Traditional Indexing Methods Research Articles

Innovative approach for assessing nitrogen loss risk to surface waters from crop production in a watershed scale through nitrogen surplus index method

Although various measures are applied to control farmland nitrogen loss at the field scale, effective control strategies at the watershed scale are still lacking due to poor understanding of nitrogen loss risks across different scales. Therefore, an innovative approach to assess the risk of nitrogen loss to surface waters from crop production at the watershed scale was established, with nitrogen surplus, a more accurate indicator, as a major source factor introduced to the traditional index method. Furthermore, this approach was applied in the Baiyangdian Lake Watershed, a typically agricultural-dominated watershed in China, and the control strategies for farmland nitrogen loss reduction were investigated with scenario analysis. The major results included: (1) Areas with high (2–4 for risk to river, 1.6–3.2 for risk to lake) and very high (>4 for risk to river, >3.2 for risk to lake) risks of nitrogen loss in crop production were relatively small. For example, in the wheat-maize rotation system, these areas accounted for only 2.1–3.5 % and 0.9–1.8% of the total study area, respectively. Additionally, the critical source areas of nitrogen loss were concentrated around the receiving water bodies. (2) The nitrogen surplus in wheat season increased the nitrogen loss to surface waters. Compared to the maize season, the area with high and very high risk for nitrogen loss to the nearby river in wheat-maize rotation system increased by 58.3% and 141.4%. Furthermore, the areas with high and very high risk for nitrogen loss to the lake in wheat-maize rotation system were 129.6% and 220.6% more than that during maize season. (3) Nitrogen loss risk to the nearby rivers was correlated with nitrogen surplus, but nitrogen loss risk to the lake was related to the transport coefficient. (4) The optimized scenario for nitrogen loss reduction in crop production was the combination of nitrogen surplus being less than 20 kg/ha and 500 m banning area around the river with no fertilizer application, which reduced the nitrogen loss risk to the nearby river and to the lake by 94–95% and 89–95%. The results of this study provide a scientific basis for nutrient resource management and non-point source pollution control.

Dynamic monitoring of open-surface water bodies in the middle reaches of the Tarim River based on the GEE cloud platform

ABSTRACT This study innovatively utilizes the Google Earth Engine (GEE) cloud platform, combined with a multispectral index rule set, to address the challenges of extracting open-surface water bodies in the middle reaches of the Tarim River Basin (MROTR). To overcome the limitations of traditional index methods, particularly the reduced extraction accuracy caused by mixed pixels in arid environments, this research integrates indices such as MNDWI, NDVI, and EVI for automated water extraction, significantly enhancing the precision of water body delineation. By analyzing the spatiotemporal changes in open-surface water area (SWA) from 1990 to 2022, a notable increase in SWA was observed following the implementation of ecological gate-controlled water management, while a decreasing trend was identified within an 8-km range from the riverbanks. The results show a 93.7% accuracy in surface water identification and an SWA growth rate of 12.47 km2/year, with 96% of this growth attributed to seasonal water areas. Runoff loss decreased by 8.53% in the S1 and S2 regions but increased by 7.76% in the S3 and S4 regions. The mixed index rule method proved effective for large-scale water detection, offering new insights for managing arid region water resources.

A method for extracting small water bodies based on DEM and remote sensing images

Accurate and comprehensive extraction of water bodies facilitates the study of watershed changes and water quality monitoring and also provides a reference for ecological evolution. When extracting tributaries and small water bodies based on remote sensing images, there are problems such as uncertain threshold selection, more edge voids, and extraction difficulties. The improved water body extraction method (Combination Normalized Difference Water Index), fuses Digital Elevation Model and remote sensing images. The river network is extracted from the Digital Elevation Model and the remote sensing image is normalized. After fusion, threshold comparison is performed, and the threshold of the river network location is used as the basis for connecting the broken water bodies, it is compared and verified with the normalized thresholds to achieve accurate identification and complete extraction of water bodies. Taking the water bodies in Xinyang City as an example, the overall accuracy of the confusion matrix was the highest, with an overall accuracy of 93.0% compared with the traditional index method. Combined with water bodies in Zhengzhou City for model validation, it has high applicability. Whether the water body is large or small, the extraction results are complete, with no voids or broken, and the accuracy is high.

