Topographic Index Research Articles

Futuristic flood risks assessment, in the Upper Vellar Basin, integrating AHP and bivariate analysis

Flood susceptibility maps provide invaluable information for assessing and managing flood-prone areas, aiding in proactive planning, risk reduction strategies, and safeguarding vulnerable communities. The current research concentrates on advancing sustainable development practices by undertaking a comprehensive assessment of flood susceptibility in the Upper Vellar basin, with a projection for 2050. Employing an integrative methodology, this study utilizes an Analytical Hierarchy Process (AHP) and Bivariate Analysis. Nine critical parameters were used: elevation, distance from the river, distance from the road, drainage density, predicted LULC, projected precipitation, slope, soil type, and Topographic Wetness Index (TWI). The Modules of Land Use Change Evaluation (MOLUSE) plugin, which uses Cellular Automata-Artificial Neural Network (CA-ANN), was employed to predict the LULC map for the year 2050. Furthermore, bias-corrected Coupled Model Intercomparison Project 6 (CMIP 6) EC EARTH 3 Model (GCM) RCP 4.5 and 8.5 projected precipitation data were employed. The resulting flood susceptibility zones are classified into three categories: low, moderate, and high, with proportions of 32.64%, 55.52%, and 11.84% for RCP 4.5, and 34.63%, 53.46%, and 11.91% for RCP 8.5, respectively, concerning the total area. In both scenarios, nearly 38% of the settlement area is at high flood risk. This study provides essential insights for policymakers and stakeholders, facilitating the formulation of sustainable strategies to address projected changes in land use, precipitation patterns, and flood susceptibility in the Upper Vellar region up to 2050.

Integrated remote sensing and geographic information system overlay analysis for groundwater potential evaluation using AHP and fuzzy AHP: Southern sections of the western Afar rift margin and associated rift floor

A persistent drought commonly affects the southern sections of the western Afar rift margin and the associated rift floor, leaving the populace concerned about their access to food. A comprehensive assessment and usage of the local groundwater resource is therefore essential to overcoming the ongoing drought and meeting the area's increasing demand for sustainable water. With the aid of RS, GIS, AHP, and Fuzzy AHP methods, this study aims to assess the area's groundwater potential. Lineament density, Geology,Topographic Wetness Index (TWI), Recharge, Slope, Land Use and Land Cover, Soil, and Drainage Density were created using GIS and RS techniques and are the thematic layers taken into consideration for this purpose. The weight of these themes for their relative significance to groundwater occurrence was determined using AHP and Fuzzy AHP (FAHP). Lineament density is the primary factor influencing the presence of groundwater, followed by geology, TWI, and recharge. The Very low (221.48 km2), Low (9084.46 km2), Moderate (8281.96 km2), High (1441.65 km2), and Very High (102.45 km2) groundwater potential zones (GWPZs) were delineated using the GIS-overlay analysis technique. The validation of the groundwater potential zone map was completed using the well discharge, transmissivity, and specific capacity, and the scatter plot demonstrated a strong connection with R2 values of 0.81, 0.79, and 0.80, respectively. The sensitivity index (SI) shows that lineament density and geology, which have SI values of 2.08% and 1.74%, respectively, have the greatest influence on the distributions of groundwater potentiality in the catchment, while drainage density has the least influence, with a SI value of −0.89%. By ensuring sustainable management and development, the study's findings will help with decision-making for the long-term supply of groundwater. Additionally, the approach utilized in the study can be applied to other comparable arid to semiarid regions.

Soil erosion susceptibility prediction using ensemble hybrid models with multicriteria decision-making analysis: Case study of the Medjerda basin, northern Africa

