Spatial Prediction Research Articles

Heterogeneous transfer learning considering feature representation and environmental consistency for landslide spatial prediction

ABSTRACT The difficult, time-consuming, and imbalanced acquisition of landslide inventories in complex and heterogeneous large areas often results in limited predictive performance for most statistical landslide spatial prediction methods. Although partial transfer learning methods have produced reliable predictive results and successfully implemented knowledge transfer between data-rich and data-scarce areas, most of these methods generally only extract inadequate environmental features and lack the ability to interpret when, what, and how to effectively transfer knowledge to other regions with limited data. In this paper, a heterogeneous transfer learning method considering feature representations and environmental consistency, which features robustness, similarity, and transferability, is proposed for landslide spatial prediction. Specifically, we trained a stacked autoencoder (SAE) to extract more nonlinear features among environmental factors, and added an environmental similarity criterion to the transfer adaptation boosting (TrAdaBoost) algorithm to minimize feature dissimilarities and avoid negative transfer in different scenarios. To evaluate the robustness of the proposed method, we first selected two target areas (Lushan County and Luding County, China) and a source area (Wenchuan County, China) as case study areas. Then, we directly combined the source area and target area as an additional dataset without considering transfer learning to validate the significance and necessity of the proposed method. The area under the receiver operating characteristic curve (AUC) of the two target regions for the proposed method were 0.920 and 0.972, respectively, which were greater than those of the traditional TrAdaBoost (0.909 and 0.969, respectively), SAE (0.790 and 0.937, respectively), and random forest (0.915 and 0.966, respectively) methods. Furthermore, the AUC values of the SAE (0.851 and 0.900) and random forest (0.890 and 0.935) models based on the expanded datasets were also lower than those of the proposed method. Therefore, the experimental results show that the proposed method can be generalized well due to its efficient utilization and high adaptability. Moreover, the proposed method can not only be applied to emergency rescue and disaster prevention but can also offer a promising way to improve landslide predictions of models with incomplete landslide inventories.

Near-Real Prediction of Earthquake-Triggered Landslides on the Southeastern Margin of the Tibetan Plateau

Earthquake-triggered landslides (ETLs) feature large quantities, extensive distributions, and enormous losses to human lives and critical infrastructures. Near-real spatial prediction of ETLs can rapidly predict the locations of coseismic landslides just after a violent earthquake and is a vital technical support for emergency response. However, near-real prediction of ETLs has always been a great challenge with relatively low accuracy. This work proposes an ensemble prediction model of EnPr by integrating machine learning tree models and a deep learning convolutional neural network. EnPr exhibits relatively strong prediction and generalization performance and achieves relatively accurate prediction of ETLs. Six great seismic events occurring from 2008 to 2022 on the southeastern margin of the Tibetan Plateau are selected to conduct ETL prediction. In a chronological order, the 2008 Ms 8.0 Wenchuan, 2010 Ms 7.1 Yushu, 2013 Ms 7.0 Lushan, and 2014 Ms 6.5 Ludian earthquakes are employed for model training and learning. The 2017 Ms 7.0 Jiuzhaigou and 2022 Ms 6.1 Lushan earthquakes are adopted for ETL prediction. The prediction accuracy merits of ACC and AUC attain 91.28% and 0.85, respectively, for the Jiuzhaigou earthquake. The values of ACC and AUC achieve 93.78% and 0.88, respectively, for the Lushan earthquake. The proposed EnPr algorithm outperforms the algorithms of XGBoost, random forest (RF), extremely randomized trees (ET), convolutional neural network (CNN), and Transformer. Moreover, this work reveals that seismic intensity, high and steep relief, pre-seismic fault tectonics, and pre-earthquake road construction have played significant roles in coseismic landslide occurrence and distribution. The EnPr model uses globally accessible open datasets and can therefore be used worldwide for new large seismic events in the future.

