Spatial Complexity Research Articles

Traffic Congestion Scheduling for Underground Mine Ramps Based on an Improved Genetic Scheduling Algorithm

The dispatching of trackless transportation on the ramp of underground metal mines is closely related to the transportation efficiency of daily production equipment, personnel, and construction materials in the mine. The current dispatching of trackless transportation on the ramp of underground metal mines is discontinuous and imprecise, with unscientific vehicle arrangement leading to low efficiency and transportation congestion. This paper presents this study, which puts forward a kind of trackless transportation optimization method that can fully make use of the ramp in the roadway, and the slow slope fork point can be used for the trackless transportation vehicle passing section to improve the efficiency of trackless transportation on the ramp. This study adopts the principles of fuzzy logic and uses interval-based positioning instead of real-time positioning to effectively reduce the spatial complexity inherent in the algorithm. At the same time, this research presents a modified genetic algorithm that incorporates a time-loss fitness calculation. This innovation makes it possible to differentiate traffic priorities between different types of vehicles, thus bringing the scheduling scheme more in line with the economic objectives of the mining operations. Various parameters were determined and several sets of simulation experiments were carried out on the response speed and scheduling effect of the scheduling system, resulting in a 10 to 20 percent improvement for different vehicles in the efficiency of underground mining transport operations.

Impact of Nutrient Starvation on Biofilm Formation in Pseudomonas aeruginosa: An Analysis of Growth, Adhesion, and Spatial Distribution.

Objectives: This study investigates the impact of nutrient availability on the growth, adhesion, and biofilm formation of Pseudomonas aeruginosa ATCC 27853 under static conditions. Methods: Bacterial behaviour was evaluated in nutrient-rich Luria-Bertani (LB) broth and nutrient-limited M9 media, specifically lacking carbon (M9-C), nitrogen (M9-N), or phosphorus (M9-P). Bacterial adhesion was analysed microscopically during the transition from reversible to irreversible attachment (up to 120 min) and during biofilm production/maturation stages (up to 72 h). Results: Results demonstrated that LB and M9 media supported bacterial growth, whereas nutrient-starved conditions halted growth, with M9-C and M9-N inducing stationary phases and M9-P leading to cell death. Fractal analysis was employed to characterise the spatial distribution and complexity of bacterial adhesion patterns, revealing that nutrient-limited conditions affected both adhesion density and biofilm architecture, particularly in M9-C. In addition, live/dead staining confirmed a higher proportion of dead cells in M9-P over time (at 48 and 72 h). Conclusions: This study highlights how nutrient starvation influences biofilm formation and bacterial dispersion, offering insights into the survival strategies of P. aeruginosa in resource-limited environments. These findings should contribute to a better understanding of biofilm dynamics, with implications for managing biofilm-related infections and industrial biofouling.

Drought dynamics in California and Mississippi: A wavelet analysis of meteorological to agricultural drought transition

Understanding the transition from meteorological to agricultural drought is crucial for developing effective drought management strategies and early warning systems. This study provides a unique perspective by utilizing hybrid drought indices to explore the temporal and spatial complexities of drought propagation across two large watersheds—California and Mississippi—that feature distinct agro-climatic conditions and irrigation practices. We assess the links between meteorological drought, measured by the Standardized Precipitation Index (SPI), and agricultural drought using three indicators: Vegetation Drought Response Index (VegDRI), GRACE Root Zone Soil Moisture Percentile (SMI), and the Evaporative Demand Drought Index (EDDI). By comparing watersheds with different irrigation systems, we also consider the role of irrigation in modifying drought dynamics. Our analysis identified the 14-day SPI as particularly effective in capturing drought dynamics. Soil moisture showed the earliest response to rainfall anomalies, with a lag of 41 days in California and 59 days in Mississippi. Vegetation stress followed, with a lag of 137 days in California and 75 days in Mississippi, while atmospheric conditions lagged by 143 and 139 days, respectively. Irrigation was found to delay drought impacts by up to two months. This framework offers a finer temporal resolution and a more nuanced understanding of drought propagation, potentially informing more targeted drought mitigation strategies than traditional methods.

