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- Correction: The performance of landslides frequency-area distribution
analyses using a newly developed fully automatic tool-
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PubDate: 2024-08-05
DOI: 10.1007/s12518-024-00583-6
- The effect of spatial lag on modeling geomatic covariates using analysis
of variance-
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Abstract: Abstract In recent years, statistical methods have been developed that include spatial considerations, for example, those that incorporate data with georeferencing. The descriptive part of geographical information systems currently provides many visualization and analysis tools; however, in terms of analysis, these systems are still quite limited, therefore, ignorance of these limitations may result in data with spatial effects being treated with conventional statistical methods for non-spatial use, which can certainly invalidate the excellent work of data capture with advanced tools such as those that are used daily in the geomatic context. This prompted the current document, drawing attention to how geomatic information analyzed with statistical methods that imply independence in modeled observations can be invalid. The Moran index is compared with a proposal for a spatial lag coefficient in the context of experimental design so that users of variance analysis do not apply this well-known procedure in a ritualistic way, perhaps revising some assumptions and perhaps ignoring more important ones. The distortion of the p value generated from the analysis of variance is clear in the presence of spatial dependence. In this case, it is associated with the lag or spatial overlap. The methodology is easy to apply in other designs with the development of the design matrix, its reparameterization and the choice of the respective weight matrix. This may cause users to reconsider the traditional method of analysis and incorporate some appropriate analysis methodology to address spatial effects present in data or in outputs from the modeling process.
PubDate: 2024-07-22
DOI: 10.1007/s12518-024-00579-2
- Spatial assessment of groundwater potential zones using remote sensing,
GIS and analytical hierarchy process: A case study of Siliguri
subdivision, West Bengal-
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Abstract: Abstract One of the most significant natural resources, groundwater is essential to providing a long-term, reliable and sustainable global water supply. Therefore, delineating Groundwater potential zones (GWPZs) is crucial in effectively managing groundwater reserves. The present study attempts to delineate GWPZs in the Siliguri subdivision of West Bengal using integrated Remote Sensing (RS), Geographic Information System (GIS) and Analytical Hierarchy Process (AHP) in the light of a considerable shift in the patterns of groundwater usage, especially considering the ongoing rise in demand for groundwater owing to a variety of causes. Raster layers of fourteen causative factors Viz. geomorphology, lithology, lineament density, soil texture, elevation, slope, land use and land cover (LULC), river density, rainfall, pre-monsoon groundwater depth, post-monsoon groundwater depth, groundwater fluctuation, topographic wetness index (TWI) and topographic roughness index (TRI) are used to delineate GWPZs using AHP in GIS software. The final GWPZs map was categorised into five classes: very high (25.67%), High (31.77%), moderate (20.73%), low (17.67%) and very low (4.15%). The results are further validated by evaluating the receiver operating characteristic (ROC) curve with the groundwater level depth from 39 dug wells. The ROC curve shows that the AUC value is 0.818, representing a prediction accuracy of 81.80%. The comprehensive map of GWPZs will enhance managing natural assets to guarantee the continued preservation of water resources and the development of agriculture. The method utilised in this research may be used in other natural contexts with a comparable environment.
PubDate: 2024-07-19
DOI: 10.1007/s12518-024-00577-4
- Flood susceptibility mapping using machine learning and remote sensing
data in the Southern Karun Basin, Iran-
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Abstract: Abstract Floods in Iran lead to significant human and financial losses annually. Identifying flood-prone regions is imperative to minimize these damages. This study aims to pinpoint flood-susceptible areas in the Great Karun Plain using remote sensing data, Google Earth Engine (GEE), and machine learning techniques. For the analysis, Landsat 8 data from April 8, 2019, and multiple variables including actual evapotranspiration, aspect, soil bulk density, clay content, climate water deficit, elevation, NDVI, land cover, Palmer Drought Severity Index, reference evapotranspiration, precipitation accumulation, sand content, soil moisture, minimum temperature, and maximum temperature were employed. These variables were utilized in the machine learning process to establish flood susceptibility zones. During the machine learning process, the base flow data of the Karun River was extracted from the Landsat image. A total of 19,335 samples were incorporated into the machine learning procedure using techniques such as MARS, CART, TreeNet, and RF. The model assessment criteria encompassed ROC, sensitivity, specificity, overall accuracy, F1score and mean sensitivity. Results indicated that the TreeNet technique yielded the most promising outcomes among the machine learning algorithms with ROC values of 0.965 for test data. The characteristic criterion reached 91.2%, while the overall accuracy criterion stood at 91.12%. The model’s average sensitivity was 90.81%, and F1score was 63.51% for this technique. Additionally, analysis of the relative importance of independent variables highlighted that factors like vegetation cover (0.37), cumulative precipitation (0.23), soil water deficit (0.12), drought intensity (0.12), and landscapes (0.1) exerted a more pronounced influence on flooded areas compared to other variables.