A Methodology for Susceptibility Assessment of Wave-Induced Seabed Liquefaction in Silt-Dominated Nearshore Environments

Wave-induced seabed liquefaction significantly jeopardizes the stability of marine structures and the safety of human life. Susceptibility assessment is key to enabling spatial predictions and establishing a solid foundation for effective risk analysis and management. However, the current research encounters various challenges, involving an incomplete evaluation system, poor applicability of methods, and insufficient databases. These issues collectively hinder the accuracy of susceptibility assessments, undermining their utility in engineering projects. To address these challenges, a susceptibility assessment method with the safety factor was developed as the key assessment parameter, allowing for a comprehensive susceptibility assessment across the silt-dominated nearshore environment using Empirical Bayesian Kriging (EBK). The safety factor is determined by combining the cyclic stress ratio (CSR) and the cyclic resistance ratio (CRR), which characterize wave loadings and sediment properties in the study area, respectively. This method was applied in the Chengdao region of the Yellow River Estuary, China, a typical silt-dominated nearshore environment where wave-induced liquefaction events have been reported as being responsible for multiple oil platform and pipeline accidents. By collecting the regional wave and seabed sediment data from cores spanning from 1998 to 2017, the safety factors were calculated, and a zonal map depicting the susceptibility assessment of wave-induced seabed liquefaction was created. This study can serve as a valuable reference for the construction and maintenance of marine engineering in liquefaction-prone areas.

Uncertainty model-based edge detection technology in badminton

As virtual reality technology develops, the analysis and processing of video content have become hot spots in the field of computer vision. Video Action Detection aims to locate features in network video, and its research spans many fields, such as computer vision and spatial prediction. In view of the problem of low-efficiency classification models and inaccurate localization of small-scale targets in complex scenes, we propose a novel method to generate candidate intervals for action detection. The action recognition model is adopted to generate the action score sequence on the video time series. We also propose the uncertainty model of the descending pose detection algorithm. The pre-reaction phase generates a candidate list in the form of concatenated videos containing exactly the same pose to detect action poses that are not identical and of non-maximum duration. Experiments with traditional target detection and multiple deep learning models show that the proposed Non-Maximum Suppression algorithm has a strong ability to extract neural network features. Furthermore, compared with traditional ATSS and Faster R-CNN methods, the detection quality and performance are improved by more than 15.2% and 7.8%, respectively. Our method can fully utilize perception information to improve the quality of decision planning and plays a connecting role between perception fusion and decision planning.

Enhancing spatial streamflow prediction through machine learning algorithms and advanced strategies

Forecasting and extending streamflow is a critical aspect of hydrology, especially where the time series are locally unavailable for a variety of reasons. The necessity of preprocessing, model fine-tuning, feature selection, or sampling to enhance prediction outcomes for streamflow forecasting using ML techniques is evaluated in this study. In this regard, the monthly streamflow at Pol-Chehr station is analyzed using various monthly rainfall and streamflow time series data from different stations. The results of streamflow prediction in the k-folds cross-validator approach are generally better than those of the time series approach, except when raw data with no preprocessing or feature selection is used. Applying the simple SVR model to raw data leads to the weakest result, but using the GA-SVR model on raw data significantly increases the Nash coefficient by about 215% and 72%, decreases the NRMSE by about 48% and 36% in the k-fold and time series approaches, even with no feature selection. On the other hand, standardization produces highly accurate model predictions in both the k-fold and time series approaches, with a minimum Nash coefficient of 0.83 and 0.73 during the test period in the simple SVR model, respectively. Finally, using optimization algorithms like GA to fine-tune ML models and feature selection does not always yield improved prediction accuracy, but it depends on whether raw or preprocessed data is chosen. In conclusion, combining k-fold cross-validator and preprocessing typically yields highly accurate predictive results, with an R value exceeding 93.7% (Nash = 0.83, SI = 0.55, NRMSE = 0.09), without requiring any additional fine-tuning or optimization. Using feature selection is only significant when utilizing the TS approach as well.

Abstract PO2-02-11: Radiomics and an Erosion Model to Predict Post-Neoadjuvant Therapy Tumor Characteristics for Personalized Breast Cancer Treatment

