Measuring Air Quality Over Denpasar City Indonesia in 2021

The imbalance number of rapid vehicles with transportation facilities has become the problem. In Denpasar, Indonesia, the congestion during peak hours happens so often. Based on the department of transportation in Denpasar, Indonesia, the number of vehicle ownership in Bali is 4.1 million in condition with a ratio of one resident to one vehicle with the current population of Bali Province approximately 4.2 million. Our study aim to measure the air chemical parameters of CO, O3, SO2, NO2 and the physical parameters of the noise level. The research population is the atmosphere environment in the Denpasar City area. The research sample points were taken in the city center and the outskirts of Denpasar, with a total of 27 sample points. We employed impinge to get the airborne chemical gases and it is all analyzed with a spectrophotometer. We used a sound level meter to measure the ambient noise level. The data analysis was performed with free sample t test. The average ambient air chemistry obtained CO 517.34 µgr/Nm3, O3 0.17 µgr/Nm3, SO2 61.46 µgr/Nm3 and NO2 2.51 µgr/Nm3 and an average noise level 67.66 dBA. The number has found below the requirements Environmental Quality Standards and Environmental Damage Standard Criteria by Bali Governor. There is a difference in the mean parameters of CO, SO2, NO2 and ambient noise level in the downtown area. The average CO is 757.15 µgr/Nm3, SO2 67.60 µgr/Nm3, NO2 3.77 µgr/Nm3 and the noise level is 68.53 dBA with Denpasar outskirts mean CO 217.57 µgr/Nm3, SO2 53.79 µgr/Nm3, NO2 0.95 µgr/Nm3 and noise level 66.57 dBA. There is no difference in the average ambient O3 in the city center area with an average of 0.22 µgr/Nm3 with the outskirts of Denpasar an average of 0.11 µgr/Nm3.

Spatial variability of soil erodibility in pastures and forest areas in the municipality of Porto Velho, Rondônia

“Erodibility” is a characteristic of the soil that represents the susceptibility with which its particles from the most superficial layer are taken and transported to lower places by erosive agents, causing environmental and economic damages. This work estimated soil erodibility in pastures and forest areas in the municipality of Porto Velho-Rondônia. In the field, three areas with different types of vegetation were selected, one with brachiaria, another with mombaça grass, and a third in native forest. In areas with pastures, a sampling mesh of equal sizes was outlined (90 m x 60 m), and in the forested area an approximate sampling mesh (90 m x 50 m), with a regular spacing of 10 m between the samples points for both areas. The sampling was done at the crossing points of the mesh at a depth of 0.0-0.2 m, composing 70 sample points in the areas with pastures and 60 sample points in the forest area, totaling 200 samples. Then, laboratory analyzes were carried out to determine the texture followed by the fractionation of the sand, and the organic carbon followed by the estimate of the organic matter of the soil. The erodibility factors were calculated using indirect prediction models, and then, univariate, geostatistical and multivariate techniques were applied. The pastures’ environments differed from the forest environment. However, the mombaça grass area functions as an intermediate environment between the forest and the brachiaria, being closer to the forest environment. Keywords: erodibility, factors, kriging, principal components.

Entomological surveillance of mosquitoes (Diptera: Culicidae), vectors of arboviruses, in an ecotourism park in Cachoeiras de Macacu, state of Rio de Janeiro-RJ, Brazil

Arboviruses are arthropod-dependent viruses to complete their zoonotic cycle. Among the transmitting arthropods, culicids stand out, which participate in the cycle of several arboviruses that can affect humans. The present study aimed to identify species of culicidae and to point out the risk of circulation, emergency, or reemergence of pathogenic arboviruses to humans in the region of the Jequitibá headquarters of the Parque Estadual dos Três Picos (PETP), in Cachoeiras de Macacu, state of Rio de Janeiro, Brazil. Sampling was carried out at five Sample Points (SP) demarcated on trails from the headquarters, with CDC light traps, HP model with dry ice attached to the side, for 48 hours of activity each month. Additionally, active catches were made with a castro catcher in the period of one hour per day in the field, from six to eleven o’clock in the morning, in each PM. After the captures, thematic map was assembled using the ArcGIS 10 software and performing a multidimensional scaling (MDS). A total of 1151 specimens were captured and the presence of culicids already incriminated as vectors of arboviruses circulating in the region was observed: Aedes fluviatilis Lutz, 1904 (71 specimens); Aedes scapularis Rondani, 1848 (55 specimens); Haemagogus leococelaenus Dyar and Shannon, 1924 (29 specimens). In addition to the subgenus Culex (culex) spp. (163 specimens). In this sense, we highlight the importance of strengthening the actions of continuous entomological surveillance of the emergence and re-emergence of new arboviruses in ecotourism visitation parks.