Extracting Plastic Greenhouses from Remote Sensing Images with a Novel U-FDS Net

The fast and accurate extraction of plastic greenhouses over large areas is important for environmental and agricultural management. Traditional spectral index methods and object-based methods can suffer from poor transferability or high computational costs. Current deep learning-based algorithms are seldom specifically aimed at extracting plastic greenhouses at large scales. To extract plastic greenhouses at large scales with high accuracy, this study proposed a new deep learning-based network, U-FDS Net, specifically for plastic greenhouse extraction over large areas. U-FDS Net combines full-scale dense connections and adaptive deep supervision and has strong future fusion capabilities, allowing more accurate extraction results. To test the extraction accuracy, this study compiled new greenhouse datasets covering Beijing and Shandong with a total number of more than 12,000 image samples. The results showed that the proposed U-FDS net is particularly suitable for complex backgrounds and reducing false positive conditions for nongreenhouse ground objects, with the highest mIoU (mean intersection over union) an increase of ~2%. This study provides a high-performance method for plastic greenhouse extraction to enable environmental management, pollution control and agricultural plans.

A Bearing Fault Diagnosis Method Based on Improved Mutual Dimensionless and Deep Learning

Under nonlinear and non-stationary dynamic conditions, the fault diagnosis methods based on multi-dimensional dimensionless indicators (MDI) often cannot provide effective and accurate health monitoring in the fault diagnosis of petrochemical units. In view of the above problems, this paper preprocesses the dynamic signal and reconstructs a new dimensionless indicator. The indicator combines Complementary Ensemble Empirical Mode Decomposition (CEEMD) with MDI, named as Complementary Ensemble multi-dimensionless indicators (CEMDI). By using the sequential mapping method, the CEMDI processed data can be converted into gramian angular fields (GAF). In processing sparse data, the advantages of convolutional neural networks (CNNs) were used to identify different fault types. The method is validated using three datasets, motor bearing data provided by the Case Western Reserve University, multistage centrifugal fan data and machinery failure prevention technology challenge data. Compared with the traditional dimensionless index method and the latest published dimensionless methods in the literature, the fault diagnosis methods based on CEMDI and CNN exhibits good performance in identifying fault types under different conditions, which verifies its effectiveness and superiority.

Exploration of the factors that influence total phosphorus in surface water and an evaluation of surface water vulnerability based on an advanced algorithm and traditional index method

Due to the continuous influence of human activities, phosphorus pollution in surface water has become a persistent problem that needs to be addressed since phosphorous entails certain risks and degrees of damage to ecosystems and humans. The presence and accumulation of total phosphorus (TP) concentrations in surface waters is the result of a combined effect of many natural and anthropogenic factors, and it is often difficult to intuitively identify the individual importance of each factor in regard to the pollution of the aquatic environment. Considering these issues, this study provides a new methodology to better understand the vulnerability of surface water to TP pollution and the factors that influence TP pollution through the application of two modeling approaches. This includes the boosted regression tree (BRT), an advanced machine learning method, and the traditional comprehensive index method (CIM). Different factors, such as natural variables (including slope, soil texture, normalized difference vegetation index (NDVI), precipitation, and drainage density) and point and nonpoint source anthropogenic factors were included to model the vulnerability of surface water to TP pollution. Two methods were used to produce a vulnerability map of surface water to TP pollution. Pearson correlation analysis was used to validate the two vulnerability assessment methods. The results showed that BRT was more strongly correlated than CIM. In addition, the importance ranking results showed that slope, precipitation, NDVI, decentralized livestock farming and soil texture had a greater influence on TP pollution. Industrial activities, scale livestock farming and population density, which are all contributing sources of pollution, were all relatively less important. The introduced methodology can be used to quickly identify the area most vulnerable to TP pollution and to develop problem specific adaptive policies and measures to reduce the damage from TP pollution.