Abstract Background: Neoadjuvant therapy (NAT) has become a standard-of-care (SOC) for patients diagnosed with locally advanced or early-stage breast cancer. A critical step towards optimizing treatment selection and coordinating surgical care is to estimate the tumor’s response to NAT. While pathological complete response (pCR) is commonly used to measure NAT effectiveness, patients who do not achieve pCR still benefit from NAT depending on the tumor’s response. A reduction in tumor extent could alter the surgical approach from mastectomy to breast conserving therapy and improve recurrence and overall survival. In this study we used pre-treatment imaging data to provide a comprehensive prediction of clinically relevant metrics including tumor volume, tumor convex hull volume, and spatially derived metrics; this research represents an important advancement in predicting NAT effectiveness. Methods: 262 breast cancer patients from 5 institutions who underwent NAT with pre- and post-NAT dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were included in this study. Each MRI was automatically segmented using a convolutional neural network with manual edits and verification. Radiomic features were extracted from the pre-NAT MRI and tumor segmentation. Significant radiomic features were selected based on their correlation with post-NAT volume, feature-to-feature correlation, and LASSO regression. Altogether, 12 and 17 radiomic features were selected for volume and convex hull volume prediction, respectively. The selected features were inputted into a Huber loss regression model to predict either the post-NAT tumor volume or the convex hull volume (surgical excision volume). To enable spatial and morphological comparison between the predicted and post-NAT tumor, an erosion model was developed in which the pre-NAT and post-NAT MRIs were co-registered, and the pre-NAT tumor was symmetrically eroded until its volume matched the predicted volume. Clinically relevant metrics such as the distance from the tumor to the nipple, skin, and chest were computed to assess the morphological and spatial prediction capabilities of the model. All models were tested on patients from cohort holdout sets (n=110) unseen during training. Results: The volume prediction analysis yielded promising results, with an R^2 value of 0.62 for post-NAT tumor volume prediction and an R^2 value of 0.56 for post-NAT tumor convex hull volume prediction. The mean volume and convex hull volume error was 1.1 cc and 1.3 cc, respectively. Table 1 provides an overview of the morphological and spatial comparisons between the predicted and post-NAT tumors. Overall, the predicted tumors demonstrated close agreement with their post-NAT counterparts. Conclusion: This study demonstrates a successful approach towards estimating post-NAT tumor volume, convex hull volume, and morphological and spatial characteristics. By providing reliable estimates of post-NAT tumor characteristics using SOC pre-NAT data, our predictive models enable personalized treatment planning and patient stratification to optimize patient care. Table 1. Predicted vs. post-NAT tumor comparison. Results demonstrate accurate predictions for clinically relevant distance metrics. Citation Format: Bradley Feiger, John Pfeiffer, John Cole, Anuja Antony, Joseph Peterson. Radiomics and an Erosion Model to Predict Post-Neoadjuvant Therapy Tumor Characteristics for Personalized Breast Cancer Treatment [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-02-11.

Understanding the Disparities of PM2.5 Air Pollution in Urban Areas via Deep Support Vector Regression.

In densely populated urban areas, PM2.5 has a direct impact on the health and quality of residents' life. Thus, understanding the disparities of PM2.5 is crucial for ensuring urban sustainability and public health. Traditional prediction models often overlook the spillover effects within urban areas and the complexity of the data, leading to inaccurate spatial predictions of PM2.5. We propose Deep Support Vector Regression (DSVR) that models the urban areas as a graph, with grid center points as the nodes and the connections between grids as the edges. Nature and human activity features of each grid are initialized as the representation of each node. Based on the graph, DSVR uses random diffusion-based deep learning to quantify the spillover effects of PM2.5. It leverages random walk to uncover more extensive spillover relationships between nodes, thereby capturing both the local and nonlocal spillover effects of PM2.5. And then it engages in predictive learning using the feature vectors that encapsulate spillover effects, enhancing the understanding of PM2.5 disparities and connections across different regions. By applying our proposed model in the northern region of New York for predictive performance analysis, we found that DSVR consistently outperforms other models. During periods of PM2.5 surges, the R-square of DSVR reaches as high as 0.729, outperforming non-spillover models by 2.5 to 5.7 times and traditional spatial metric models by 2.2 to 4.6 times. Therefore, our proposed model holds significant importance for understanding disparities of PM2.5 air pollution in urban areas, taking the first steps toward a new method that considers both the spillover effects and nonlinear feature of data for prediction.