Estimation of Salinity Content in Different Saline-Alkali Zones Based on Machine Learning Model Using FOD Pretreatment Method

Soil salinization is a global ecological and environmental problem in arid and semi-arid areas that can be ameliorated via soil management, visible-near infrared-shortwave infrared (VNIR-SWIR) spectroscopy can be adapted to rapidly monitor soil salinity content. This study explored the potential of Grünwald–Letnikov fractional-order derivative (FOD), feature band selection methods, nonlinear partial least squares regression (PLSR), and four machine learning models to estimate the soil salinity content using VNIR-SWIR spectra. Ninety sample points were field scanned with VNIR-SWR and soil samples (0–20 cm) were obtained at the time of scanning. The samples points come from three zones representing different intensities of human interference (I, II, and III Zones) in Fukang, Xinjiang, China. Each zone contained thirty sample points. For modeling, we firstly adopted FOD (with intervals of 0.1 and range of 0–2) as a preprocessing method to analyze soil hyperspectral data. Then, four sets of spectral bands (R-FOD-FULL indicates full band range, R-FOD-CC5 bands that met a 0.05 significance test, R-FOD-CC1 bands that met a 0.01 significance test, and R-FOD-CC1-CARS represents CC1 combined with competitive adaptive reweighted sampling) were selected as spectral input variables to develop the estimation model. Finally, four machine learning models, namely, generalized regression neural network (GRNN), extreme learning machine (ELM), random forest (RF), and PLSR, to estimate soil salinity. Study results showed that (1) the heat map of correlation coefficient matrix between hyperspectral data and salinity indicated that FOD significantly improved the correlation. (2) The characteristic band variables extracted and used by R-FOD-CC1 were fewer in number, and redundancy between bands smaller than R-FOD-FULL and R-FOD-CC5, thus estimation accuracy of R-FOD-CC1 was higher than R-FOD-CC5 or R-FOD-FULL. A high prediction accuracy was achieved with a less complex calculation. (3) The GRNN model yielded the best salinity estimation in all three zones compared to ELM, BPNN, RF, and PLSR on the whole, whereas, the RF model had the worst estimation effect. The R-FOD-CC1-CARS-GRNN model yielded the best salinity estimation in I Zone with R2, RMSE and RPD of 0.7784, 1.8762, and 2.0568, respectively. The fractional order was 1.5 and estimation performance was great. The optimal model for predicting soil salinity in II and III Zone was, also, R-FOD-CC1-CARS-GRNN (R2 = 0.7912, RMSE = 3.4001, and RPD = 1.8985 in II Zone; R2 = 0.8192, RMSE = 6.6260, and RPD = 1.8190 in III Zone), with the fractional order of 1.7- and 1.6-, respectively, and the estimation performance were all fine. (4) The characteristic bands selected by the best model in I, II, and III Zones were 8, 9, and 11, respectively, which account for 0.45%, 0.51%, and 0.63%% of the full bands. This approach reduces the number of modeled band variables and simplifies the model structure.

Optimal Path Planning using Informed Probabilistic Road Map Algorithm

This study aims to propose a new path planning algorithm that can guarantee the optimal path solution. The method used is to hybridize the Probabilistic Road Map (PRM) algorithm with the Information Search Algorithm. This hybridization algorithm is called the Informed-PRM algorithm. There are two informed search methods used. The first method is the informed sampling through an ellipsoid subset whose eccentricity is dependent on the length of the shortest current solution that is successfully planned in that iteration. The second method is to use a local search algorithm. The basic PRM algorithm will be run in the first iteration. Since the second iteration, the generation of sample points in the PRM algorithm will be carried out based on information. The informed sampling method will be used to generate 50% of the sampling points. Meanwhile, the remaining number of sample points will be generated using a local search algorithm. Using several benchmark cases, we compared the performance of the Informed-PRM algorithm with the Rapidly Exploring Random Tree* (RRT*) and informed RRT* algorithm. The test results show that the Informed-PRM algorithm successfully constructs the nearly optimal path for all given cases. In producing the path, the time and path cost of the Informed-PRM algorithm is better than the RRT* and Informed RRT* algorithm. The Friedman test was then performed to check for the significant difference in performance between Informed-PRM with RRT* and Informed RRT*. Thus, the Informed-PRM algorithm can be implemented in various systems that require an optimal path planning algorithm, such as in the case of medical robotic surgery or autonomous vehicle systems.

Research on expansion and classification of imbalanced data based on SMOTE algorithm

With the development of artificial intelligence, big data classification technology provides the advantageous help for the medicine auxiliary diagnosis research. While due to the different conditions in the different sample collection, the medical big data is often imbalanced. The class-imbalance problem has been reported as a serious obstacle to the classification performance of many standard learning algorithms. SMOTE algorithm could be used to generate sample points randomly to improve imbalance rate, but its application is affected by the marginalization generation and blindness of parameter selection. Focusing on this problem, an improved SMOTE algorithm based on Normal distribution is proposed in this paper, so that the new sample points are distributed closer to the center of the minority sample with a higher probability to avoid the marginalization of the expanded data. Experiments show that the classification effect is better when use proposed algorithm to expand the imbalanced dataset of Pima, WDBC, WPBC, Ionosphere and Breast-cancer-wisconsin than the original SMOTE algorithm. In addition, the parameter selection of the proposed algorithm is analyzed and it is found that the classification effect is the best when the distribution characteristics of the original data was maintained best by selecting appropriate parameters in our designed experiments.