Water quality assessment of deep learning-improved comprehensive pollution index: a case study of Dagu River, Jiaozhou Bay, China

In the past few decades, with the country's rapid development, water pollution has become a significant problem many countries face. Most of the existing water quality evaluation uses a single time-invariant model to simulate the evolution process, which cannot directly describe the complex behavior of long-term water quality evolution. In addition, the traditional comprehensive index method, fuzzy comprehensive evaluation, and gray pattern recognition have more subjective factors. It can lead to an inevitable subjectivity of the results and weak applicability. Given these shortcomings, this paper proposes a deep learning-improved comprehensive pollution index method to predict future water quality development. As a first processing step, the historical data is normalized. Three deep learning models, multilayer perceptron (MLP), recurrent neural network (RNN), and long short-term memory (LSTM), are used to train historical data. The optimal data prediction model is selected through simulation and comparative analysis of relevant measured data, and the improved entropy weight comprehensive pollution index method is applied to evaluate future water quality changes. Compared with the traditional time-invariant evaluation model, the feature of this model is that it can effectively reflect the development of water quality in the future. Moreover, the entropy weight method is introduced to balance the errors caused by subjective weight. The result shows that LSTM performs well in accurately identifying and predicting water quality. And the deep learning-improved comprehensive pollution index method can provide helpful information and enlightenment for water quality change, which can help improve the water quality prediction and scientific management of coastal water resources.

Remote-Sensing Extraction of Small Water Bodies on the Loess Plateau

The mixed pixel of low-resolution remote-sensing image makes the traditional water extraction method not effective for small water body extraction. This study takes the Loess Plateau with complex terrain as the research area and develops a multi-index fusion threshold segmentation algorithm (MFTSA) for a large-scale small water body extraction algorithm based on GEE (Google Earth Engine). MFTSA uses the AWEI (automated water extraction index), MNDWI (modified normalized difference water index), NDVI (normalized difference vegetation index) and EVI (enhanced vegetation index) for multi-index synergy to extract small water bodies. It also uses slope data generated by the SRTM (Shuttle Radar Topography Mission digital elevation model) and NIR band reflectance to eliminate suppressing high reflectivity noise and shadow noise. An MFTSA algorithm was proposed and the results showed that: (1) The overall extraction accuracy of the MFTSA algorithm on the Loess Plateau was 98.14%, and the correct extraction rate of small water bodies was 92.82%. (2) Compared with traditional water index methods and classification methods, the MFTSA algorithm could extract small water bodies with higher integrity and clearer and more accurate boundaries. (3) The MFTSA algorithm was used to extract a total of 69,900 small water bodies on the Loess Plateau, accounting for 97.63% of the total water bodies, and the area was 482.11 square kilometers, accounting for 16.50% of the total water bodies.

Measurement and decomposition of China's real economic growth efficiency under environmental constraints

From the perspective of economic growth efficiency, this paper adopts the traditional DEA-Malmquist index method and the SBM-ML index method considering undesired output to measure the growth efficiency of China’s real economy with or without environmental constraints. The results show that: (1) From 2005 to 2017, the total factor productivity of China's real economy increased by 2.7%, but under environmental constraints, the total factor productivity of China’s real economy decreased by 0.6%, mainly due to the “drag” of the growth efficiency of the real economy by technological changes. Second, without considering the unintended output, 76.67% of the provinces and cities have improved the growth efficiency of the real economy, and 93.33% have made technological progress. Third, under environmental constraints, the total factor productivity and technological progress showed an upward trend only in the central region, while all other regions showed a downward trend to varying degrees. The descending range is east > northeast > west.

Geo-accumulation Index Method to Optimize the Evaluation Method of Polymetallic Environment Quality: Taking Developed Agricultural Areas as an Example