Unveiling spatial insights: navigating the parameters of dynamic Geographic Information Systems (GIS) analysis

In today's rapidly evolving landscape of Geographic Information Systems (GIS), understanding the intricate parameters that govern GIS analysis is paramount. This title encapsulates the essence of delving into the multifaceted dimensions of GIS, where a myriad of factors converges to shape the process and outcomes of spatial analysis. From the selection of input data and spatial layers to the fine-tuning of analytical techniques and model parameters, every step in GIS analysis is governed by a set of parameters that influence the accuracy, reliability, and relevance of the results. The title beckons researchers, practitioners, and enthusiasts alike to embark on a journey of exploration, unraveling the complexities of GIS analysis and its underlying parameters. It speaks to the dynamic nature of GIS, where the interplay of spatial data, computational algorithms, and user-defined parameters creates a rich tapestry of insights and discoveries. Whether mapping urban growth patterns, assessing natural resource availability, or modeling environmental change, understanding the parameters of GIS analysis is essential for unlocking the full potential of spatial data and informing decision-making processes. Moreover, the title hints at the transformative power of GIS as a tool for understanding and addressing complex spatial phenomena. By scrutinizing the parameters of GIS analysis, researchers can gain deeper insights into the underlying processes driving spatial patterns and trends. They can identify optimal parameter settings, refine analytical workflows, and enhance the accuracy and precision of spatial models and predictions. In essence, "Exploring the Parameters: Understanding the Dynamics of GIS Analysis" encapsulates the essence of GIS as a dynamic and evolving field, where the exploration of parameters serves as a gateway to unlocking the full potential of spatial analysis and harnessing the power of geography to address real-world challenges and opportunities.

Spatial best linear unbiased prediction: a computational mathematics approach for high dimensional massive datasets

Spatial best linear unbiased prediction: a computational mathematics approach for high dimensional massive datasets

Evaluating machine learning models and identifying key factors influencing spatial maize yield predictions in data intensive farm management

Understanding the relationships between crop yields, soil properties, weather patterns and input applications is important for optimizing agricultural production. Data variation analysis using statistical and machine learning (ML) approaches can help identify and understand the practices that optimize yield. The objectives of this study were (i) to evaluate the predictive accuracy of selected ML models for estimating grain yields in on-farm maize (Zea mays L.) trials with different combinations of seeding and fertilizer rates in a commercial field, and (ii) to investigate the ability of ML models to assist in identifying yield-limiting factors in the same field. Multiple linear regression, multilayer perceptron, decision tree, and random forest (RF) ML models were trained and tested using crop management and soil from a data-intensive farm management (DIFM) trial and remotely sensed data. The dataset consisted of multiple subplot treatment observations of crop management, soil properties and normalized difference vegetation index (NDVI), linked to final grain yield for the 2019/2020 and 2020/2021 seasons. The RF had the best combination of high correlation (R2 = 0.69 and 0.80) and low error (MAPE = 5.4 and 8.4% and RMSE = 0.69 and 0.95 t ha−1) when compared to other models for both seasons. Feature importance analysis revealed that urea application was consistently the most critical variable and explained yield variations to the greatest extent, whereas soil phosphorus (P), plant population, and sodium in 2020, and soil P, soil pH, clay content, and plant population in 2021 emerged as the most influential factors for explaining yields. This study concluded that the RF model was the best for spatial yield predictions using DIFM trial datasets. There was also variability between seasons in yield limiting factors resulting from temporal variations in growing conditions. To effectively apply insights from yield prediction models, it is crucial that the variables incorporated into these models have a significant connection to yield and the findings can be translated into actionable management decisions. The DIFM trials combined with ML can play an important role in advancing the field of precision agriculture by providing valuable insights into the complex interactions between crops, soils, and management practices, and identifying new opportunities for improving crop yields and environmental sustainability.

Towards accurate spatial prediction of Glossina pallidipes relative densities at country-scale in Kenya

Vector-borne diseases, like those transmitted by tsetse flies, pose a significant global public health threat. Reducing vector populations is a promising strategy for disease control, especially in the case of tsetse-transmitted African trypanosomiasis. However, the cost-effective implementation of large-scale vector surveillance and control measures face challenges due to the lack of spatially explicit and reliable maps identifying vector hotspots. In this study, we assessed the accuracy of predicting Glossina pallidipes relative densities across Kenya by linking constrained in-situ tsetse catch data from 660 traps across three Kenyan regions with readily available gridded satellite information (human population, land cover, soil properties, elevation, precipitation, and land surface temperature) using a classical random forest algorithm. To enhance predictive performance, we employed two feature elimination techniques specifically designed for machine learning algorithms, i.e., Recursive Feature Elimination (RFE) and Variable Selection Using Random Forests (VSURF). For each set of retained variables, we trained a Random Forest model using a spatial cross-validation technique. Our findings showed that tsetse fly relative densities decreased with mean annual precipitation, and soil moisture, and conversely increased with higher tree cover. Based on the cross-validated R2, 41% of the spatial variability in relative densities of tsetse flies could be explained. For spatial extrapolation, only the set of predictors retained by VSURF closely matched known tsetse fly distributions in Kenya. This more accurate performance of VSURF may be attributed to its approach of assessing variables for both importance and their contribution to reducing prediction error. Our study demonstrates the potential of using a random forest method to upscale tsetse relative abundance predictions to the national level. However, the reliability of the current extrapolated map remains uncertain. We recommend: 1) increasing tsetse fly sampling efforts, particularly in the data-limited northern and eastern regions of Kenya, and 2) developing a more precise and accurate land cover map with classes that directly associate with known habitat characteristics of the target tsetse species.

