Pattern Classification and Recognition of Invertebrate Functional Groups Using Self-Organizing Neural Networks

Self-organizing neural networks can be used to mimic non-linear systems. The main objective of this study is to make pattern classification and recognition on sampling information using two self-organizing neural network models. Invertebrate functional groups sampled in the irrigated rice field were classified and recognized using one-dimensional self-organizing map and self-organizing competitive learning neural networks. Comparisons between neural network models, distance (similarity) measures, and number of neurons were conducted. The results showed that self-organizing map and self-organizing competitive learning neural network models were effective in pattern classification and recognition of sampling information. Overall the performance of one-dimensional self-organizing map neural network was better than self-organizing competitive learning neural network. The number of neurons could determine the number of classes in the classification. Different neural network models with various distance (similarity) measures yielded similar classifications. Some differences, dependent upon the specific network structure, would be found. The pattern of an unrecognized functional group was recognized with the self-organizing neural network. A relative consistent classification indicated that the following invertebrate functional groups, terrestrial blood sucker; terrestrial flyer; tourist (nonpredatory species with no known functional role other than as prey in ecosystem); gall former; collector (gather, deposit feeder); predator and parasitoid; leaf miner; idiobiont (acarine ectoparasitoid), were classified into the same group, and the following invertebrate functional groups, external plant feeder; terrestrial crawler, walker, jumper or hunter; neustonic (water surface) swimmer (semi-aquatic), were classified into another group. It was concluded that reliable conclusions could be drawn from comparisons of different neural network models that use different distance (similarity) measures. Results with the larger consistency will be more reliable.     

Feature extraction using Hough transform for solid waste bin level detection and classification

This paper deals with the solid waste image detection and classification to detect and classify the solid waste bin level. To do so, Hough transform techniques is used for feature extraction to identify the line detection based on image’s gradient field. The feedforward neural network (FFNN) model is used to classify the level content of solid waste based on learning concept. Numbers of training have been performed using FFNN to learn and match the targets of the testing images to compute the sum squared error with the performance goal met. The images for each class are used as input samples for classification. Result from the neural network and the rules decision are used to build the receiver operating characteristic (ROC) graph. Decision graph shows the performance of the system waste system based on area under curve (AUC), WS-class reached 0.9875 for excellent result and WS-grade reached 0.8293 for good result. The system has been successfully designated with the motivation of solid waste bin monitoring system that can applied to a wide variety of local municipal authorities system.     

Formation and modeling of disinfection by-products in drinking water of six cities in China

Water quality parameters including TOC, UV(254), pH, chlorine dosage, bromide concentration and disinfection by-products were measured in water samples from 41 water treatment plants of six selected cities in China. Chloroform, bromodichloromethane, dibromochloromethane, dichloroacetic acid and trichloroacetic acid were the major disinfection by-products in the drinking water of China. Bromoform and dibromoacetic acid were also detected in many water samples. Higher concentrations of trihalomethanes and haloacetic acids were measured in summer compared to winter. The geographical variations in DBPs showed that TTHM levels were higher in Zhengzhou and Tianjin than other selected cities. And the HAA5 levels were highest in Changsha and Tianjin. The modeling procedure that predicts disinfection by-products formation was studied and developed using artificial neural networks. The performance of the artificial neural networks model was excellent (r > 0.84).     

基于深度学习技术的藻类智能监测系统开发

浮游藻类对水环境的变化非常敏感,是评价水环境质量的重要指示生物.传统的浮游藻类监测依靠人工采样分析,需要专业检测人员使用显微镜对藻细胞逐一鉴定并计数,耗时耗力且严重依赖检测人员的专业知识与鉴定经验,限制了浮游藻类监测工作的标准化推广和普及应用.利用神经网络模型建立了一套浮游藻类智能监测系统,该系统能够实现浮游藻类检测的...     