How to interpret Jiangnan gardens: a study of the spatial layout of Jiangnan gardens from the perspective of fractal geometry

This study contributes to design studies by offering a novel approach to understanding the spatial layout of Jiangnan gardens through the lens of fractal geometry. Analyzing 106 gardens, we found that the ideal fractal dimension range for Jiangnan gardens is 1.148 ~ 1.276, with gardens in the 2500 ~ 7200 m2 range exhibiting the highest complexity (1.238 ~ 1.276). Additionally, for gardens ranging from 2500 ~ 20,000 m2, the maximum spacious space area stabilizes, no longer expanding indefinitely with overall area. This suggests a design principle of spatial proportion and balance. By quantifying spatial complexity and the contrast between spacious and profund spaces, the study provides a new method for evaluating garden design and can help students and designers better apply the principles of Jiangnan garden design.

Evaluating the ability of the Wind Erosion Prediction System (WEPS) to simulate near-surface wind speeds in the Inland Pacific Northwest, USA

Wind speed is one of the main control factors of wind erosion and dust emissions, which are major problems in arid and semiarid regions of the world. Accurately simulating highly precise hourly wind speeds is critical and cost-efficient for land management decisions with the goal of mitigating wind erosion and land degradation. The Wind Erosion Prediction System (WEPS) is a process-based, daily time-step model that simulates changes in the soil–vegetation–atmosphere. However, to date, relatively few studies have been conducted to test the ability of the WEPS in simulating hourly wind speeds. In this study, the performance of the WEPS model was tested in the Inland Pacific Northwest (iPNW), where wind erosion is a serious problem. Hourly wind speeds were observed and simulated by the WEPS at 13 meteorological stations from 2009 to 2018 using the WEPS hourly wind speed probability histogram. Owing to increasing wind shear, the model is not as precise in reproducing high wind speeds. The WEPS inadequately simulated the hourly wind speeds at six of the 13 stations, with a low index of agreement (d < 0.5). The complex regional topography may be one of the reasons for this lack of agreement, because the WEPS’s performance of interpolation relies on spatial distances and surface complexity. Therefore, we validated the model using another wind-speed database to eliminate the impact of spatial interpolation. The performance of the WEPS was improved after removing the impact of spatial interpolation, producing d values > 0.5 at nine of the 13 stations. Our results suggest that the WEPS can accurately simulate hourly wind speeds and assess wind erosion in the absence of interpolation, whereas the model may be uncertain when invoking spatial interpolation. Some evidence also suggests that the model may have a tendency to underestimate observed hourly wind speeds. Pragmatically, this suggests that model users should consider the possibility that WEPS may underestimate wind erosion risk in the iPNW and plan implementation of conservation practices accordingly.

Advances in optical pooled screening to map spatial complexity.

Advances in optical pooled screening to map spatial complexity.

Identification and Spatial Characterization of suburban areas in Chengdu

As transitional territories where urban and rural functions interpenetrate, suburban areas have multiple values such as recreation, ecology, agriculture and landscape. Promoting their benign development is the key to realizing global sustainable development of urban and rural areas. Planning control is based on precisely defining the spatial extent of suburban areas, and understanding suburban area differentiation and its driving forces scientifically is a key component in raising the bar for focused planning. However, there are currently few research on fine-scale quantitative identification of suburban regions because of their spatial complexity, ambiguous boundaries, and structural dynamic features. Based on this, this paper develops a multi-dimensional identification index system for suburban areas by using multi-source big data and remote sensing information technology, employs the random forest model and the K-Medoids clustering algorithm, identifies the distribution of suburban areas and their subdivided types, analyzes spatial differentiation characteristics, and conducts empirical research using Chengdu City as an example. The findings demonstrate that: (1) The majority of suburban areas in Chengdu City are concentrated in groups or belts surrounding the urban centers of various districts and counties, and the higher the functional class of a district or county, the denser the distribution of suburban areas. (2)The distribution pattern of suburban areas in Chengdu basically conforms to its planned urban hierarchy system.(3)Urban-oriented, suburban-interacted and rural-oriented suburban areas have their own characteristics in land use level, economic development and regional population characteristics.(4)Regional openness, government behavior, social development and geographical location are the core driving factors of spatial differentiation of suburban areas in Chengdu, and the interaction between urban and rural systems, the flow of social resources and the transformation of development models are the spatial mapping dimensions that affect its differentiation.(5)Chengdu can draw up detailed regulatory planning of suburban units to standardize and guide the overall development of suburban areas. In support of the global sustainable development goals, this study offers a methodology for precisely and impartially defining suburban areas, assisting in the implementation of integrated urban-rural development globally.