Using Automated Machine Learning for Spatial Prediction—The Heshan Soil Subgroups Case Study

Recently, numerous spatial prediction methods with diverse characteristics have been developed. Selecting an appropriate spatial prediction method, along with its data preprocessing and parameter settings, presents a challenging task for many users, especially for non-experts. This paper addresses this challenge by exploring the potential of automated machine learning method proposed in artificial intelligent domain to automatically determine the most suitable method among various machine learning methods. As a case study, the automated machine learning method was applied to predict the spatial distribution of soil subgroups in Heshan farm. A total of 110 soil samples and 10 terrain variables were utilized in the designed experiments. To evaluate the performance, the proposed method was compared to each machine learning method with default parameters values or parameters determined by expert knowledge. The results showed that the proposed method typically achieved higher accuracy scores than the two alternative methods. This suggests that automated machine learning performs effectively in scenarios where numerous machine learning methods are available and offers practical utility in reducing the dependence on users’ expertise in spatial prediction. However, a more robust automated framework should be developed to encompass a broader range of spatial prediction methods, such as spatial statistic methods, rather than only focusing on machine learning methods.

Spatial error prediction method for precision machine tools considering the influence of repeatability error

A spatial error prediction method for precision machine tools considering the influence of repeatability errors is proposed in this paper. Firstly, the reciprocal of the repeatability error is used to evaluate the measurement data confidence to distinguish the difference of useful information contained in the measurement data of different positions. Secondly, the proposed confidence index is used to weight the measured values of PDGEs at different positions, so that the data containing more useful information can play a full role in the error prediction, and the predicted error is substituted into the error model to predict the spatial error. Then, the computer simulation based on statistical analysis is designed, the PDGEs and spatial error prediction accuracy of the weighted algorithm are verified by simulation. Finally, a multi-instrument fusion spatial error measurement scheme is proposed, and the accuracy and reliability of the model are verified on a horizontal machining center prototype.

In Situ Nondestructive Detection of Nitrogen Content in Soybean Leaves Based on Hyperspectral Imaging Technology

In this paper, hyperspectral imaging technology, combined with chemometrics methods, was used to detect the nitrogen content of soybean leaves, and to achieve the rapid, non-destructive and in situ detection of the nitrogen content in soybean leaves. Soybean leaves under different fertilization treatments were used as the research object, and the hyperspectral imaging data and the corresponding nitrogen content data of soybean leaves at different growth stages were obtained. Seven spectral preprocessing methods, such as Savitzky–Golay smoothing (SG), first derivative (1-Der), and direct orthogonal signal correction (DOSC), were used to establish the quantitative prediction models for soybean leaf nitrogen content, and the quantitative prediction models of different spectral preprocessing methods for soybean leaf nitrogen content were analyzed and compared. On this basis, successive projections algorithm (SPA), genetic algorithm (GA) and random frog (RF) were employed to select the characteristic wavelengths and compress the spectral data. The results showed the following: (1) The full-spectrum prediction model of soybean leaf nitrogen content based on DOSC pretreatment was the best. (2) The PLS model of soybean leaf nitrogen content based on the five characteristic wavelengths had the best prediction performance. (3) The spatial distribution map of soybean leaf nitrogen content was generated in a pixel manner using the extracted five characteristic wavelengths and the DOSC-RF-PLS model. The nitrogen content level of soybean leaves can be quantified in a simple way; this provides a foundation for rapid in situ non-destructive detection and the spatial distribution difference detection of soybean leaf nitrogen. (4) The overall results illustrated that hyperspectral imaging technology was a powerful tool for the spatial prediction of the nitrogen content in soybean leaves, which provided a new method for the spatial distribution of the soybean nutrient status and the dynamic monitoring of the growth status.