An accurate and reasonable soil pollution assessment method is the premise of regional soil pollution assessment. Triangular fuzzy numbers were introduced into the geo-accumulation index method, combined with α-cut technology and the Latin hypercube sampling (LHS) stochastic simulation method, to evaluate the accumulation of heavy metals in five different soil types (moisture soil, aeolian sandy soil, cinnamon soil, loessal soil, and alluvial soil), with large differences in heavy metal (Cd, Hg, As, Pb, and Cr) content in the study area. This method can avoid the inaccuracy of the evaluation results caused by the traditional geological accumulation index method in the selection of background values and the great difference between local and whole heavy metal contents in the study area and can comprehensively and truly represent the regional soil heavy metal pollution status, thus providing a theoretical basis for scientific decision-making. The results showed that the improvement in the method will not affect the evaluation results when the heavy metal content difference is small, that is, when the evaluation results of the geological accumulation index at all points are within a certain interval level or when the evaluation results of the geological accumulation index at all points are less than 0. The traditional and geological cumulative index method based on the evaluation with triangular fuzzy numbers provides results for a certain level, which is determined by the heavy metal content in the study area average; conversely, differences in the heavy metal content of soil sampling points throughout the study area can result in a decrease or increase in the recorded heavy metal contents in larger areas, making the evaluation result inaccurate. Combined with the LHS sampling method, the possibility of changing the evaluation results into various pollution levels can greatly improve the limitations of traditional evaluation methods and make the evaluation results more reasonable and accurate. Combined with the geographic information system (GIS) method, the regional heavy metal pollution accumulation concentration can also be visualized.

Exploration of the Factors that Influence Total Phosphorus in Surface Water and an Evaluation of Surface Water Vulnerability Based on an Advanced Algorithm and Traditional Index Method

Exploration of the Factors that Influence Total Phosphorus in Surface Water and an Evaluation of Surface Water Vulnerability Based on an Advanced Algorithm and Traditional Index Method

Dynamic changes of land use/cover and landscape pattern in a typical alpine river basin of theQinghai‐TibetPlateau, China

AbstractLandscape heterogeneity is a comprehensive reflection of spatial patch and gradient, which is an important feature of landscape pattern. The China land use/cover dataset (CLUD) with 1 km spatial resolution was used in this study to investigate the dynamic changes of land use/cover in the Yarlung Tsangpo River basin (YTRB) of China from 1980 to 2015 through the land use transition matrix and dynamic degrees. The semi‐variogram model of the study area was constructed at different scales to determine the landscape characteristic scale of the YTRB. The traditional landscape index method and moving window method were used to investigate the watershed landscape pattern at the class and landscape levels, respectively. The results showed that the overall change in land use over the YTRB was not significant during the period 1980–2015, whereas the relative change in each type of land use was significant. Permanent glacier‐snow significantly degraded by 22.72%, while built‐up areas increased by 106.38%. The degree of the landscape fragmentation and diversity gradually increased from upstream to downstream. The unused land has the highest degree of fragmentation, and the agglomeration degree of urban land patches increased. The result of land use dynamic degree indicated that natural factors were the main causes of changes in land use during the first sub‐period 1980–2000, while human activities were the major drivers of changes in land use during the second sub‐period 2000–2015. The results can provide important information on the impact of regional development and environmental governance policies on the changes in land use/cover and landscape in Tibet.

Is Deep-Learning and Natural Language Processing Transcending the Financial Forecasting? Investigation Through Lens of News Analytic Process

This study tries to unravel the stock market prediction puzzle using the textual analytic with the help of natural language processing (NLP) techniques and Deep-learning recurrent model called long short term memory (LSTM). Instead of using count-based traditional sentiment index methods, the study uses its own sum and relevance based sentiment index mechanism. Hourly price data has been used in this research as daily data is too late and minutes data is too early for getting the exclusive effect of sentiments. Normally, hourly data is extremely costly and difficult to manage and analyze. Hourly data has been rarely used in similar kinds of researches. To built sentiment index, text analytic information has been parsed and analyzed, textual information that is relevant to selected stocks has been collected, aggregated, categorized, and refined with NLP and eventually converted scientifically into hourly sentiment index. News analytic sources include mainstream media, print media, social media, news feeds, blogs, investors’ advisory portals, experts’ opinions, brokers updates, web-based information, company’ internal news and public announcements regarding policies and reforms. The results of the study indicate that sentiments significantly influence the direction of stocks, on average after 3–4 h. Top ten companies from High-tech, financial, medical, automobile sectors are selected, and six LSTM models, three for using text-analytic and other without analytic are used. Every model includes 1, 3, and 6 h steps back. For all sectors, a 6-hour steps based model outperforms the other models due to LSTM specialty of keeping long term memory. Collective accuracy of textual analytic models is way higher relative to non-textual analytic models.