PubDate: 2024-07-19
DOI: 10.1007/s12518-024-00582-7
- Sequential Gaussian simulation for mapping the spatial variability of
saturated soil hydraulic conductivity at watershed scale-
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Abstract: Abstract The saturated soil hydraulic conductivity (Ksat) exhibits high spatial variability due to the various physical, chemical, and biological processes that act simultaneously with different intensities in soil formation. The use of geostatistics as a tool to study soil heterogeneity facilitates the understanding of the spatial variability of Ksat. This study aimed to simulate the spatial variability of Ksat and evaluate its uncertainties using sequential Gaussian simulation (SSG) in a tropical watershed located in southern Brazil. Soil sampling was conducted in an experimental watershed-scale grid with a sample spacing of 300 m, and Ksat was analyzed. Descriptive statistics were applied to assess the behavior of Ksat spatial variability, followed by geostatistical analysis, specifically SSG. Variogram parameters were defined, and SSG was used to generate 100 equiprobable random fields. The results showed that Ksat in the Santa Rita watershed (SRW) is heterogeneous, and uncertainties among the hundred fields ranged from 58.70 to 81.10 mm h-1 for the 5th and 95th percentiles, respectively, possibly influenced by soil type, land use, density, and texture. The criteria for validating SSG simulation were met and successfully described the spatial continuity of Ksat in the SRW. Thus, SSG proved to be an effective tool for understanding the magnitude and structure of Ksat spatial variability at the watershed scale, contributing to effective soil and water management in the SRW.
PubDate: 2024-07-18
DOI: 10.1007/s12518-024-00580-9
- Geoinformatics and Analytic Hierarchy Process (AHP) in modelling
groundwater potential in Obudu Plateau, Southeastern Nigeria Bamenda
Massif-
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Abstract: Abstract Water is a vital resource used in effective sanitation, hygiene, drinking and agricultural uses. This study was in Obudu Plateau covering an area of 3,053.08km2. Analysis of remote sensing, geographic information system (GIS), and global positioning system (GPS) data obtained from satellite imageries and digital elevation model (DEM) assessed groundwater potential. The model was validated using borehole data in the area. Thematic layers of geology, lineament density, slope, geomorphology, land use and land cover and drainage density were integrated using GIS software. Multicriteria evaluation of the layers was by analytic hierarchy process (AHP). Pairwise comparison matrix shows consistency the consistency ratio is 0.07 or 7%. This shows the comparison of groundwater controlling factors is within acceptable limit of consistency. Overlay analysis produced groundwater potential map classified into five zones of very high 2.66% (81.31km2), high 6.92% (211.38km2) very low 9.60% (292.69km2), moderate 46.95% (1,433.46 km2) and low 33.87% (1,034.23km2). Structural geological setting determines largely the suitability of an area to groundwater occurrence. Overlaying each thematic layer with lineament density map produced a more credible groundwater potential model compared to preceding related works. This method is suitable for both local and regional groundwater development.