Spatial Data Prediction Model Integrated with K-Nearest Neighbor Mechanism in Neural Networks

Spatial Data Prediction Model Integrated with K-Nearest Neighbor Mechanism in Neural Networks

Explicitly Representing Syntax Improves Sentence-to-Layout Prediction of Unexpected Situations

Abstract Recognizing visual entities in a natural language sentence and arranging them in a 2D spatial layout require a compositional understanding of language and space. This task of layout prediction is valuable in text-to-image synthesis as it allows localized and controlled in-painting of the image. In this comparative study it is shown that we can predict layouts from language representations that implicitly or explicitly encode sentence syntax, if the sentences mention similar entity-relationships to the ones seen during training. To test compositional understanding, we collect a test set of grammatically correct sentences and layouts describing compositions of entities and relations that unlikely have been seen during training. Performance on this test set substantially drops, showing that current models rely on correlations in the training data and have difficulties in understanding the structure of the input sentences. We propose a novel structural loss function that better enforces the syntactic structure of the input sentence and show large performance gains in the task of 2D spatial layout prediction conditioned on text. The loss has the potential to be used in other generation tasks where a tree-like structure underlies the conditioning modality. Code, trained models, and the USCOCO evaluation set are available via Github.1

Machine learning methods to improve spatial predictions of coastal wind speed profiles and low-level jets using single-level ERA5 data

Abstract. Observations of the wind speed at heights relevant for wind power are sparse, especially offshore, but with emerging aid from advanced statistical methods, it may be possible to derive information regarding wind profiles using surface observations. In this study, two machine learning (ML) methods are developed for predictions of (1) coastal wind speed profiles and (2) low-level jets (LLJs) at three locations of high relevance to offshore wind energy deployment: the US Northeastern Atlantic Coastal Zone, the North Sea, and the Baltic Sea. The ML models are trained on multiple years of lidar profiles and utilize single-level ERA5 variables as input. The models output spatial predictions of coastal wind speed profiles and LLJ occurrence. A suite of nine ERA5 variables are considered for use in the study due to their physics-based relevance in coastal wind speed profile genesis and the possibility to observe these variables in real-time via measurements. The wind speed at 10 ma.s.l. and the surface sensible heat flux are shown to have the highest importance for both wind speed profile and LLJ predictions. Wind speed profile predictions output by the ML models exhibit similar root mean squared error (RMSE) with respect to observations as is found for ERA5 output. At typical hub heights, the ML models show lower RMSE than ERA5 indicating approximately 5 % RMSE reduction. LLJ identification scores are evaluated using the symmetric extremal dependence index (SEDI). LLJ predictions from the ML models outperform predictions from ERA5, demonstrating markedly higher SEDIs. However, optimization utilizing the SEDI results in a higher number of false alarms when compared to ERA5.

Spatial Prediction of Groundwater Potential of Upper Tigris Basin Mapping in the Turkey with GIS-Based Multicriteria Decision Making (MCDM) Method

The Upper Tigris region in the Middle East is in Turkey and this study shows it to be an area with significant water resources that enable agricultural activities in the region. Since the GAP irrigation project has not been completed yet, there is an extensive use of groundwater for irrigation. This situation threatens the groundwater potential of the basin in a negative manner. Therefore, determination of groundwater potential should be evaluated properly instead of approving the groundwater potential of the region with observation wells, which is a more costly method. In this study, the groundwater potential of the basin was calculated by the GIS-based Multi-Criteria Decision Making (MCDM) method via main elements affecting groundwater formation. The GIS-based-AHP method is used for identifying the groundwater potential zones of the Upper Tigris Basin as an alternative to expensive and time-consuming method of well drilling. There are 8 key criteria taken into account; Geomorphology (GM), Geology(G), Line Density (LD), Slope (SL), Drainage Density (DD), Land Use (LU), Rainfall (R), and Soil Type (ST) and the individual weight of each criterion was evaluated by the AHP technique and utilized by the “Spatial Analysis Overlay Weighted Method” obtaining the “Groundwater Potential Index (GWPI)”. The GWPI values obtained is used to classify the Upper Tigris Basin into five categories as follows: 319 km2 of the basin has very poor potential (3.8%); 2217 km2 has poor potential (26.7%); 2800 km2 has moderate potential (33.7%); 2200 km2 has good potential (26.5%); and finally, 763 km2 has very good potential (9.2%).