Assessment of groundwater quality: a fusion of geochemical and geophysical information via Bayesian neural networks

Deplorable quality of groundwater arising from saltwater intrusion, natural leaching and anthropogenic activities is one of the major concerns for the society. Assessment of groundwater quality is, therefore, a primary objective of scientific research. Here, we propose an artificial neural network-based method set in a Bayesian neural network (BNN) framework and employ it to assess groundwater quality. The approach is based on analyzing 36 water samples and inverting up to 85 Schlumberger vertical electrical sounding data. We constructed a priori model by suitably parameterizing geochemical and geophysical data collected from the western part of India. The posterior model (post-inversion) was estimated using the BNN learning procedure and global hybrid Monte Carlo/Markov Chain Monte Carlo optimization scheme. By suitable parameterization of geochemical and geophysical parameters, we simulated 1,500 training samples, out of which 50 % samples were used for training and remaining 50 % were used for validation and testing. We show that the trained model is able to classify validation and test samples with 85 % and 80 % accuracy respectively. Based on cross-correlation analysis and Gibb’s diagram of geochemical attributes, the groundwater qualities of the study area were classified into following three categories: “Very good”, “Good”, and “Unsuitable”. The BNN model-based results suggest that groundwater quality falls mostly in the range of “Good” to “Very good” except for some places near the Arabian Sea. The new modeling results powered by uncertainty and statistical analyses would provide useful constrain, which could be utilized in monitoring and assessment of the groundwater quality.     

Plants as bio-monitors for Cs-137, Pu-238, Pu-239,240 and K-40 at the Savannah River Site

The Savannah River Site was constructed in South Carolina to produce plutonium (Pu) in the 1950s. Discharges associated with these now-ceased operations have contaminated large areas within the site, particularly streams associated with reactor cooling basins. Evaluating the exposure risk of contamination to an ecosystem requires methodologies that can assess the bioavailability of contaminants. Plants, as primary producers, represent an important mode of transfer of contaminants from soils and sediments into the food chain. The objective of this study was to identify local area plants for their ability to act as bio-monitors of radionuclides. The concentrations of cesium-137 ((137)Cs), potassium-40 ((40)K), (238)Pu and (239,240)Pu in plants and their associated soils were determined using γ and α spectrometry. The ratio of contamination concentration found in the plant relative to the soil was calculated to assess a concentration ratio (CR). The highest CR for (137)Cs was found in Pinus palustris needles (CR of 2.18). The correlation of soil and plant (137)Cs concentration was strong (0.76) and the R(2) (0.58) from the regression was significant (p = 0.006). This suggests the ability to predict the degree of (137)Cs contamination of a soil through analysis of the pine needles. The (238)Pu and (239,240)Pu concentrations were most elevated within the plant roots. Extremely high CR values were found in Sparganium americanum (bur-reed) roots with a value of 5.86 for (238)Pu and 5.66 for (239,240)Pu. The concentration of (40)K was measured as a known congener of (137)C. Comparing (40)K and (137)C concentrations in each plant revealed an inverse relationship for these radioisotopes. Correlating (40)K and (137)Cs was most effective in identifying plants that have a high affinity for (137)Cs uptake. The P. palustris and S. americanum proved to be particularly strong accumulators of all K congeners from the soil. Some species that were measured, warrant further investigation, are the carnivorous plant Utricularia inflata (bladderwort) and the emergent macrophyte Juncus effusus. For U. inflata, the levels of (137)Cs, (238)Pu, and (239,240)Pu (which were 3922, 8399, and 803 Bq kg(-1), respectively) in the leaves were extremely high. The highest (137)Cs concentration from the study was measured in the J. effusus root (5721 Bq kg(-1)).     