PubDate: 2024-07-13
DOI: 10.1007/s12518-024-00565-8
- The performance of landslides frequency-area distribution analyses using a
newly developed fully automatic tool-
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Abstract: Abstract Frequency-Area Distribution (FAD) analyses were introduced to landslides research since the early 2000’s. This technique is a powerful tool that allows assessing the completeness of landslide inventory maps (LIM), used to build both landslides susceptibility and landslides hazard assessment models. However, FAD analyses are not commonly used in such studies despite the significant potential of the technique. The long processing steps needed to generate FAD curves, which involve logarithmic binning and iterative model fitting using various statistical tools, constitutes an energy and time-consuming task that pushes many researchers away from using the technique. In fact, no fully automatic tool capable of generating FAD curves and models exists as of July 2023. Therefore, we attempt to provide a fully automatic computer program capable of binning, fitting FAD curves and assessing their goodness of fit to theoretical models in a fully automatic, one step process. An example is provided using real data from Taounate province, Northern Morocco, so as to demonstrate the ability of the tool to deal with exhaustive datasets. In addition, the Kolmogorov-Smirnov, goodness of fit test is added to provide an objective assessment of the data fitting, which constitutes a better alternative to the subjective visual assessment that most landslides researchers rely on. To sum up, we believe that this tool will help popularize the FAD technique, which will consequently improve the accuracy and objectivity of landslides risk and hazard assessment disciplines.
PubDate: 2024-07-12
DOI: 10.1007/s12518-024-00581-8
- Groundwater potential recharge assessment in Southern Mediterranean basin
using GIS and remote sensing tools: case of Khalled- Teboursouk basin,
karst aquifer-
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Abstract: Abstract In the Khaled-Teboursouk basin (Southern Mediterranean Basin), karstic aquifers are the main sources of drinking and irrigation water. They play a crucial role in the socio-economic development of the region. Therefore, the estimation of groundwater recharge is necessary for a good management of water resources, while considering the impacts of climate change. The present study utilizes the application of APLIS method integrated with Geographic Information System (GIS) as a remote sensing technique for geospatial analysis to explore groundwater recharge areas along Khalled-Teboursouk basin, expressed as a percentage of precipitation combined with numerous parameters. The morphology of earth surface features such as Altitude (A), Slope (P), Lithology (L), infiltration (I), and Soil (S) influence the groundwater recharge rate in carbonate aquifers, from the infiltration of rainfall in aquifers in either direct or indirect way. The results revealed that 60–80% of precipitation is identified as high potential for groundwater recharge and it is associated with karstified limestones of Eocene lower age. The gentle slope areas in the Middle-East and Central parts have been moderate potential for groundwater recharge 40–60% of precipitation and they are associated with karstified limestone of Campanian-Maastrichtian age (Abiod Fm.). Hilly terrains with low and very low recharge are the most represented for groundwater recharge processes. They are associated with areas of non-karstified rocks and Quaternary deposits. The dominant water type of the groundwater in this area is Ca–Mg–Cl–SO4 water type. The Total Dissolved Solids (TDS) of these waters (0.37 to 3.58 g/l) are slow in the recharge area and high in the discharge area. This is caused by rapid circulation of water from the recharge areas to the discharge points. The aquifers have been recharged by rainfall originating from a mixture of Atlantic and Mediterranean vapor masses. The isotope analyses, δ18O and δ2H ranged from − 6.8 to -5.3‰ (vs. SMOW) and from − 42 to -4‰ (vs. SMOW) respectively, confirm the recent recharge of these carbonate aquifers.
PubDate: 2024-07-12
DOI: 10.1007/s12518-024-00573-8
- Effect of neighbourhood and its configurations on urban growth prediction
of an unplanned metropolitan region-
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Abstract: Abstract Rapid urbanisation, especially in developing countries like India, has resulted in unplanned and haphazard urban expansion. With saturated urban cores, growth is observed in the peri-urban areas, resulting in severe challenges for urban planners. The present study aims to study the urban growth patterns of the fast-growing Mumbai Metropolitan Region (MMR) using the Landsat data from 1999 to 2019 and to evaluate the neighbourhood configurations’ effect on urban growth prediction. The urban area maps are classified using a maximum likelihood algorithm and are used along with the potential drivers to test three levels of neighbourhood considerations. The first model assumes no neighbourhood effect, the second incorporates the built-up pixels in the neighbourhood as an additional potential driver variable, and the third uses a Cellular Automata (CA). The CA model explores variations in neighbourhood types and sizes, distance decay and iterations to identify the optimal configuration. The results show an 89.44% increase in built-up areas over two decades (1999-2019). The urban growth prediction model testing reveals the importance of neighbourhood, with the first model without neighbourhood consideration giving the least accuracy (67%) while the inbuilt neighbourhood model gives better results (71%). However, the CA-based model with a 9 × 9 Moore neighbourhood, distance exponent β = 2 and two iterations give the highest accuracy (76%). The growth prediction shows a new wave of peri-urban growth in MMR, with overall urban areas increasing by 25% between 2019 and 2029 and 20% between 2029 and 2039. The results provide urban planners with a valuable tool for informed decision-making and promoting sustainable development.