Soil erosion is considered one of the most prevalent natural hazards in semiarid regions, leading to the instability of ecosystems and human life. The main purpose of this research was to investigate and analyze soil erosion susceptibility maps in the Medjerda basin in northern Africa. This study utilizes four ensemble models based on the analytical hierarchy process (AHP) multicriteria decision-making analysis, namely, deep learning neural network AHP (DLNN-AHP), frequency ratio AHP (FR-AHP), Monte Carlo AHP (MC-AHP), and fuzzy AHP (F-AHP). Eight predictor variables were considered as inputs to the model, namely, the slope degree, digital elevation model (DEM), topographic wetness index (TWI), distance to river (DFR), distance to road (DFRD), normalized difference vegetation index (NDVI), rainfall erosivity (R), factor and soil erodibility factor (K). Soil erosion inventory maps were developed from field surveys and satellite images. The dataset was randomly divided into 70% for training and 30% for testing. The performances of the utilized models were compared using a receiver operating characteristic (ROC) curve. The results highlighted that all the models utilized exhibited good performance, with DLNN-AHP (93.1%) exhibiting slight superiority, followed by FR-AHP (90.9%), F-AHP (88.9%), and MC-AHP (88.5%). Among the influencing factors, the distance to the river and rainfall erosivity had the most significant impacts on the incidence of soil erosion. Moreover, the current findings revealed that 38.3% of the study area is extremely highly susceptible to soil erosion. The results of this study can aid in developing decision-support tools for planners and managers aiming to mitigate the adverse effects of soil erosion.

AHP and geospatial technologies for identifying groundwater exploration target regions in Dindigul, southern India

Recent advances in space technology are being widely utilized in resource mapping and management as they provide a high level of accuracy over a short duration and at a lower cost, when compared to traditional methods. Remote sensing and the geographical information system (GIS) play a vital role in collecting and analyzing information used to map resources and earth surface features. For example, anthropogenic activities and climate change affect the natural interaction between the surface and ground water making the monitoring and management of groundwater resources mandatory for future sustainable development. In this study, we attempt to identify the groundwater potential zones in the Dindigul district of Tamil Nadu, India using these emerging technologies. To map regions with groundwater potential, the analytical hierarchical process (AHP) was used to assign weights to all classes in each thematic layer based on groundwater-influencing factors. The sub-features of every parameter were also ranked, using a pairwise comparison method, based on the groundwater-influencing factors and expert knowledge. The parameters selected for the analysis were geology, geomorphology, soil, land-use/land-cover, drainage density, lineament density, slope, rainfall, thickness of top-soil, thickness of fractured zone, thickness of weathered zone, topographic wetness index, roughness and curvature. The weighted overlay analysis method was adopted for all the thematic layers, with technical support from the AHP for assigning weightages. The results were classified into five categories viz., very high, high, moderate, low, and very low potential zones, and used to prepare a groundwater potential map. This map revealed a nuanced distribution of groundwater potential across the district, with varying degrees of potentiality in different regions. Very high potential zones occupy a minimal area (0.07%) but are strategically located in areas characterized by high weathered zone thickness and conducive soil characteristics. High potential zones cover a significant area (22.33%) and are dispersed throughout the district, with specific concentrations in regions featuring highly fractured zones and favorable geological attributes. Moderate potential zones (35.12%) are primarily located in the northeastern part of the study area, while low and very low potential regions cover 19.56% and 22.92% of the study area. The low and very low potential zones appear particularly in hilly terrains, with unfavorable top soil and geological conditions. The validation of the groundwater potential zone (GWPZ) map using the annual average water level data demonstrates a substantial match (60.53%) between the identified groundwater conditions and actual well depths, affirming the reliability and significance of the findings from this study.

Utilizing geodetectors to identify conditioning factors for gully erosion risk in the black soil region of northeast China

In the black soil region of Northeast China, the issue of gully erosion persists as a significant threat, resulting in extensive damage to farmland, severe degradation of the black soil, and decreased productivity. It is therefore of utmost importance to accurately identify areas that are susceptible to gully erosion to effectively prevent and control its negative impact. This study tried to utilize geographical detectors (geodetectors) as a means to identify the factors that contribute to the distribution of gullies and assess the risk of gully erosion (GER) in five catchments within the region, with areas ranging from approximately 80 km2–200 km2. By employing the geodetectors method, fourteen geo-environmental factors were analyzed, including topographic attributes (such as aspect, catchment area, convergence index, elevation, plan curvature, profile curvature, slope length, slope, stream power index, and topographic wetness index), channel network distance, vegetation index (NDVI and EVI), as well as land use/land cover (LULC). The modeling of GER was conducted using the random forest algorithm (RFA). Out of the fourteen examined geo-environmental factors, only a subset, comprising less than or equal to 50%, demonstrated a significant (p < 0.05) influence on the spatial distribution of gullies. These selected factors were sufficient in assessing GER, with LULC (mean q-value = 0.270) and elevation (mean q-value = 0.113) identified as the two most important factors. Furthermore, the RFA exhibited satisfactory performance across all catchments, achieving AUC values ranging from 0.712 to 0.933 (mean = 0.863) in predicting GER. Overall, the catchment areas were classified into high, moderate, low, and very low-risk levels, representing 9.67%–15.95%, 19.28%–26.08%, 24.59%–30.55%, and 30.54%–39.08% of the total area, respectively. Importantly, a significant positive linear relationship (r2 = 0.722, p < 0.05) was observed between the proportion of cropland area and the occurrence of high-level GER. Although the primary risk levels were categorized as low and very low, the proportion of high-risk levels exceeded the existing gully coverage (0.34%–3.69%). These findings highlight the substantial potential for gully erosion and underscore the necessity for intensified efforts in the prevention and control of gully erosion within the black soil region of Northeast China.