Radionuclide Concentrations in Benthic Invertebrates from Amchitka and Kiska Islands in the Aleutian Chain, Alaska

Concentrations of 13 radionuclides (¹³⁷Cs, ¹²⁹I, ⁶⁰Co, ¹⁵²Eu, ⁹⁰Sr, ⁹⁹Tc, ²⁴¹Am, ²³⁸Pu, ^239,249Pu, ²³⁴U, ²³⁵U, ²³⁶U, ²³⁸U were examined in seven species of invertebrates from Amchitka and Kiska Islands, in the Aleutian Chain of Alaska, using gamma spectroscopy, inductively coupled plasma mass spectroscopy, and alpha spectroscopy. Amchitka Island was the site of three underground nuclear test (1965–1971), and we tested the null hypotheses that there were no differences in radionuclide concentrations between Amchitka and the reference site (Kiska) and there were no differences among species. The only radionuclides where composite samples were above the Minimum Detectable Activity (MDA) were ¹³⁷Cs, ²⁴¹Am, ^239,249Pu, ²³⁴U, ²³⁵U, ²³⁶U, and ²³⁸U. Green sea urchin (Strongylocentrotus polyacanthus), giant chiton (Cryptochiton stelleri), plate limpets (Tectura scutum) and giant Pacific octopus (Enteroctopus dofleini) were only tested for ¹³⁷Cs; octopus was the only species with detectable levels of ¹³⁷Cs (0.262 ± 0.029 Bq/kg, wet weight). Only rock jingle (Pododesmus macroschisma), blue mussel (Mytilus trossulus) and horse mussel (Modiolus modiolus) were analyzed for the actinides. There were no interspecific differences in ²⁴¹Am and ^239,240Pu, and almost no samples above the MDA for ²³⁸Pu and ²³⁶U. Horse mussels had significantly higher concentrations of ²³⁴U (0.844 ± 0.804 Bq/kg) and ²³⁸U (0.730 ± 0.646) than the other species (both isotopes are naturally occurring). There were no differences in actinide concentrations between Amchitka and Kiska. In general, radionuclides in invertebrates from Amchitka were similar to those from uncontaminated sites in the Northern Hemisphere, and below those from the contaminated Irish Sea. There is a clear research need for authors to report the concentrations of radionuclides by species, rather than simply as ‘shellfish’, for comparative purposes in determining geographical patterns, understanding possible effects, and for estimating risk to humans from consuming different biota.     

基于B—P网络的大气环境质量评价

选取大气环境质量标准作为运算样本，以大气污染物各级标准值作为样本输入信息，建立了大气环境质量的Ｂ－Ｐ网络评价模型，该模型用于武汉市大气环境质量评价，并与用模糊数学评价结果比较，表明Ｂ－Ｐ人工神经网络用于大气环境质量评价具有通用性、合理性和实用性。     

BP网络应用于大气颗粒物的源解析

应用BP网络对大气颗粒物进行源解析,将大气采集样本中的元素含量和大气颗粒物源成分谱构成训练样本集,用BP网络进行训练,由训练好的网络的权值可以计算出大气颗粒物的污染排放源的权重贡献率.将BP源解析法的计算结果与其它源解析法得到的结果比较,表明BP网络应用于大气颗粒物的源解析是可行的.     

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

Identification and quantification of dissolved oxygen (DO) profiles of river is one of the primary concerns for water resources managers. In this research, an artificial neural network (ANN) was developed to simulate the DO concentrations in the Heihe River, Northwestern China. A three-layer back-propagation ANN was used with the Bayesian regularization training algorithm. The input variables of the neural network were pH, electrical conductivity, chloride (Cl^?), calcium (Ca²⁺), total alkalinity, total hardness, nitrate nitrogen (NO₃-N), and ammonical nitrogen (NH₄-N). The ANN structure with 14 hidden neurons obtained the best selection. By making comparison between the results of the ANN model and the measured data on the basis of correlation coefficient (r) and root mean square error (RMSE), a good model-fitting DO values indicated the effectiveness of neural network model. It is found that the coefficient of correlation (r) values for the training, validation, and test sets were 0.9654, 0.9841, and 0.9680, respectively, and the respective values of RMSE for the training, validation, and test sets were 0.4272, 0.3667, and 0.4570, respectively. Sensitivity analysis was used to determine the influence of input variables on the dependent variable. The most effective inputs were determined as pH, NO₃-N, NH₄-N, and Ca²⁺. Cl^? was found to be least effective variables on the proposed model. The identified ANN model can be used to simulate the water quality parameters.     

