Spatial Variation Rules Research Articles

Using Ensemble Learning for Remote Sensing Inversion of Water Quality Parameters in Poyang Lake

Inland bodies of water, such as lakes, play a crucial role in sustaining life and supporting ecosystems. However, with the rapid development of socio-economics, water resources are facing serious pollution problems, such as the eutrophication of water bodies and degradation of wetlands. Therefore, the monitoring, management, and protection of inland water resources are particularly important. In past research, empirical models and machine learning models have been widely used for the water quality assessment of inland lakes. Due to the complexity of the optical properties of inland lake water bodies, the performance of these models is often limited. To overcome the limitations of these models, this study uses in situ water quality data from 2017 to 2018 and multispectral (MS) remote sensing data from Sentinel-2 to construct experimental samples of Poyang Lake. Based on these experimental samples, we constructed a spatio-temporal ensemble model (STE) to evaluate four common water quality parameters: chlorophyll-a (Chl-a), total phosphorus (TP), total nitrogen (TN), and chemical oxygen demand (COD). The model adopts an ensemble learning strategy, improving the model’s performance by merging multiple advanced machine learning algorithms. We introduced several indices related to water quality parameters as auxiliary variables, such as NDCI and Enhanced Three, and used band data and these auxiliary variables as predictive variables, thereby greatly enhancing the predictive potential of the model.The results show that the inversion accuracy of these four inversion models is high (R2 of 0.94, 0.88, 0.92, and 0.93; RMSE of 1.15, 0.01, 0.02, and 0.02; MAE of 0.81, 0.01, 0.09, and 0.10), indicating that the STE model has good evaluation accuracy. Meanwhile, we used the STE model to reveal the spatio-temporal distribution of Chl-a, TP, TN, and COD from 2017 to 2018, and analyzed their seasonal and spatial variation rules. The results of this study not only provide an effective and practical method for monitoring and managing water quality parameters in inland lakes, but also provide water security for socio-economic and ecological environmental safety.

Lightning Whistler Wave Speech Recognition Based on Grey Wolf Optimization Algorithm

The recognition algorithm of the lightning whistler wave, based on intelligent speech, is the key technology to break the bottleneck of massive data and study the temporal and spatial variation rules of the lightning whistler wave. However, its recognition effect depends on the hyperparameters determined by manual experiments repeatedly, which takes a great deal of time and cannot guarantee the best recognition effect of the model. Therefore, we proposed the lightning whistler wave recognition algorithm based on grey wolf optimization (GWO). In this paper, the GWO algorithm is used to automatically find the best value of hyperparameters of Long Short-Term Memory (LSTM) in their limited searching space. Here we consider the number of hidden units (hu) and learning rate (lr) as the hyperparameters to be optimized, and the spatial coordinate (hu, lr) as the grey wolf position. By the end of the GWO process, we obtain the position of the wolf king α with the optimal hu and lr searched by the GWO algorithm. Then we use the optimal hu and lr to configure LSTM and perform supervised learning on the train set to obtain the final lightning whistler wave speech recognition model. Through experimental verification, the recognition model based on the GWO not only overcomes the uncertainty of the traditional model relying on manual finetuning of parameters and realizes the mechanism of automatic search and acquisition of hyperparameters, but also its recognition effect improves by about 2% in accuracy, F1score, and other metrics compared with the model trained by manually setting hyperparameters.

Influence of Temporal and Spatial Fluctuations of the Shallow Sea Acoustic Field on Underwater Acoustic Communication.

In underwater acoustic communication (UAC) systems, the channel characteristics are mainly affected by spatiotemporal changes, which are specifically manifested by two factors: the effects of refraction and scattering caused by seawater layered media on the sound field and the random fluctuations from the sea floor and surface. Due to the time-varying and space-varying characteristics of a channel, the communication signals have significant variations in time and space. Furthermore, the signal shows frequency-selective fading in the frequency domain and signal waveform distortion in the time domain, which seriously affect the performance of a UAC system. Techniques such as error correction coding or space diversity are usually adopted by UAC systems to neutralize or eliminate the effects of deep fading and signal distortion, which results in a significant waste of limited communication resources. From the perspective of the sound field, this study used experimental data to analyze the spatiotemporal fluctuation characteristics of the signal and noise fields and then summarized the temporal and spatial variation rules. The influence of the system then guided the parameter configuration and network protocol optimization of the underwater acoustic communication system by reasonably selecting the communication signal parameters, such as frequency, bandwidth, equipment deployment depth, and horizontal distance.

Research on spatiotemporal fluctuation characteristics of underwater acoustic communication signals

In underwater acoustic communication systems, channel characteristics are mainly affected by changes in space and time. The changes embody the random fluctuations of the seabed and sea surface, and the effects of refraction and scattering caused by seawater layered media on the sound field. As the channel is time-varying and space-varying, the rapid fluctuation of communication signals, which is shown in the frequency selective fading in frequency-domain and the signal waveform distortion in the time domain, leads to a bad effect on the performance of the underwater acoustic communication system. The techniques to compensate or offset the effects of deep fading such as coding error correction and spatial diversity are widely used in underwater acoustic communication systems, which may cause a significant waste of limited communication efficiency. This paper analyzes the spatiotemporal fluctuation characteristics of both signal field and noise field, and summarizes the temporal and spatial variation rules. The effects of signal spatiotemporal fluctuation on communication systems are significantly reduced by reasonably selecting the communication signal parameters, equipment deployment depth and horizontal distance, which will guide the parameter configuration and network protocol optimization of communication systems.