Lightweight medical image segmentation network with multi-scale feature-guided fusion

In the field of computer-aided medical diagnosis, it is crucial to adapt medical image segmentation to limited computing resources. There is tremendous value in developing accurate, real-time vision processing models that require minimal computational resources. When building lightweight models, there is always a trade-off between computational cost and segmentation performance. Performance often suffers when applying models to meet resource-constrained scenarios characterized by computation, memory, or storage constraints. This remains an ongoing challenge. This paper proposes a lightweight network for medical image segmentation. It introduces a lightweight transformer, proposes a simplified core feature extraction network to capture more semantic information, and builds a multi-scale feature interaction guidance framework. The fusion module embedded in this framework is designed to address spatial and channel complexities. Through the multi-scale feature interaction guidance framework and fusion module, the proposed network achieves robust semantic information extraction from low-resolution feature maps and rich spatial information retrieval from high-resolution feature maps while ensuring segmentation performance. This significantly reduces the parameter requirements for maintaining deep features within the network, resulting in faster inference and reduced floating-point operations (FLOPs) and parameter counts. Experimental results on ISIC2017 and ISIC2018 datasets confirm the effectiveness of the proposed network in medical image segmentation tasks. For instance, on the ISIC2017 dataset, the proposed network achieved a segmentation accuracy of 82.33 % mIoU, and a speed of 71.26 FPS on 256 × 256 images using a GeForce GTX 3090 GPU. Furthermore, the proposed network is tremendously lightweight, containing only 0.524M parameters. The corresponding source codes are available at https://github.com/CurbUni/LMIS-lightweight-network.

Formation and conversion of high-peak-power structured beams by an Nd:YVO4/Cr4+:YAG laser subject to tight-focusing off-axis pumping

A compact Nd:YVO4/Cr4+:YAG passively Q-switched laser in a near-hemispherical resonator is exploited to realize high-peak-power pulsed beams with high spatial degrees of freedom. Beneficial from the advantages of strong intracavity beam focusing as well as the point-like excitation condition for the proposed cavity design, various high-order structured pulses as coherent superpositions of multiple degenerate eigenmodes are stably generated under different off-axis pump schemes. Besides, by employing external-cavity astigmatic mode conversion (AMC), the oval-shaped and chessboard-like structured pulses under on-axis and 1D off-axis pumping are transformed into exotic modes with polygonal and figure-eight-shaped envelopes to further enrich the spatial complexity of the generated fields. With well-defined beam structures that are reconstructed using the analytical resonant wave functions of the resonator, the phase structures of AMC pulsed fields are numerically resolved to present a variety of singularity arrays. Experimental results reveal that the overall peak power of the on-axis and off-axis generated structured pulses respectively exceeds 600 W and 1 kW while maintaining good pulse train stability with peak-to-peak amplitude fluctuation to be less than 10% and 15%.

A lightweight progressive joint transfer ensemble network inspired by the Markov process for imbalanced mechanical fault diagnosis

Owing to safety limitations and data collection costs, scenarios with imbalanced data usually arise, posing a great challenge for precise fault diagnosis. Targeting imbalanced fault diagnosis and the high computational cost of mainstream ensemble learning methods currently used, this article proposes a lightweight and accurate scheme based on a progressive joint-transfer ensemble network (PJTEN) and a Markov-lightweight strategy (MLS). Specifically, a PJTEN is developed, incorporating a multiple excitation-channel attention basic estimator and progressive joint-transfer strategy (PJTS) to maintain diversity of basic estimators better and focus more on key information from minority classes. Besides, the MLS guided by Markov transition probabilities is for the first time constructed for ensemble learning to reduce the network redundancy by alternating optimization. Using a standard dataset and a brand-new dataset of a real ship propulsion system, the proposed method achieves leading results in Accuracy, F1 score and MCC, compared with eight cutting-edge methods, thereby validating its substantial value. In terms of lightweight operation, such as temporal complexity (TC), spatial complexity (SC), and time efficiency, it is also ahead of the latest ensemble-based methods.