PubDate: 2024-07-10
DOI: 10.1007/s12518-024-00566-7
- Morphometric analysis and prioritization of sub-watersheds of the Inaouene
River upstream of the Idris I dam using the GIS techniques-
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Abstract: Abstract The prioritization of watersheds has increasingly become an optimal and relevant approach for the management and planning against natural hazards. This approach is based on the morphometric analysis of the watersheds according to some parameters and indicators. In this study, we adopted this approach using Geographic Information System (GIS) techniques to identify the priority sub-watersheds of the Inaouene River upstream of the Idris I dam. This watershed, which is part of the Sebou watershed with an area of approximately 3608.2 km2, is made of up 38 sub-watersheds and an area of gulleys. The results showed that 57.89% of the Inaouene River’s sub-watersheds have high to very high priority. The most important ones are Lahdar, El Melah 1, Gherghab, Larbaâ, and Mezwarou watersheds. By unveiling the distinctive morphometric characteristics of the watershed, this study enhances our understanding of its hydrological behavior, while providing crucial data to support soil and water conservation measures. This ensures sustainable agriculture, preserves water quality, and prevents sedimentation in the Idris I dam.
PubDate: 2024-07-09
DOI: 10.1007/s12518-024-00574-7
- Analyzing effects of environmental indices on satellite remote sensing
land surface temperature using spatial regression models-
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Abstract: Abstract Land Surface Temperature (LST) is a vital satellite remote sensing-driven indicator of earth heat studies. LST can provide information about urban heat emission, urban climate, and human activities in urban areas. In recent years, the calculated LST for a satellite image pixel has been studied as a parameter affected by urban environment factors such as available land cover types in the same pixel. However, in this study, a scenario in which the calculated LST for a pixel is not only affected by the factors in the same pixel but also by the factors in the neighbor pixels is studied. Firstly, required maps for the calculated LST and influential factors (indicators of vegetation, building, and water surfaces) are produced from satellite remote sensing images. Secondly, the relationship between the LST and influential factors is modeled using the Ordinary Least Squares (OLS) model. Thirdly, Moran’s I and Lagrange Multiplier tests are used to analyze the existence of spatial dependency and correlation in residuals of the OLS model. Fourthly, three spatial regression models (Spatially Lagged X (SLX), Spatial Lag (SL), and Spatial Error (SE) models) are used to model the spatial dependency and correlation between the LST and influential factors. Finally, the outcomes of the models are compared and evaluated. Results present related maps for the variables besides maps for spatial residuals in the spatial regression models. The outcomes of the models are investigated by p-values, log-likelihood, and RMSE. To conclude, the spatial regression models fitted the relation between the dependent and independent variables better than the OLS model.
PubDate: 2024-07-09
DOI: 10.1007/s12518-024-00568-5
- Combining satellite data and artificial intelligence with a crop growth
model to enhance rice yield estimation and crop management practices-
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Abstract: Abstract Rice is the staple food of more than half of the world’s population, especially in Asia, where rice provides more than 50% of the caloric supply for at least 520 million people, most of them are either extremely impoverished or poor. Information on rice production is thus essential for agricultural management and the formulation of food security policies. The objective of this research is to develop an approach combining remote sensing and artificial intelligence (AI) techniques with a crop growth model for enhancing yield estimation and crop management in Taiwan. The data processing involves three main steps: (1) data pre-processing to generate model inputs, (2) crop yield modeling through assimilating satellite-derived leaf area index (LAI) into a crop growth model using the AI particle swarm optimization (PSO) algorithm, and (3) model validation. The assimilation process was performed using a cost function based on the difference between remotely-sensed and simulated LAI values. The optimization process began with an initial parameterization and appropriately adjusted input parameters in the model. The fitness value derived from a cost function was determined using the PSO. The results of yield estimates obtained from the crop growth model based on optimized inputs were evaluated using the government’s yield statistics, revealing close agreement between these two datasets. The root mean square percentage error (RMSPE) and the mean absolute percentage error (MAPE) for the first crop were 19.8% and 17.1%, and the values for the second crop were 8.4% and 6.3%, respectively. The relative percentage error (RPE) values of 18.5% and − 5.1%, respectively, showed a slight overestimate and underestimate for the first and second crops.