Spatial modeling for detection of water retention capacity in technosols developed on carboniferous spoil heap after hard coal mining

Post-industrial areas, such as heaps, are an Anthropocene pressure on the environment but show natural potential for new ecosystem services, i.e., water retention, biodiversity, and C-sequestration. All these ecosystem functions of such sites can be developed only in the case of appropriate reclamation treatments, which are especially difficult under the conditions of carboniferous spoil heaps after coal deep mining. The study of reclamation effects has been increasingly supported in recent years by modern remote sensing techniques. This paper presents the possibilities of using selected remote sensing data and topographic indices and their effect on volumetric water content, which is crucial for habitat development. PlanetScope satellite imagery and airborne laser scanning point clouds were used to estimate the volumetric water content (VWC) of soil. This integrated analysis allows for more accurate spatial modeling of soil moisture, a key indicator of ecological restoration in degraded land. The method of generalized additive models was used to develop a predictive model. The developed predictive model was characterized by the following values of model accuracy evaluation parameters: MAE – 4.1%; RMSE – 5.6%; and R2–0.49. As a result of the analysis, topographic and spectral variables with significant influence on VWC were identified, and a map of the retention capacity of the Sosnica heap was developed.

A fuzzy analytical hierarchy process -GIS approach to flood susceptibility mapping in NEOM, Saudi Arabia

The Kingdom of Saudi Arabia is undergoing massive and rapid urbanization as part of Vision 2030. This includes development projects along Saudi Arabia’s coastline across the Red Sea. Coastal areas, especially the ones along Saudi’s western regions are susceptible to natural disasters such as flooding. NEOM, a futuristic city currently being developed in the northwest of Saudi Arabia, exemplifies a potential flooding hazard due to its geographic location and proposed urbanization plans. This research aims to enhance flood hazard assessment in NEOM by applying the Fuzzy Analytical Hierarchy Process (FAHP) in combination with Geographic Information System (GIS). Acknowledging traditional limitations related to data availability and parameter selection consensus, the study carefully selects parameters such as drainage density, elevation, slope, rainfall, land use/land cover (LULC), soil type, normalized difference vegetation index (NDVI), and topographic wetness index (TWI). The 30 m DEM was used to derive Drainage Density, Slope, and TWI while LULC data helped assess land cover changes. Rainfall data and soil type information are integrated to evaluate their impact on flood susceptibility. NDVI is employed to analyze vegetation cover. Utilizing ArcGIS Pro’s weighted overlay model, the criteria were combined to generate the final flood susceptibility map. The research outcomes manifest in a flood susceptibility map categorizing areas into seven distinct susceptibility classes, ranging from ‘very low’ to ‘very high.’ A quantitative breakdown in a summary table provides insights into the proportional distribution of flood risk. Results indicate a significant portion of NEOM falls within varying degrees of moderate susceptibility range with relatively limited distribution of flood susceptibility on the extremes, equating to areas with ‘low to moderate’ susceptibility is 4,322.8 km2, areas with ‘moderate’ susceptibility is 5,109.69 km2, areas with ‘moderate to high’ is 4,081.39 km2. The flood susceptibility map developed in this study can shed insights on potential optimum areas for flood mitigation measures (i.e., optimum locations for establishing stormwater collection points).

Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region

Accurately estimating aboveground biomass (AGB) in forest ecosystems facilitates efficient resource management, carbon accounting, and conservation efforts. This study examines the relationship between predictors from Landsat-9 remote sensing data and several topographical features. While Landsat-9 provides reliable data crucial for long-term monitoring, it is part of a broader suite of available remote sensing technologies. We employ machine learning algorithms such as Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Random Forest (RF), alongside linear regression techniques like Multiple Linear Regression (MLR). The primary objectives of this study encompass two key aspects. Firstly, the research methodically selects optimal predictor combinations from four distinct variable groups: Landsat-9 (L1) data, a fusion of Landsat-9 data and Vegetation-based indices (L2), and the integration of Landsat-9 data with the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) (L3) and the combination of best predictors (L4) derived from L1, L2, and L3. Secondly, the research systematically assesses the effectiveness of different algorithms to identify the most precise method for establishing any potential relationship between field-measured AGB and predictor variables. Our study revealed that the Random Forest (RF) model was the most efficient method utilizing Landsat-9 OLI and SRTM DEM (L3) predictors, achieving remarkable accuracy. This conclusion was reached by assessing its outstanding performance when compared to an independent validation dataset. The RF model exhibited remarkable accuracy, presenting relative mean absolute error (RMAE), relative root mean square error (RRMSE), and R2 values of 14.33%, 22.23%, and 0.81, respectively. The XGBoost model is the subsequent choice with RMAE, RRMSE, and R2 values of 15.54%, 23.85%, and 0.77, respectively. The study further highlights the significance of specific spectral bands, notably B4 and B5 from Landsat 9 OLI data, in capturing spatial AGB distribution patterns. Integration of vegetation-based indices, including TNDVI, NDVI, RVI, and GNDVI, further refines AGB mapping precision. Elevation, slope, and the Topographic Wetness Index (TWI) are crucial proxies for representing biophysical and biological mechanisms impacting AGB. Through the utilization of openly accessible fine-resolution data and employing the RF algorithm, the research demonstrated promising outcomes in the identification of optimal predictor-algorithm combinations for forest AGB mapping. This comprehensive approach offers a valuable avenue for informed decision-making in forest management, carbon assessment, and ecological monitoring initiatives.

Gone with the wind: environmental variation influences detection efficiency in a coastal acoustic telemetry array

Range tests play a critical role in designing acoustic telemetry studies, guiding equipment configuration, deployment techniques, and the analysis of animal movement data. These studies often strive to capture the effects of environmental variation on detection efficiency over time but are frequently limited in spatial and temporal scale. This could lead to disparities between test results and the circumstances encountered during animal tracking studies. In this study, we evaluated detection range and efficiency at two distinct spatial and temporal scales in a dynamic intertidal ecosystem. Two range tests were conducted, the first being a small-scale study using 6 receivers deployed over 1 month. Using modern acoustic receivers with built-in transmitters and environmental sensors, we then conducted a large-scale range test with 22 receiver stations over a full year to approximate the area and duration of a typical animal movement study. Differences in detection range between the two studies occurred as a result of environmental variation and tag power output, with midpoint ranges estimated as 123 m (small scale, low power), 149 m (small scale, high power) and 311 m (large scale, very high power). At both scales, wind speed emerged as the most influential factor explaining temporal variation in predicted detection efficiency. However, this effect was modulated by wind direction which varied as a result of land sheltering and fetch between the two study scales. At the small scale, detection efficiency decreased with winds from the south and east, while at the large scale, northern and westerly winds were most detrimental. Water temperature had a positive effect on predicted detection efficiency at both scales, while relative water level was positive at the small scale and negative at the large scale. Additional factors, including precipitation and Topographic Position Index, were found to influence detection efficiency at a large scale. Moreover, sensors associated with receivers in the larger array revealed the significant influences of receiver tilt and ambient noise. These discrepancies in the outcomes of the two studies underscore the critical role of scale in range test design and emphasize the need for long-term, in situ range testing at relevant spatial scales.