Regional and local patterns of soil arthropod diversity are explained by different processes in southwest China

Understanding the distribution patterns and underlying mechanisms of community assembly at different spatial scales is crucial for the ecology and conservation of organisms, yet little is known about soil arthropods. In this study, we tested the relative importance of the two hypotheses that could explain soil arthropod distribution patterns at local and regional scales: (1) the habitat heterogeneity hypothesis (spatial complexity and variability of a habitat allow the coexistence of many species), and (2) the more-individuals hypothesis (the amount of resources per se, regardless of its spatial heterogeneity, increases overall soil arthropod abundance and richness). We extensively collected soil arthropods (identified to families or subfamilies) and measured soil physicochemical properties in four forests distributed across latitudes in Yunnan Province, China. We found that soil arthropod abundance and taxon richness increased with increasing latitudes, contrasting with the generally known pattern for plants and aboveground animals. Litter biomass (resources) explained the taxon richness and abundance at the regional scale, which aligned with the more-individuals hypothesis. Other soil physicochemical properties (e.g., Ca, TP, Mg, Mn, and pH) were more important at the local scale, underpinning the habitat heterogeneity hypothesis. However, the relative importance of individual soil properties was highly location-specific, suggesting that the environmental factors operating in one location do not necessarily reflect those operating in others at different latitudes. These findings improve our understanding of the community assembly and ecological processes of soil arthropods at different spatial scales, which is vital for predicting the future of soil biodiversity.

Microtopography and vegetation generate uneven predation pressure on forest insects

Predation plays an important role in the coexistence of multiple species within forest ecosystems. It is spatially heterogeneous and influenced by the surrounding environment at different spatial scales. Studies focusing on multiple environmental factors in systems with high spatial complexity are lacking, but elucidating the effects of local environmental factors within a forest could assist in understanding the effects of local differences in predation pressures on multispecies coexistence. Here, we examined the effects of microtopography and vegetation on predation pressure using the model caterpillar method. We hypothesized that differences in microtopography and vegetation types would result in different predation pressures on invertebrates within a forest. Insect attacks were dominant throughout the study period. The attack rates on the model caterpillars were also lower on hill tops and evergreen deciduous trees. Predation pressure within the forest was heterogeneous and independently influenced by topography and vegetation type. Our results suggest that environmental heterogeneity within forests may lead to highly variable predation pressures and affect multispecies coexistence. This study suggests that microtopography and vegetation types within forests should be considered for biological control.

Three-dimensional molecular atlas highlights spatial and neurochemical complexity in the axial nerve cord of octopus arms

Octopus arms, notable for their complex anatomy and remarkable flexibility, have sparked significant interest within the neuroscience community. However, there remains a dearth of knowledge about the neurochemical organization of various cell types in the arm's nervous system. To address this gap, we used hybridization chain reaction (HCR) to identify distinct neuronal types in the axial nerve cords of the pygmy octopus, Octopus bocki, including putative dopaminergic, octopaminergic, serotonergic, GABAergic, glutamatergic, cholinergic, and peptidergic cells. We obtained high-resolution multiplexed fluorescent images at 0.28×0.28×1.0μm voxel size from 10 arm base and arm tip cross sections (each 50μm thick) and created three-dimensional reconstructions of the axial ganglia, illustrating the spatial distribution of multiple neuronal populations. Our analysis unveiled anatomically distinct and molecularly diverse scattered neurons, while also highlighting multiple populations of dense small neurons that appear uniformly distributed throughout the cortical layer and potential glial cells in the neuropil. Our data provide new insights into how different types of neurons may contribute to an octopus's ability to interact with its environment and execute complex tasks. In addition, our findings establish a benchmark for future studies, allowing pioneering exploration of octopus arm molecular neuroanatomy and offering exciting new avenues in invertebrate neuroscience research.

High-Precision Automated Soybean Phenotypic Feature Extraction Based on Deep Learning and Computer Vision.

The automated collection of plant phenotypic information has become a trend in breeding and smart agriculture. Four YOLOv8-based models were used to segment mature soybean plants placed in a simple background in a laboratory environment, identify pods, distinguish the number of soybeans in each pod, and obtain soybean phenotypes. The YOLOv8-Repvit model yielded the most optimal recognition results, with an R2 coefficient value of 0.96 for both pods and beans, and the RMSE values were 2.89 and 6.90, respectively. Moreover, a novel algorithm was devised to efficiently differentiate between the main stem and branches of soybean plants, called the midpoint coordinate algorithm (MCA). This was accomplished by linking the white pixels representing the stems in each column of the binary image to draw curves that represent the plant structure. The proposed method reduces computational time and spatial complexity in comparison to the A* algorithm, thereby providing an efficient and accurate approach for measuring the phenotypic characteristics of soybean plants. This research lays a technical foundation for obtaining the phenotypic data of densely overlapped and partitioned mature soybean plants under field conditions at harvest.

ResiDEM: Analytical Tool for Isomorphous Difference Electron Density Maps Utilizing Dynamic Residue Identification via Density Clustering.