PubDate: 2024-07-09
DOI: 10.1007/s12518-024-00575-6
- Detection of land subsidence using hybrid and ensemble deep learning
models-
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Abstract: Abstract Land subsidence (LS) is among the most prominent forms of subsurface erosion and geomorphological hazards. This study used two deep learning (DL) models consisting of the hybrid CNN-RNN and ensemble DL (EDL) merged with two dense models. The main variables controlling LS (consisting of environmental, hydrological, hydrogeological, digital elevation model, and soil characteristics), were used as the input for the predictive DL models. Likewise, to establish the degree of performance of each parameter, different control points have been established. We then trained and tested our DL models using the receiver-operating characteristic-area under curve (ROC-AUC) and precision-recall plots. The measures based on the game theory consisting of permutation feature importance measure (PFIM) and SHapley Additive exPlanations (SHAP) were employed to assess the features relative importance and interpretability of the predictive model output. Our findings show that the ensemble CNN-RNN model performed well with the ROC-AUC curve (0.95) of class 1 (land subsidence) for training data for detecting and mapping land subsidence compared to EDL with the ROC curve (0.93) of class 1 (land subsidence) for training datasets. The CNN-RNN also performed well with the precision-recall curve (0.954) of class 1 for testing data for detecting and mapping land subsidence compared to the EDL model with the precision-recall curve (0.95) of class 1. The results of this research revealed that much of the study area is susceptible to land subsidence. The results of the model sensitivity analysis suggested that the groundwater drop rate is the most sensitive for the model. Based on the SHAP values, the groundwater drop rate was identified as the most contributed feature to the model output. The importance of this study is at a broader level, especially in arid and semiarid environments with similar geomorphological, and climatic conditions.
PubDate: 2024-07-08
DOI: 10.1007/s12518-024-00572-9
- 3D point cloud reconstruction using panoramic images
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Abstract: Abstract Panorama photogrammetry, the process of analyzing panoramic images, has gained popularity in close-range photogrammetry for 3D reconstruction over the past decade. Initially, researchers utilized cylindrical or spherical panoramic images created through specialized cameras or conventional ones with rectilinear lenses. However, these methods were hindered by the high cost of panorama equipment and the need for manual reconstruction. Consequently, there's a growing demand for automated algorithms capable of reconstructing 3D point clouds from stitched panorama images. This study aims to provide a cost-effective solution for automatic 3D point cloud reconstruction from panoramas. The study is divided into two parts; it first outlines an image acquisition strategy for capturing overlapping perspective images to facilitate accurate panorama generation. Subsequently, it introduces an automated algorithm for 3D point cloud reconstruction from panorama images. The process utilizes the KAZE feature detector for feature detection and introduces a novel feature matching approach for matching panorama images. Accuracy assessment of the reconstructed 3D point clouds was done using three methods: Line Segment Based approach, yielding RMSE errors of 34.2mm and 39mm for dataset-1 and dataset-2 respectively, No-Reference 3D Point Cloud Quality Assessment, resulting in quality scores of 8.5939 and 7.4535 for dataset-1 and dataset-2 respectively, and M3C2 distance method computed value of 0.091059 and 0.165179 respectively, indicating high quality of the generated point clouds.