Temporal and Spatial Variation Patterns of the Environmental Elements in the Sediments of the Liaohe Estuary and the Related Influencing Factors

Remote sensing images, field survey data, and historical monitoring data are collected to analyze the historical change trend and spatial variation rules of sediment quality of the Liaohe Estuary and further investigate the effects of sea-area utilization type and vegetation succession stage on the variation patterns of environmental elements in the sediments. The results showed that the quality of the sediments in the Liaohe Estuary is overall favorable, and the average contents of various elements all satisfy the quality standards of first-grade sediments. The nutritive elements in the sediments exhibit obvious zonal distribution patterns, while heavy metals are randomly distributed and exhibit no obvious patterns. The effect of sea-area utilization type on the enrichment of pollutants in the sediments exhibits certain significant differences, suggesting that the distributions of heavy metals and pollutants are subject to human activities and exhibit certain randomness. The main control factors are significantly different at different vegetation succession stages. Specifically, the vegetation distribution in the initial succession stage is mainly affected by salinity; with the transition from halophytic vegetation to terrestrial vegetation, the inherent correlation between vegetation distribution characteristics and the contents of total organic carbon, total nitrogen, and total phosphorus increase gradually. Meanwhile, the content of heavy-metal pollutants exhibits no significantly inherent correlation between the distributions of the plant community.

REAL-TIME ACQUISITION AND ANALYSIS METHOD AND SYSTEM OF SECURITY HIDDEN DANGER INFORMATION IN INTELLIGENT SCENIC SPOTS UNDER THE ENVIRONMENT OF INTERNET OF THINGS

Abstract. Aiming at the problem that the existing methods of collecting and processing information of potential location safety hazards in scenic spots can not identify and analyze real-time potential hazards quickly and accurately, a real-time method and system of collecting and analyzing information of potential location safety hazards for tourists in intelligent scenic spots under the environment of Internet of Things is proposed.The set of method and system includes: collecting geographical coordinate information of potential safety hazards threatening visitor through the handheld terminal, and connecting the collected data and the spatial analysis request to the GIS spatial analysis server through the Web server in real time, so as to realize the buffer analysis considering the gradient; based on the knowledge base of secure locations in scenic spots and internet of things equipment, such as rainfall-sensor and water depth sensors, the radius of hidden danger buffer and parameter information of warning level are calculated in real time, and the knowledge formed in the analysis process is saved. By comparing and analyzing the historical information of Location Safety Knowledge Base, the temporal and spatial variation rules of newly added hidden danger features are discovered to determine the optimal value of hidden danger parameters. Then, through the geographic information system network service software, the new hidden danger buffer element layer is sent to the handheld terminal in real time, so as to avoid the impact of unsafe tourist caused by the untimely updating of hidden danger information in scenic spots, and improve the efficiency of safe production in scenic spots.

Characteristis of Soil Water and Salt Spatial Variations in the Spring Season in Typical Yellow River Delta Areas of Kenli County, China

The Yellow River Delta as an important area of reserved land resources, is faced with the problem of soil salinization. Grasping the status of soil water and salt as well as their spatial variation rules is an important foundation of prevention, control and use of soil salinization.This study selected Kenli County of the Yellow River Delta, obtained soil water and salt content data through field survey and lab experiments, and analyzed the status of soil water and salt as well as their spatial variation rules using statistics, GIS interpolation and buffer analysis methods. The results showed that the general salt content in the study area was mainly moderate. Salt content increased from soil surfacelayer to underlayer and salt content in each layer was significantly correlated. The areas with high saltness in surfacelayer, middlelayer and underlayer soil mainly distributed in the east near the Bohai Sea in Kenli County, while the areas with lower saltness mainly distributed in the southwest. Soil salt contents showed the trends of decrease, and soil water contents showed the trends of decrease first and then increase with the increase in distance to Bohai Sea. Stretching from the Yellow River, soil salt content showed increase tendency with the increase in distance to the Yellow River, and water content decreased first and then increased. The order from high saltness to low of different vegetation types was naked landsuaeda glaucatamarixvervainreedcouch grasspaddycottonwinter wheatmaize, the order of different geomorphic types was depressionslightly sloping groundslow hillockbeach heights. This study preliminary delineates soil water and salt status as well as their spatial variation rules in the spring season of the study area, and provides scientific basis for soil resource sustainable utilization in the Yellow River Delta.

ASPECTS CONCERNING THE OUTLIERS PROBLEM IN THE CONTEXT OF DIGITAL CLIMATIC MAPPING

When regression analysis is used as a global spatialisation method for climatic variables, one must pay special attention to the presence of values evading the spatial variation rules stated by the model (outliers). The outliers may alter significantly our regression models, therefore leading us to drawing the wrong conclusions. Our study focuses on the outliers problem through a simple example of mean annual precipitations spatialisation in eastern Romania using the altitude as predictor. The identification of the outliers is based on the magnitude of the residuals, on cross-validation and on the comparison of the regression residuals with the deleted residuals (jackknife error). After the identification stage, we construct regression models leaving out the outliers in order to quantify their negative effects. We then present several possible options to avoid these effects, focusing on the one which eliminates the outliers from the regression models but keeps the residual values in the respective points during the kriging stage in a residual kriging approach.