Time-resolved serial femtosecond crystallography (TR-SFX) of biological molecules captures the time-evolved dynamics of the residual motions across crystal structures, enabling the visualization of structural changes in response to chemical and physical stimuli to elucidate the relationship between the structure and function of the system under study. However, interpretations of residual motions can be complex to deconvolute because of various factors such as the system's size, temporal and spatial complexity, and allosteric behavior away from active sites. Relying solely on electron density map visualization can also pose a challenge in differentiating between useful and irrelevant data. In order to accurately identify residues and determine their respective contributions to the reaction dynamics, new tools are needed. We developed a new tool, ResiDEM, which employs a clustering-based approach to group difference electron densities and associate them with proximal residues. It can identify and rank residues with significant motions. Network representation can be used to delineate the interrelations between the residues in motion. With these features, ResiDEM helps to interpret residual motions in TR-SFX data, identify key residues, and elucidate their roles in dynamic processes.

Optimizing Spatial Complexity of the Hard Decision Decoder Based on Hash and Syndrome Decoding (HSDec)

In this article, we propose an optimized version of the Hard Decision Decoder based on Hash and Syndrome Decoding (HSDec) decoder, named Reduced Memory Space of HSDec (RMS-HSDec), which uses less memory space. In this article, we aim to reduce the spatial complexity of the HSDec decoding algorithm while preserving its error correction capabilities. Our methodology involves allocating only the essential memory space for correctable error patterns and optimizing the hashing mechanism to effectively handle potential collisions. While maintaining the integrity of error correction, this new method guarantees memory reduction rates of over 96 % for the BCH(63, 39, 9) code and over 84 % for the QR(47, 24, 11) code compared to HSDec. Simulations were conducted to evaluate the performance of RMS-HSDec on various BCH and QR codes over AWGN and Rayleigh channels. The results demonstrated significant memory reduction rates and coding gains ranging from 0.8 dB to 2.8 dB over the AWGN channel and from 14 dB to 32 dB over the Rayleigh channel, confirming the robustness of the algorithm under different channel conditions. Comparative analyses showed that RMS-HSDec maintains competitive performance with existing decoders while offering effective error correction. These findings confirm the robustness of the RMS-HSDec algorithm under different channel conditions. Overall, the proposed decoder proves to be an effective solution, optimizing memory usage without compromising error correction capabilities, making it ideal for high-density data applications and environments with limited memory resources.

Urban synergistic carbon emissions reduction research: A perspective on spatial complexity and link prediction

Reducing urban carbon emissions (UCEs) holds paramount importance for global sustainable development. However, the complexity of interactions among urban spatial units has impeded further research on UCEs. This study investigates synergistic emission reduction between cities by analyzing the spatial complexity within the UCEs network. The future potential for synergistic carbon emissions reduction is predicted by the link prediction algorithm. A case study conducted in the Pearl River Basin of China demonstrates that the UCEs network has a complex spatial structure, and the synergistic capacity of emission reduction among cities is enhanced. The core cities in the UCEs network, including Dongguan, Shenzhen, and Guangzhou, have spillover effects that contribute to synergistic emission reduction. Community detection reveals that the common characteristics associated with UCEs become concentrated, thereby enhancing the synergy of joint efforts between cities. The link prediction algorithm indicates a high probability of strengthened carbon emission connections in the Pearl River Delta, alongside those between upstream cities, which shows potential in forecasting synergistic emission reductions. Our research framework offers a comprehensive analysis for synergistic emission reduction from the spatial complexity of UCEs network and link prediction. It acts as a worthwhile reference for developing differentiated policies on synergistic emission reduction.

Patient´s perception of spatial complexity in emergency spaces of a hospital in China: a method for stakeholders to improve design and management

ABSTRACT The focus of medical facility users has shifted from prioritizing epidemic prevention to prioritizing experience after COVID-19, particularly in the emergency department (ED) of a large-scale Chinese hospital. The current visual elements of the emergency department provide a negative perception to the patients who visit the department. The study aims to optimize the ED's spatial design by understanding how visual elements affect patients' perception of spatial complexity. The methodology involved recreating a patient's typical path through the ED using data from the hospital information system (HIS), capturing images at 70 location nodes, and calculating the fractal dimensions of these images using the box-counting method. Visual elements were categorized into eight groups, and their fractal dimensions and proportions within the images were analyzed. A breakpoint graph was developed to track changes in spatial complexity perception as patients move through the ED. Statistical analysis, including correlation and one-way ANOVA, revealed that spatial attributes and lighting conditions significantly impact spatial complexity. From the results, grouping analysis was conducted on the visual elements of the horizontal surfaces, vertical surfaces, ceiling, furniture, and vegetation. Further optimization was proposed. In conclusion, this study highlights the importance of visual elements in emergency spaces, directly influencing users' perception of spatial complexity. The relationship between spatial complexity and fractal dimension is further emphasized. Stakeholders and designers can use the findings of this study as references to make informed decisions to improve the user's experience in the healthcare-built environment.