A Full Coverage Reconstruction Algorithm for the Electromagnetic Map Based on Affinity Propagation Clustering

Considering the reconstruction of electromagnetic maps without the prior information of electromagnetic propagation environment in the target area, a new algorithm based on affinity propagation clustering is proposed to complete the electromagnetic map reconstruction of the target area from points to surfaces and then from points and surfaces to a larger surface. Firstly, according to the actual situation, the target area is reasonably divided into grids. Electromagnetic data is sampled by distributed sensing nodes, and a certain number of sample points are selected for affinity propagation clustering to determine the locations of centers of sample points. Secondly, for the incomplete sample data, the Kriging algorithm is used to reconstruct the small circular electromagnetic maps. The class center is the center of the circle and the radius is certain. After that, the obtained small area electromagnetic map data and the data obtained from the sample points are used for domain mapping processing, and the electromagnetic data of a larger area of the target area is obtained. Finally, the overall electromagnetic map is reconstructed through data fusion. The simulation results show that the proposed algorithm is better than several interpolation algorithms. When sample points account for 0.1 of total data points, the RMSE of the result is less than 1.5.

An Improved K-Means Algorithm Based on Evidence Distance

The main influencing factors of the clustering effect of the k-means algorithm are the selection of the initial clustering center and the distance measurement between the sample points. The traditional k-mean algorithm uses Euclidean distance to measure the distance between sample points, thus it suffers from low differentiation of attributes between sample points and is prone to local optimal solutions. For this feature, this paper proposes an improved k-means algorithm based on evidence distance. Firstly, the attribute values of sample points are modelled as the basic probability assignment (BPA) of sample points. Then, the traditional Euclidean distance is replaced by the evidence distance for measuring the distance between sample points, and finally k-means clustering is carried out using UCI data. Experimental comparisons are made with the traditional k-means algorithm, the k-means algorithm based on the aggregation distance parameter, and the Gaussian mixture model. The experimental results show that the improved k-means algorithm based on evidence distance proposed in this paper has a better clustering effect and the convergence of the algorithm is also better.

Exploring Geostatistical Modeling and VisualizationTechniques of Uncertainties for Categorical Spatial Data

This article presents and analyzes the indicator geostatistical modeling and some visualization techniques of uncertainty models for categorical spatial attributes. A set of sample points of some categorical attribute is used as input information. The indicator approach requires a transformation of sample points on fields of indicator samples according to the classes of interest. Experimental and theoretical semivariograms of the indicator fields are defined representing the spatial variation of the indicator information. The indicator fields, along with their semivariograms, are used to determine the uncertainty model, the conditioned probability distribution function, of the attribute at any location inside the geographic region delimited by the samples. The probability functions are considered for producing prediction and probability maps based on the maximum class probability criterion. These maps can be visualized using different techniques. In this work, it is considered individual visualization of the predicted and probability maps and a combination of them. The predicted maps can also be visualized with or without constraints related to the uncertainty probabilities. The combined visualizations are based on three-dimensional (3D) planar projection and on the Red-Green-Blue to Intensity-Hue-Saturation (RGB-IHS) fusion transformation techniques. The methodology of this article is illustrated by a case study with real data, a sample set of soil textures observed in an experimental farm located in the region of São Carlos city in São Paulo State, Brazil. The resulting maps of this case study are presented and the advantages and the drawbacks of the visualization options are analyzed and discussed.

Spatial Evaluation of Earthquake Disaster Based On Correlation Characteristics And BP Neural Network

Rapid spatial evaluation of disaster after earthquake occurrence is required in the emergency rescue management, due to its significant support for decreasing casualties and property losses. The earthquake-hit population is taken as an example of earthquake disaster to construct the evaluation model using the data from the 2013 Ms7.0 Lushan earthquake. Ten influencing factors are classified into environmental factors and seismic factors. The correlation analysis reveals characteristics that there is a nonlinear relationship between the earthquake-hit population and various factors, and per capita GDP and PGA factor have a stronger correlation with earthquake-hit population. Moreover, the spatial variability of influencing factors would affect the distribution of earthquake-hit population. The earthquake-hit population is evaluated using BP neural network with optimizing training samples based on the spatial characteristics of per capita GDP and PGA factors. Different number of sample points are generated in areas with different value intervals of influencing factors, instead of the random distribution of sample points. The minimum value of RMSE (Root Mean Square Error) from testing set is 18 people/km2, showing good accuracy in the spatial evaluation of earthquake-hit population. Meanwhile, the optimizing samples considering spatial characteristics could improve the convergence speed and generalization capability comparing to random samples. The trained network was generalized to the 2017 Ms7.0 Jiuzhaigou earthquake to verify the prediction accuracy. The evaluation results indicate that BP neural network considering the correlation characteristics of factors has the capability to evaluate the seismic disaster information in space, providing more detailed information for emergency service and rescue operation.