Methodological considerations of assessing meaningful/reliable change in computerized neurocognitive testing following sport-related concussion

Objective Traditional reliable change index (RCI) methods may be psychometrically limited due to their inability to account for particularly high or low baseline performance and regression to the mean following serial testing. The current study sought to examine differences between RCI and standardized regression-based (RBz) methods in a cohort of adolescent athletes engaged in sport-related concussion recovery. Methods Consultation records and results of computerized testing data via the Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) battery were retrospectively reviewed among 240 adolescent athletes involved in return-to-play protocols following a sport-related concussion. Results High concordance rates were found between RCI and RBz metrics across ImPACT composites, consistent with previous literature. However, especially for those with lower baseline performance, RBz scores tended to suggest cognitive performance not meeting or exceeding baseline scores despite RCI metrics being appropriate across speed-based ImPACT composites. In contrast, results revealed high rates of RCI scores suggesting continued cognitive difficulties despite RBz metrics being within normal limits, especially for adolescents with higher baseline performance. Conclusions Results suggest value in interpreting RBz values in addition to RCI values as these allow for clinical interpretation more sensitive to statistical confounds, including baseline performance and regression to the mean.

Research on Industry Question Answering System Based on Near Neighbor Vector Search

In the field of natural language processing, language-based question answering has been widely studied and made great achievements. As an important form of search engine, question answering system can further improve the search quality compared with traditional search engines. Traditional tree-based indexing methods are inefficient in high-dimensional space. This paper focuses on the application of k nearest neighbor search in large-scale image databases, and researches on high-dimensional indexing technology based on vector approximation method. Fast-Hessian detector is used to detect feature points and generate SURF feature description vector; Then, the initial matching point pair is obtained by fast approximate nearest neighbor search algorithm, and then the unidirectional matching results are matched bidirectionally; at the same time, it makes a detailed analysis of the situation that the events in the sentences are verbs of “abstract change”, so as to realize the automatic question and answer after the change of abstract relations between entities.

Study on a New Rock Fracability Evaluation Model of Shale Gas Reservoir

Shale gas is an important unconventional energy resource that needs large-scale fracturing to form industrial deliverability. The evaluation of reservoir fracability plays a key role in the optimization of the sweet spot, the design of multistage fracturing, and the prediction of economic benefit. Based on volumetric fracturing, the study proceeded from the fracture complexity of the fractured core, and the bursting pressure experiment technology using the constant strain rate method was established. After the core has fractured, the fracture morphology was extracted and the fracture parameters including fracture area ratio and fracture declination dispersion were calculated to construct the fracture complexity of the pressed core. Combined with the core strength, the fracability index of the core was determined to evaluate the reservoir fracability. This method can represent not only the fracturing effect but also the fracturing difficulty. Compared with the monitoring data of hydrofracture-induced microseism of the sample well, the core fracturing index was found to be in good agreement with the actual fracturing effect. This method is more reasonable than the traditional brittleness index method and rock mechanics parameter method.

Probabilistic River Water Mapping from Landsat-8 Using the Support Vector Machine Method

River water extent is essential for river hydrological surveys. Traditional methods for river water mapping often result in significant uncertainties. This paper proposes a support vector machine (SVM)-based river water mapping method that can quantify the extraction uncertainties simultaneously. Five specific bands of Landsat-8 Operational Land Imager (OLI) data were selected to construct the feature set. Considering the effect of terrain, a widely used terrain index called height above nearest drainage, calculated from the 1 arc-second Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), was also added into the feature set. With this feature set, a posterior probability SVM model was established to extract river water bodies and quantify the uncertainty with posterior probabilities. Three river sections in Northwestern China were selected as the case study areas, considering their different river characteristics and geographical environment. Then, the reliability and stability of the proposed method were evaluated through comparisons with the traditional Normalized Difference Water Index (NDWI) and modified NDWI (mNDWI) methods and validated with higher-resolution Sentinel-2 images. It was found that resultant probability maps obtained by the proposed SVM method achieved generally high accuracy with a weighted root mean square difference of less than 0.1. Other accuracy indices including the Kappa coefficient and critical success index also suggest that the proposed method outperformed the traditional water index methods in terms of river mapping accuracy and thresholding stability. Finally, the proposed method resulted in the ability to separate water bodies from hill shades more easily, ensuring more reliable river water mapping in mountainous regions.

