated time frame (1997–2022). The emerging hot spot analysis (EHSA) tool, based on the Getis–Ord Gi* and Mann–Kendall statistics, is applied to further identify any existing pattern associated with both SD and AC in both space and time simultaneously. Under stationary conditions, AC decreases along depth and is relatively lower near water bodies. Similarly, AC cold spot trends also show up near water bodies under transient conditions. The result captures not only the trends across time but also shows the exact location where the changes happen. The proposed framework provides an efficient tool to look for locations that have a persistently low or a gradually decreasing ability to attenuate diesel over time, indicating the need for stricter management regulations from a long-term perspective.

Assessing Groundwater Recharge in the Wabe River Catchment, Central Ethiopia, through a GIS-Based Distributed Water Balance Model

The utilization of groundwater has emerged as an indispensable asset in facilitating economic advancement, preserving ecological integrity, and responding to the challenges posed by climate change, especially in regions characterized by aridity and semi-aridity. The sustainable management of water resources requires an assessment of the geographical and temporal patterns of groundwater recharge. The present study employed the GIS-based WetSpass-M model to model the water balance components by utilizing hydro-meteorological and biophysical data from the Wabe catchment, which spans an area of 1840 km2 in central Ethiopia, for a long time. The objective of this study was to assess the long-term average annual and seasonal groundwater recharge for the catchment area utilizing the WetSpass-M model. The input data were collected through remote sensing data and surveys in the field. The model was employed to gain insights into the process of groundwater recharge in a particular region and to facilitate effective management, prudent utilization, and sustainable planning of water resources in the long run. Water balance components were estimated using seasonal fluctuations in evapotranspiration, surface runoff, and groundwater recharge. The Wabe catchment’s summer, winter, and mean long-term yearly groundwater recharge were determined to be 125.5 mm, 78.98 mm, and 204.51 mm, respectively. The model indicates that summer seasons account for 86.5% of the mean annual precipitation, while winter seasons account for 13.5%. On the other hand, the groundwater system percolates 14.8% of the total annual rainfall (1374.26 mm). While evapotranspiration accounts for 51% of total precipitation and surface runoff accounts for 34.1%, the Wabe catchment’s mean annual evapotranspiration and surface runoff values are simulated at 701.11 mm and 485.58 mm, respectively. The findings suggest the use of the WetSpass-M model to precisely calculate the water balance components within the Wabe catchment.

Groundwater delineation for sustainable improvement and development aided by GIS, AHP, and MIF techniques

Exploration of groundwater is an integral part of viable resource growth for society, economy, and irrigation. However, uncontrolled utilization is mainly reported in urban and industries due to the increasing demand for water in semi-arid and arid regions of the world. In the background, groundwater demarcation for potential areas is vital in meeting necessary demand. The current study applied an integrated method comprising the analytical hierarchy process (AHP), multiple influence factors (MIF), combined with a linear regression curve and observatory well data for groundwater prospects mapping. Thematic maps such as flow direction, flow accumulation, elevation map, land use land cover, slope, soil texture, hill shade, geomorphology, normalized vegetation index, and groundwater depth map were generated utilizing remote sensing techniques. The relative weight of each parameter was estimated and then assigned to major and minor parameters. Potential zones for groundwater were classified into five classes, namely very good, good, moderate, poor, and very poor, based on AHP and MIF methods. A spatially explicit sensitivity and uncertainty analysis method to a GIS-based multi-criteria groundwater potential zone model is presented in this research. The study addressed a flaw in the way groundwater potential mapping results are typically presented in GIS-based multi-criteria decision analysis studies, where discrete class outputs are used without any assessment of their certainty with respect to variations in criteria weighting, which is one of the main contributors to output uncertainty. The study region is categorized based on inferred results as very poor, poor, marginal, and very good in potential ground quality 3.04 km2 is considered extremely poor, 3.33 km2 is considered poor, 64.42 km2 is considered very good, and 85.84 km2 is considered marginal zones, which shows reliable and potential implementation. The outcomes of AHP and MIF were validated by linear regression curve and actual water table in a study area. The study results help to formulate the potential demarcation of groundwater zones for future sustainable planning and development of groundwater sources. This study may be helpful to provide a cost-effective solution to water resources crises. The current study finding may be helpful for decision-makers and administrative professionals for sustainable management of groundwater resources for present and future demands.

Impacts of sustainable management on the spatial distributions of erosion susceptibility and probable sediment yield in a mixed-forested watershed

Forest management practices play multifaceted roles in enhancing the geophysical properties that affect raindrop erosion in the watershed, and consequently, sediment deposition in the reservoir. The current work attempts to integrate empirical and physically-based modeling approaches to quantify the impacts of forest conservation on erosion risk and potential sediment accumulation in the mixed-forested Ogouchi Dam watershed in Japan. The reliability of the empirical model for estimating the total erodibility coefficient (TEr), as a function of various forest properties, was evaluated by applying the mathematical expression to multiple forest conditions and comparing the values to field-measured soil erosion rates. The spatial distribution of the empirically derived values showed that about 25.8% of the Government-managed forests and 45.1% of the private forests have higher risks of raindrop splash erosion compared to natural forests. The TEr value in each small Government-divided forest land (less than 5 ha) was then corresponded to the MUSLE management practice factor (MUSLE P) input in each hydrologic response unit (HRU) in the Soil and Water Assessment Tool (SWAT) model to create a sediment yield distribution map and to predict the amounts of sediment accumulation for different management scenarios. The spatial distribution of sediment yield for the base condition showed that 20.9% of the Government-managed forests and 61.6% of the private forests have higher probable amounts of sediment yield relative to the value simulated in the natural forest. A maximum cumulative sediment reduction of about 14.4% is likely attainable upon the complete control of the Government in the entire planted forest area. Overall, this study effectively utilized the field survey datasets to develop a robust empirical model for quantifying erosion risk and was able to couple the results to a GIS-based model that predicts the amounts of sediment yield under varying environmental conditions.

The effect of land use and land cover changes on soil erosion in semi-arid areas using cloud-based google earth engine platform and GIS-based RUSLE model

The effect of land use and land cover changes on soil erosion in semi-arid areas using cloud-based google earth engine platform and GIS-based RUSLE model