PubDate: 2024-07-06
DOI: 10.1007/s12518-024-00563-w
- Spatial analysis and extent of soil erosion risk using the RUSLE approach
in the Swat River Basin, Eastern Hindukush-
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Abstract: Abstract Soil erosion is a severe issue posing a number of adverse effects on the environment. It is a prominent hazard damaging the fertile agricultural land. Therefore, in this study, a spatio-temporal assessment of soil erosion was carried out in the Swat River Basin, Pakistan by employing the Revised Universal Soil Loss Equation (RUSLE). The parameters of the RUSLE model are rainfall erosivity, soil erodibility, slope length and steepness, land management and support practice. These factors were developed from monthly mean rainfall data obtained from the Regional Metrology Department Peshawar, FAO soil database, land use data prepared from Landsat-5 and 8 satellite imageries, topographic data obtained from the ALOS PALSAR Digital Elevation Model (DEM). The analysis discovered that 13% of the study area experienced severe erosion. Results of the spatial distribution and vulnerability to erosion within the Swat River Basin have been categorized into different zones such as very low (59.7%), low (19.5%), moderate (5.37%), high (6.86%), and very high (5.96%). These results accentuate the necessity for mitigation measures in the study area to mitigate the loss of valuable topsoil. This research possesses the potential to offer valuable insights into decision-making and planning to reduce the risk of erosion.
PubDate: 2024-07-05
DOI: 10.1007/s12518-024-00567-6
- Use of UAV imagery for land consolidation: analysis of the accuracy of the
resulting orthophotomosaic in relation to the GNSS RTK measurement-
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Abstract: Abstract Land consolidation projects are fundamental tools that enable the reorganization of agricultural space to enhance agricultural productivity and improve quality of life in rural areas. However, the high costs associated with such projects necessitate ongoing refinement of their technical aspects, including cost reduction and shortened implementation time while maintaining the required accuracy parameters. This study aimed to assess the accuracy of digital orthomosaic creation obtained using UAVs from the perspective of the implementation of land consolidation projects. The research area is located in southern Poland (Przeginia village), and the data used for the study were obtained during the ongoing land consolidation project. The processing of the resulting images was performed with Structure from Motion algorithms using 103 adjustment points with known coordinates. An analysis performed using a set of 87 control points showed an average error in the position of points on a surface of 0.08 m in relation to control results carried out using the GNSS RTK technique. The observed maximum error value was 0.29 m. The analysis of the causes of the high value of observed errors indicates that they were the result of an incorrectly planned, too low number of control points and their uneven distribution across the study area.
PubDate: 2024-07-05
DOI: 10.1007/s12518-024-00576-5
- The agro-ecological capacity of north and central Sistan-Baluchestan
Province, Iran, for canola cultivation determined by GIS and analytical
hierarchical process-
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Abstract: Abstract Identification of potential lands on the basis of their environmental benefits and constraints can greatly contribute to the stability of canola production in different parts of the world including Sistan-Baluchestan province, Iran. Accordingly, using GIS, the scores of environmental factors affecting canola production including temperature, rainfall, slope, altitude, organic matter, soil salinity, pH and soil nutrients (N, P, K, Fe and Zn) were integrated along with the weights of analytical hierarchical process (AHP) for the production of canola suitability maps. The zoning maps of climate, topography, and soil as well as the canola suitability maps and the current production maps of canola were prepared. According to the AHP results canola cultivation was affected the most by climate (rainfall and water sources) compared with topography and soil. ArcGIS results indicated southern lands of Zahedan had the highest organic matter, and excluding Hirmand, other parts of the area had appropriate salinity for canola production. The most appropriate areas in terms of acidity for canola production are Hirmand and the central part of Zahedan. In arable soil, the nitrogen level was not maximum in the region, and Nimrooz and Zabol had the highest phosphorus. Potassium was average in the research area, and Zabol, Zahak, Nimrooz and Hamoon had the highest Fe. The output maps obtained from the combination of various ecological factors indicated that the moderate and non-suitable classes of land for canola cultivation are located in the northern parts of Zahak, Hamon, Nimzoz and the total lands of Hirmand and Zabol.