Assessment of Ecological Quality and Analysis of Influencing Factors in Coal-Bearing Hilly Areas of Northern China: An Exploration of Human Mining and Natural Topography

The Changhe Basin is located in the earth–rock mountainous area in southeastern Shanxi, China, and represents a characteristic northern coal-bearing hilly area. The terrain is complex, and the area is rich in coal mines. It plays an indispensable role in maintaining ecological balance and sustainable development in North China. To investigate the changes in ecological quality in the Changhe Basin, as well as the impact of human mining activities and natural topography on ecological quality, this study constructs the Remote Sensing Ecological Index (RSEI) based on Landsat remote sensing images from 2001, 2008, 2015, and 2022, undertaking an analysis of the spatial–temporal distribution characteristics of the ecological quality and its changing trends over the past 20 years. Additionally, spatial autocorrelation distribution features are revealed using Moran’s I. The exploration extends to examining the relationship between mining activities and the surrounding ecological quality. Subsequently, we study the relationship between Topographic Wetness Index (TWI) and RSEI. The results indicate the following: (1) On the temporal scale, the average proportion of RSEIs categorized as excellent and good from 2001 to 2022 is 46.78%. Types showcasing stable ecological conditions average 52.49%. The level of overall ecological quality of the basin has remained consistently high. On the spatial scale, the western part of the Changhe River, particularly in mountainous areas, exhibits higher ecological quality. Poorer areas concentrate in Chuandi Town in the southwestern part, and are significantly impacted by mining activities. The eastern region manifests areas undergoing either rapid or gradual degradation. (2) The four-phase Moran index results reveal a robust positive correlation in the spatial distribution of ecological quality within the basin. High–High and Low–Low clusters dominate, while High–Low and Low–High distributions are scattered. (3) Mining activities exert a discernible impact on the surrounding ecological quality. As the distance from the buffer zone outside the mining area increases, RSEI gradually decreases. The impact level exhibits an initial increase and subsequent decrease from 2001 to 2022.

Enhancing the Performance of Machine Learning and Deep Learning-Based Flood Susceptibility Models by Integrating Grey Wolf Optimizer (GWO) Algorithm

Flooding is a recurrent hazard occurring worldwide, resulting in severe losses. The preparation of a flood susceptibility map is a non-structural approach to flood management before its occurrence. With recent advances in artificial intelligence, achieving a high-accuracy model for flood susceptibility mapping (FSM) is challenging. Therefore, in this study, various artificial intelligence approaches have been utilized to achieve optimal accuracy in flood susceptibility modeling to address this challenge. By incorporating the grey wolf optimizer (GWO) metaheuristic algorithm into various models—including recurrent neural networks (RNNs), support vector regression (SVR), and extreme gradient boosting (XGBoost)—the objective of this modeling is to generate flood susceptibility maps and evaluate the variation in model performance. The tropical Manimala River Basin in India, severely battered by flooding in the past, has been selected as the test site. This modeling utilized 15 conditioning factors such as aspect, enhanced built-up and bareness index (EBBI), slope, elevation, geomorphology, normalized difference water index (NDWI), plan curvature, profile curvature, soil adjusted vegetation index (SAVI), stream density, soil texture, stream power index (SPI), terrain ruggedness index (TRI), land use/land cover (LULC) and topographic wetness index (TWI). Thus, six susceptibility maps are produced by applying the RNN, SVR, XGBoost, RNN-GWO, SVR-GWO, and XGBoost-GWO models. All six models exhibited outstanding (AUC above 0.90) performance, and the performance ranks in the following order: RNN-GWO (AUC: 0.968) &gt; XGBoost-GWO (AUC: 0.961) &gt; SVR-GWO (AUC: 0.960) &gt; RNN (AUC: 0.956) &gt; XGBoost (AUC: 0.953) &gt; SVR (AUC: 0.948). It was discovered that the hybrid GWO optimization algorithm improved the performance of three models. The RNN-GWO-based flood susceptibility map shows that 8.05% of the MRB is very susceptible to floods. The modeling found that the SPI, geomorphology, LULC, stream density, and TWI are the top five influential conditioning factors.