Integrated assessment of potentially toxic elements in soil of the Kangdian metallogenic province: A two-point machine learning approach

The accumulation of potentially toxic elements in soil poses significant risks to ecosystems and human well-being due to their inherent toxicity, widespread presence, and persistence. The Kangdian metallogenic province, famous for its iron-copper deposits, faces soil pollution challenges due to various potentially toxic elements. This study explored a comprehensive approach that combinescombines the spatial prediction by the two-point machine learning method and ecological-health risk assessment to quantitatively assess the comprehensive potential ecological risk index (PERI), the total hazard index (THI) and the total carcinogenic risk (TCR). The proportions of copper (Cu), cadmium (Cd), manganese (Mn), lead (Pb), zinc (Zn), and arsenic (As) concentrations exceeding the risk screening values (RSVs) were 15.03%, 5.1%, 3.72%, 1.24%, 1.1%, and 0.13%, respectively, across the 725 collected samples. Spatial prediction revealed elevated levels of As, Cd, Cu, Pb, Zn, mercury (Hg), and Mn near the mining sites. Potentially toxic elements exert a slight impact on soil, some regions exhibit moderate to significant ecological risk, particularly in the southwest. Children face higher non-carcinogenic and carcinogenic health risks compared to adults. Mercury poses the highest ecological risk, while chromium (Cr) poses the greatest health hazard for all populations. Oral ingestion represents the highest non-oncogenic and oncogenic risks in all age groups. Adults faced acceptable non-carcinogenic risks. Children in the southwest region confront higher health risks, both non-carcinogenic and carcinogenic, from mining activities. Urgent measures are vital to mitigate Hg and Cr contamination while promoting handwashing practices is essential to minimize health risks.

Spatial analysis of modern contraceptive use among women who need it in Ethiopia: Using geo-referenced data from performance monitoring for action.

The challenge of achieving maternal and neonatal health-related goals in developing countries is significantly impacted by high fertility rates, which are partly attributed to limited access to family planning and access to the healthcare systems. The most widely used indicator to monitor family planning coverage is the proportion of women in reproductive age using contraception (CPR). However, this metric does not accurately reflect the true family planning coverage, as it fails to account for the diverse needs of women in reproductive age. Not all women in this category require contraception, including those who are pregnant, wish to become pregnant, sexually inactive, or infertile. To effectively address the contraceptive needs of those who require it, this study aims to estimate family planning coverage among this specific group. Further, we aimed to explore the geographical variation and factors influencing contraceptive uptake of contraceptive use among those who need. We used data from the Performance Monitoring for Action Ethiopia (PMA Ethiopia) survey of women of reproductive age and the service delivery point (SDP) survey conducted in 2019. A total of 4,390 women who need contraception were considered as the analytical sample. To account for the study design, sampling weights were considered to compute the coverage of modern contraceptive use disaggregated by socio-demographic factors. Bayesian geostatistical modeling was employed to identify potential factors associated with the uptake of modern contraception and produce spatial prediction to unsampled locations. The overall weighted prevalence of modern contraception use among women who need it was 44.2% (with 95% CI: 42.4%-45.9%). Across regions of Ethiopia, contraceptive use coverage varies from nearly 0% in Somali region to 52.3% in Addis Ababa. The average nearest distance from a woman's home to the nearest SDP was high in the Afar and Somali regions. The spatial mapping shows that contraceptive coverage was lower in the eastern part of the country. At zonal administrative level, relatively high (above 55%) proportion of modern contraception use coverage were observed in Adama Liyu Zone, Ilu Ababor, Misrak Shewa, and Kefa zone and the coverage were null in majority of Afar and Somali region zones. Among modern contraceptive users, use of the injectable dominated the method-mix. The modeling result reveals that, living closer to a SDP, having discussions about family planning with the partner, following a Christian religion, no pregnancy intention, being ever pregnant and being young increases the likelihood of using modern contraceptive methods. Areas with low contraceptive coverage and lower access to contraception because of distance should be prioritized by the government and other supporting agencies. Women who discussed family planning with their partner were more likely to use modern contraceptives unlike those without such discussion. Thus, to improve the coverage of contraceptive use, it is very important to encourage/advocate women to have discussions with their partner and establish movable health systems for the nomadic community.