PubDate: 2024-07-04
DOI: 10.1007/s12518-024-00571-w
- Mapping the thermal footprint of a municipal solid waste landfill using
remote sensing and artificial intelligence-
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Abstract: Abstract This work demonstrates the value of combining remote sensing, regression models, random forest (RF) algorithms, and artificial neural networks (ANN) to provide crucial information for landfill management in Jordan. The process of predicting land surface temperature (LST) using linear and nonlinear regression models, ANN, and RF depended on past LST time series retrieved from Landsat images for the years 2000 to 2018. Additionally, the study utilized the normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), as well as data on humidity, wind velocity, and ambient air temperature. The deployed ANN model exhibited a coefficient of determination of 0.87 and a mean squared error of 6.40*10^-8. Similarly, the RF model accurately identified 93.88% of the LST values. The findings revealed that the LST at landfills was consistently higher than the summer air temperature, and that the LSTs of open landfill cells exceeded those of closed cells. Moreover, the predicted LST values from ANN and RF models surpassed those from linear and nonlinear regression models. Notably, the R^2 value of 0.81 indicates a strong correlation between ANN and RF findings.
PubDate: 2024-07-03
DOI: 10.1007/s12518-024-00570-x
- Estimating future bathymetric surface of Kainji Reservoir using Markov
Chains and Cellular Automata algorithms-
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Abstract: Abstract The menace of sedimentation to reservoirs has a significant implication for water quality, storage capacity and reservoir lifetime. Rainfall patterns and other anthropogenic and environmental impacts alter the erosion rate and, by extension, directly affect sedimentation rates if left unchecked. This research focused on using the integration of Markov Chains and Cellular Automata (MC – CA) models to estimate and forecast the future bathymetric surface of the Kainji reservoir in Nigeria for the year 2050. The bathymetric datasets used for this research comprise two different epochs (1990 and 2020). The datasets were acquired using a Single Beam Echosounder at Low and High frequencies of 20 kHz and 200 kHz. The preliminary investigation revealed that sedimentation is exacerbating a greater danger to the reservoir functionality. The results show that the maximum observed depth is 71.2 m, indicating a 7.53% loss in depth from the 1990 archived data and a 16.24% depth loss to sedimentation from 1968 to 2020 and 22.35% depth loss in the year 2050 as shown on the projected surface. Consequently, the integrated model (MC and CA) efficiently predicted the future bathymetric surface of the Kainji reservoir for the year 2050 based on the data characteristics. However, the proven techniques for analysing spatial data, such as the Markov Chain and Cellular Automata, best suited for analysing categorical transition data, show some artefacts (black spots) on the projected generated map which is subject to further investigation.
PubDate: 2024-07-03
DOI: 10.1007/s12518-024-00564-9
- Spatial assessment of produced hailstorm maps in severely affected areas
in Northern Thailand based on dual-polarimetric radar using the cloud
computing platform Google Earth Engine-
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Abstract: Abstract The objective of this study was to use dual-polarimetric radar data to create an hourly hail product, which would then be analyzed using Google Earth Engine (GEE), a cloud computing platform. We used ground-based weather radar from the Thailand Meteorological Department’s Chiang Rai station in the north of Thailand. Dual-polarimetric weather radar data were analyzed at 15-minute intervals with Python-based open-source radar libraries such as PyArt and Wradlib. Hydrometeor classification was conducted using simulated atmospheric sounding data obtained from ERA5 reanalysis data, which had been classified into ten classes between 17.00 and 20.00 Local Time. At a 2-kilometer altitude grid, similar hydrometeor types with comparable solid-state characteristics were collected and presented in CAPPI format. Furthermore, we used JavaScript programming to conduct a localized impact study of the hailstorm in GEE in order to prove the preliminary damage assessment concept by comprising sophisticated spatial overlays with land use data, urban regions, farmland, population data, and counts of roofed homes. The analysis results in GEE reveal the potential damaging area prone to hailstorm passage. This is the first attempt in Thailand to create an hourly hailstorm product and integrate it into the Geographic Information System (GIS) using GEE’s cloud-based platform. This invention can annually support local organizations in disaster monitoring, impact assessment, and adaptation to hail-related events.
PubDate: 2024-06-29
DOI: 10.1007/s12518-024-00569-4
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