Groundwater recharge potentiality mapping for opportune augmentation following two MCDMs for a Western Himalayan watershed

Accounting potential zones for recharging groundwater is a prerequisite before the implementation of the springshed conservation program. Many studies have suggested that the resurgence of the springs in the Himalayan region is waning due to anthropogenic and Climate Change impacts. Typical physical methods of recharge zone explorations are manpower & fiscal-intensive and find limited applicability in areas with steep slopes and undulating topography. The deliberated study is an attempt towards the identification of potential recharge zones using topographical and meteorological indices via two MCDM methods, namely, AHP and Fuzzy AHP for the Saryu watershed of Kumaun Himalaya, Uttarakhand. Twelve thematic layers following the assignment of suitable weights were overlayed for the development of the groundwater recharge potentiality map. The findings indicated that following AHP 6%, 86.89%, 7.10% & 0.01% of the study area demonstrates low, moderate, high, and very high potentiality for groundwater recharge, while, FAHP suggests these indices to be 19.23%, 76.77%, 3.55%, & 0.45%, respectively. Further, FAHP demonstrates the redundancy of a few input layers for the adopted scale of the input data. The study also furnishes a brief description of opportune recharge structures based on the groundwater recharge potentialities.

Comparing Frequency Ratio and Analytical Hierarchy Models for Landslide Susceptibility in the Dharan Sub-Metropolitan Region of Eastern Nepal

This study compares the application of Frequency Ratio (FR) (statistical/quantitative) and Analytical Hierarchy Process (AHP) (heuristic/semi-quantitative) for landslide susceptibility analysis in Dharan Sub-Metropolitan Region of eastern Nepal. Nine different thematic layers including slope, aspect, curvature, geology, land use, distance to roads, distance to thrusts, topographic position index (TPI), and distance to streams were used for analysis. A landslide inventory map was prepared by identifying the landslides in recent Google Earth images, and it was then verified by field observations. The resulting susceptibility map with different susceptibility levels was validated using the Area Under the Curve (AUC) method, and the resulting AUC values were determined to be 78% for FR and 76.8% for AHP. Both methods offer a reliable strategy for landslide susceptibility mapping with a good prediction rate. The FR model enclosed 41%, 31%, 20%, 7% and 1% of the area as very low, low, moderate, high, and very high susceptibility, respectively, whereas AHP showed 21%, 24%, 27%, 21% and 7% of the area for the respective susceptibility levels. The specific zones identified within the range from very low to very high susceptibility provide valuable insights for local authorities, planners, and decision-makers, allowing them to identify areas susceptible to landslides and implement mitigation measures to aid in targeted risk management efforts.

Securing water for arid regions: Rainwater harvesting and sustainable groundwater management using remote sensing and GIS techniques

Arid regions experience climatic stress under climate change: increased drought frequency coupled with intensified storm events. This disruption and lack of precipitation patterns leads to water scarcity and hinders the achievement of sustainable development goals. Egypt drainage basin exhibiting the greatest suitability for the implementation of rainwater harvesting (RWH) strategies. To facilitate the development of sustainable water resource management practices in the region, this study uses a multi-criteria methodology to delineate optimal zones for RWH within the Wadi Safaga. Integration of radar and optical remote sensing data obtained from Sentinel-1&2, Landsat-8, ALOS/PALSAR, and Sentinel-1 Interferometric SAR with climatic Tropical Rainfall Measuring Mission (TRMM), hydrological, and geological datasets emphasizes the hydrologic characteristics of the catchments. Additionally, the analysis of rainfall intensity patterns within the basin was undertaken. Thirteen factors are used in the predicted model including elevation, slope, curvature, depression, lithology, radar, InSAR CCD, drainage density (Dd), distance to river (DR), vegetation, topographic wetness index (TWI), rainfall, and lineament density. A knowledge-driven Geographic Information System (GIS) methodology, including weighted factors based on the Analytical Hierarchy Process (AHP), was implemented to delineate plausible areas for RWH and groundwater potential zones (GWPZs). The resultant map categorized the basin into five GWPZ classes: very low (14%), low (28%), moderate (27%), high (21%), and very high (10%). Furthermore, the study identified optimal locations for constructing reservoirs to store harvested rainwater and provide protection for downstream mining, industrial, and tourism activities. In conclusion, the obtained information is crucial for planners and decision-makers to implement sustainable water resource management strategies within the Wadi Safaga basin.