Mapping phenoregions and phytoplankton seasonality in Northeast Pacific marine coastal ecosystems via a satellite-based approach

Phytoplankton phenology describes yearly algal growth cycles and characterises the timing, duration, and magnitude of bloom occurrences. This study used satellite chlorophyll-a data from 1998 to 2020 and the Hierarchical Agglomerative Clustering method to define phenoregions based on phytoplankton phenology spatial patterns over the British Columbia and Southeast Alaska coastal oceans. The defined phenoregions were used to simplify the spatial complexity of the heterogenous study region and thus better describe phytoplankton seasonality across the target area. The cluster analysis allowed the delineation of four coherent regions: two coastal regions and northern and southern shelf/offshore regions. Results showed that each phenoregion had distinguishable phytoplankton phenological characteristics, likely due to different physical forcings acting in these areas. Moreover, the interannual variability of the spring bloom initiation was evaluated considering interactions between sea surface temperature (SST) anomalies and the El Niño Southern Oscillation Index (ENSO). Early spring blooms were associated with positive SST anomalies and El Niño conditions; conversely, average or late spring blooms occurred in years with negative SST anomalies and La Niña conditions, with the strongest relationship occurring in the southern shelf/offshore phenoregion. This study provided new insights into the regionalisation of the British Columbia and Southeast Alaska coastal oceans based on phytoplankton phenology patterns. Given the critical role of phytoplankton as the base of the marine food web, such phenoregions have implications for regional zooplankton biomass and fish production. The link between phytoplankton phenology and climate drivers points to the importance of environmental change in phytoplankton bloom dynamics. Further research into the connection between phytoplankton bloom indices and zooplankton community structure and production would be an important step towards using these indices for ecosystem monitoring and fisheries management.

Utilizing the Google Earth Engine for Agricultural Drought Conditions and Hazard Assessment Using Drought Indices in the Najd Region, Sultanate of Oman

Accurately evaluating drought and its effects on the natural environment is difficult in regions with limited climate monitoring stations, particularly in the hyper-arid region of the Sultanate of Oman. Rising global temperatures and increasing incidences of insufficient precipitation have turned drought into a major natural disaster worldwide. In Oman, drought constitutes a major threat to food security. In this study, drought indices (DIs), such as temperature condition index (TCI), vegetation condition index (VCI), and vegetation health index (VHI), which integrate data on drought streamflow, were applied using moderate resolution imaging spectroradiometer (MODIS) data and the Google Earth Engine (GEE) platform to monitor agricultural drought and assess the drought risks using the drought hazard index (DHI) during the period of 2001–2023. This approach allowed us to explore the spatial and temporal complexities of drought patterns in the Najd region. As a result, the detailed analysis of the TCI values exhibited temporal variations over the study period, with notable minimum values observed in specific years (2001, 2005, 2009, 2010, 2014, 2015, 2016, 2017, 2019, 2020, and 2021), and there was a discernible trend of increasing temperatures from 2014 to 2023 compared to earlier years. According to the VCI index, several years, including 2001, 2003, 2006, 2008, 2009, 2013, 2015, 2016, 2017, 2018, 2020, 2021, 2022, and 2023, were characterized by mild drought conditions. Except for 2005 and 2007, all studied years were classified as moderate drought years based on the VHI index. The Pearson correlation coefficient analysis (PCA) was utilized to observe the correlation between DIs, and a high positive correlation between VHI and VCI (0.829, p &lt; 0.01) was found. Based on DHI index spatial analysis, the northern regions of the study area faced the most severe drought hazards, with severity gradually diminishing towards the south and east, and approximately 44% of the total area fell under moderate drought risk, while the remaining 56% was classified as facing very severe drought risk. This study emphasizes the importance of continued monitoring, proactive measures, and effective adaptation strategies to address the heightened risk of drought and its impacts on local ecosystems and communities.