A critical evaluation of methods to interpret drug combinations

Combination therapy is increasingly central to modern medicine. Yet reliable analysis of combination studies remains an open challenge. Previous work suggests that common methods of combination analysis are too susceptible to noise to support robust scientific conclusions. In this paper, we use simulated and real-world combination datasets to demonstrate that traditional index methods are unstable and biased by pharmacological and experimental conditions, whereas response-surface approaches such as the BRAID method are more consistent and unbiased. Using a publicly-available data set, we show that BRAID more accurately captures variations in compound mechanism of action, and is therefore better able to discriminate between synergistic, antagonistic, and additive interactions. Finally, we applied BRAID analysis to identify a clear pattern of consistently enhanced AKT sensitivity in a subset of cancer cell lines, and a far richer array of PARP inhibitor combination therapies for BRCA1-deficient cancers than would be identified by traditional synergy analysis.

Traditional dry soil layer index method overestimates soil desiccation severity following conversion of cropland into forest and grassland on China’s Loess Plateau

Due to severe soil erosion and low crop yield, sloping croplands have been converted into forests and grasslands during the past decades on China’s Loess Plateau (CLP). The introduced exotic high-water consumption plants cause soil water deficit that leads to the formation of dry soil layer (DSL). The traditional index method used to determine the existence of DSL considers only soil hydraulic property of “stable field capacity” and fails to fully reflect the interactions between soil water and vegetation. A new index method that considers physiological response of vegetation to drought is needed for DSL research. Here, we proposed a new method to identify and quantify the severity of DSL and compared the proposed method with the traditional method using field data for the 5 m soil depth in typical tree (R. pseudoacacia, n = 85) and grass (M. sativa, n = 20) plantations on CLP. The number of sites detected with DSL through the new method was less than the traditional method for both species. Based on the traditional method, the level of DSL was even more severe; with mean DSL thickness of 3.2 for R. pseudoacacia and 3.9 m for M. sativa. Then for the new index method, it was only 2.4 m for both plantations. DSL formation depth was much deeper under the new method than the traditional method. Also mean plant available soil water (PASW) stored in the identified DSL by the traditional method was ∼44.1 and 37.8 mm; accounting for 37.2 and 88.9% of total PASW in the 1–5 m soil profile under R. pseudoacacia and M. sativa, respectively. Thus, the traditional DSL index method overestimated DSL severity as it fails to account for plant soil water availability, especially in areas with coarser soil texture. The proposed new index method that is based on soil water potential is more suitable for characterization of DSL conditions under different soil types. This is critical for application in reclamation of DSLs and optimization of vegetation cover in the study area and beyond.

Experimental seismic attributes of gas-bearing anthracite

Coal mine accidents have seriously constrained the development of energy economy, in which coal and gas outburst accidents pose the greatest impact, accounting for more than 70% of the accident rate. At present, the mine usually uses the parameters to evaluate the risk of outburst, such as gas desorption index and drilling yield index. However, the traditional index method has the shortcomings of time lag, complicated operation, and difficult real-time monitoring. In this paper, the advanced detection method of multi-component seismic wave is used to obtain the seismic attributes in real time and fast way, and three-component reception and artificial hammer excitation are arranged in the roadway. The field tests were carried out on nine roadways of No. 3 anthracite in Yangquan Mining Area, China. The correlation between the absolute gas emission quantity and attributes of the seismic wave was studied. The results show that under the same coal seam, the correlation between the absolute gas emission quantity and the seismic wave velocity parameters is weak. The absolute gas emission quantity has a good exponential correlation with the quality factor Q, the attenuation coefficient α, and the dominant frequency value fm. The absolute gas emission quantity is negatively correlated with the quality factor Q, and the regression equation is Qg = 3.5644e−0.33Q with correlation coefficient R2 = 0.9126. With the increase of absolute gas emission quantity, the attenuation coefficient of gas-bearing anthracite rises, but the dominant frequency decreases. On account of the high correlation between the absolute gas emission quantity and the quality factor, real-time monitoring of the quality factor of seismic wave have theoretical significance for predicting coal and gas outburst, and it is expected to provide a new way of the prediction of the outburst risk.