Co-seismic and rainfall-triggered landslide hazard susceptibility assessment for Uganda derived using fuzzy logic and geospatial modelling techniques

AbstractUganda has suffered from many damaging landslides like the 1966 Rwenzori, 1994 Kisomoro and 2010 Bududa events. Despite escalating landslide risks exacerbated by rapid deforestation, urbanization and population growth coupled with a substandard building stock, comprehensive national co-seismic and rainfall-induced landslide hazard and risk maps for Uganda do not exist. This study therefore aims to conduct landslide hazard assessment and zonation for Uganda using a geospatial-based fuzzy logic methodology. In this methodology, landslide frequency ratios obtained for the 1966 Toro and 1994 Kisomoro earthquakes are assigned to the stochastic event-based probabilistic seismic hazard map derived using OpenQuake-engine. The available co-seismic and rainfall-induced landslide inventory datasets are used to derive the distribution of landslide frequency ratios based on geology, topographic slope position index, slope aspect, slope angle, distance from streams, and proximity to major active faults. The spatial distribution of fuzzy membership functions obtained from frequency ratios are overlaid and aggregated to produce landslide susceptibility maps showing relative probabilities of landslide occurrences across Uganda. Results indicate that the highest overall landslide hazard susceptibility is expected in areas comprising highly weathered outcropping rocks of precambrian granites, dominantly metasedimentary, and granulites and gneisses geologies within 40 km from major active faults; where the bedrock peak ground acceleration ≥ 0.1 g, topographic position index ≥ 3.8, slope gradient ≥ 10°, and the distance from streams ≤ 1.25 km. These findings can inform Uganda’s directorate of disaster preparedness and management towards pioneering the development of co-seismic landslide risk mitigation measures for the country.

Developing flood mapping procedure through optimized machine learning techniques. Case study: Prahova river basin, Romania

Study regionPrahova river basin located in the central-southern region of Romania. Study focusThis study aims to assess the susceptibility to flooding by using state-of-the-art machine learning and optimization procedures. To achieve this goal, we employed ten flood-related variables as independent variables in our machine learning models. These variables include slope angle, convergence index, distance from the river, elevation, plan curvature, hydrological soil group, lithology, topographic wetness index, rainfall, and land use. We used 158 flood locations as dependent variables in the training of four hybrid models: Deep Learning Neural Network-Statistical Index (DLNN-SI), Particle Swarm Optimization-Deep Learning Neural Network-Statistical Index (PSO-DLNN-SI), Support Vector Machine-Statistical Index (SVM-SI), and Particle Swarm Optimization-Support Vector Machine-Statistical Index (PSO-SVM-SI). Utilizing the Statistical Index method, we calculated coefficients for each flood predictor class or category. New hydrological insights for the regionThe PSO-DLNN-SI model demonstrated the best performance, achieving an AUC-ROC curve of 0.952. It's worth noting that the application of the PSO algorithm significantly enhanced the model's performance. Additionally, it's crucial to highlight that approximately 25 % of the study region exhibits a high to very high susceptibility to flood events. Taking into account the very precise results of the models applied in the present study, we can state that from a hydrological point of view, the current research contributes to a better understanding of the intensity with which floods can affect the different areas of the Prahova river basin.

Assessment of environmental impact of road construction based on results of remote sensing monitoring

Remote sensing detected the impact of road construction on important environmental properties. The digital elevation model was applied to assess the impact of road construction on environmental regimes. The study focused on the total area of the 3-km zone of influence (monitoring zone of probable impact) around the national road H-31 Dnipro-Tsarychanka-Kobeliaky-Reshetylivka from Loboikivka village to the boundary of Dnipropetrovsk Oblast. The following derivatives of the digital elevation model are considered as geomorpho­logical variables: the topographic wetness index (TWI) and erosion factor (LS). To model the effect of road construction on the moisture regime and dynamics of erosion processes, the TWI and LS indices were calculated for a digital terrain model without a road and with a road with an elevation that corresponds to the planned level. Thus, the road is considered as an anthropogenic landform that changes the direction of water redistribution along the topography and thus affects the potential moisture conditions and erosion risks. The modeling results indicate that the transformation of the water regime will be observed within the entire 3-km monitoring area. Deviations from the normal value can be up to 7 units by module, which is about one third of the landscape-wide range of topographic moisture index values. Almost no impact of construction can be predicted for 80.7% of the territory. For 10.6% of the territory, the increase in moisture regime will be moderate, and for 3.2% of the area the increase will be very significant. In turn, for 4.3% of the area the decrease in humidification will be moderate, and for 1.2% the decrease will be very significant. The greatest impact on the redistribution of moisture conditions is predicted in the immediate vicinity of the road. Changes in the moisture regime can have significant negative consequences for both soil cover and biotic components of ecosystems. Changes in vegetation as a consequence of changes in both moisture and trophic conditions can be considered as possible scenarios for the development of landscape cover dynamics.

Landslide susceptibility assessment in Addi Arkay, Ethiopia using GIS, remote sensing, and AHP

Landslides account for the breakdown of natural topographies, impacting many mountainous areas and leading to loss of lives and damaged infrastructure. This research aims to generate a reliable landslide susceptibility zonation map employing geospatial and Analytical Hierarchy Processes (AHP) in Addi Arkay Woreda, North Gondar Zone, Amhara Regional State, northern Ethiopia. The present study uses remote sensing data, geographic information system (GIS) tools, AHP, and weighted linear combination (WLC) models to analyze multiple environmental variables, including slope, aspect, curvature, lithology, soil texture, topographic wetness index (TWI), and rainfall. As per the results, around 186.12 km2 (13.26%) of the total study area is under very high landslide susceptibility and 140.85 km2 (10.05%) under very low susceptibility. Using Google Earth images for inaccessible areas, 121 landslide inventories were identified through fieldwork. Of these inventories, 85 were used to train the model and 36 for testing. The performance of the AHP model was validated by the Receiver Operating Characteristics (ROC) curve (0.75), which indicates good predictive accuracy for identifying landslide-prone areas. These findings are essential to regional land use planning, hazard mitigation, and landslide prevention efforts. Additionally, this study contributes to the scientific understanding of landslide dynamics in the Northwestern highlands of Ethiopia and offers a methodological framework that can be applied to other regions with similar geological and climatic conditions.

Diatomite evidence for a small palaeo mountain lake at 3400 m asl, Lesotho, southern Africa

ABSTRACTThe Eastern Lesotho Highlands experience an excess of rainfall sufficient to form the country's primary export, supplying the economic hub of southern Africa, Gauteng South Africa. However, there is currently only one natural lake in the country, Letšeng‐la Letsie, and evidence of palaeolakes in the region is therefore of particular interest. This study presents the analysis of a diatomite outcrop from a depression northwest of Mafadi Summit, at 3400 m asl. The presence of diatomite, dominated by the facultative planktonic species Staurosirella pinnata and Staurosira construens and abundant planktonic Aulacoseira ambigua, is indicative of the continuous presence of a shallow lake between ~4600 and 100 cal a bp. Comparative analysis of rainfall for the Mafadi and Letšeng‐la Letsie regions from CHIRPS gridded rainfall data demonstrates sufficient rainfall for a lake of comparable size, if not larger, as Mafadi receives considerably more rainfall than Letšeng‐la Letsie. Analysis of the SRTM 30‐m Digital Elevation Model and Topographic Position Index calculations demonstrate the feasibility of a shallow surface water feature at Mafadi. The conversion of this palaeolake into the contemporary wetland is hypothesized to be the result of post‐industrial warming, possibly augmented by migration of livestock into the Eastern Lesotho Highlands.

Delineation of Intermittent Rivers and Ephemeral Streams Using a Hybrid Method

Intermittent rivers and ephemeral streams are crucial for the water cycle and ecosystem services, yet they are often neglected by managers and researchers, especially in headwater areas. This oversight has caused a lack of comprehensive basemaps for these vital river systems. In headwater regions, water bodies are typically sparse and disconnected, with narrow and less distinct channels. Therefore, we propose a novel hybrid method that integrates topographic data and remote sensing imagery to delineate river networks. Our method reestablishes connectivity among sparsely distributed water bodies through topographic pairs, enhances less distinct channel features using the gamma function, and converts topographic and water indices data into a weighted graph to determine optimal channels with the A* algorithm. The topographic and water indices data are derived from the Multi-Error-Removed Improved-Terrain DEM (MERIT DEM) and an average composite of the Modified Normalized Difference Water Index (MNDWI), respectively. In the upper Lancang-Mekong River basin, our method outperformed five publicly available DEM datasets, achieving over 91% positional accuracy within a 30 m buffer. This hybrid method enhances positional accuracy and effectively connects sparse water bodies in headwater areas, offering promising applications for delineating intermittent rivers and ephemeral streams and providing baseline information for these river systems.