Precipitable water modelling using artificial neural network in Çukurova region

Precipitable water (PW) is an important atmospheric variable for climate system calculation. Local monthly mean PW values were measured by daily radiosonde observations for the time period from 1990 to 2006. Artificial neural network (ANN) method was applied for modeling and prediction of mean precipitable water data in Çukurova region, south of Turkey. We applied Levenberg–Marquardt (LM) learning algorithm and logistic sigmoid transfer function in the network. In order to train our neural network we used data of Adana station, which are assumed to give a general idea about the precipitable water of Çukurova region. Thus, meteorological and geographical data (altitude, temperature, pressure, and humidity) were used in the input layer of the network for Çukurova region. Precipitable water was the output. Correlation coefficient (R²) between the predicted and measured values for monthly mean daily sum with LM method values was found to be 94.00% (training), 91.84% (testing), respectively. The findings revealed that the ANN-based prediction technique for estimating PW values is as effective as meteorological radiosonde observations. In addition, the results suggest that ANN method values be used so as to predict the precipitable water.     

Prediction of near-surface soil moisture at large scale by digital terrain modeling and neural networks

The capability of Artificial Neural Network models to forecast near-surface soil moisture at fine spatial scale resolution has been tested for a 99.5 ha watershed located in SW Spain using several easy to achieve digital models of topographic and land cover variables as inputs and a series of soil moisture measurements as training data set. The study methods were designed in order to determining the potentials of the neural network model as a tool to gain insight into soil moisture distribution factors and also in order to optimize the data sampling scheme finding the optimum size of the training data set. Results suggest the efficiency of the methods in forecasting soil moisture, as a tool to assess the optimum number of field samples, and the importance of the variables selected in explaining the final map obtained.     

Forecasting of daily total atmospheric ozone in Isfahan

A neural network combined to an artificial neural network model is used to forecast daily total atmospheric ozone over Isfahan city in Iran. In this work, in order to forecast the total column ozone over Isfahan, we have examined several neural networks algorithms with different meteorological predictors based on the ozone-meteorological relationships with previous day's ozone value. The meteorological predictors consist of temperatures (dry and dew point) and geopotential heights at standard levels of 100, 50, 30, 20 and 10 hPa with their wind speed and direction. These data together with previous day total ozone forms the input matrix of the neural model that is based on the back propagation algorithm (BPA) structure. The output matrix is the daily total atmospheric ozone. The model was build based on daily data from 1997 to 2004 obtained from Isfahan ozonometric station data. After modeling these data we used 3 year (from 2001 to 2003) of daily total ozone for testing the accuracy of model. In this experiment, with the final neural network, the total ozone are fairly well predicted, with an Agreement Index 76%.     

Artificial neural network modeling of dissolved oxygen in reservoir

The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.     

Artificial neural network analysis for reliability prediction of regional runoff utilization

Many factors in the reliability analysis of planning the regional rainwater utilization tank capacity need to be considered. Based on the historical daily rainfall data, the following four analyzing procedures will be conducted: the regional daily rainfall frequency, the amount of runoff, the water continuity, and the reliability. Thereafter, the suggested designed storage capacity can be obtained according to the conditions with the demand and supply reliability. By using the output data, two different types of artificial neural network models are used to build up small area rainfall–runoff supply systems for the simulation of reliability and the prediction model. They are also used for the testing of stability and learning speed assessment. Based on the result of this research, the radial basis function neural network (RBFNN) model, using the Gaussian function that has a similar trend as the nature as basic function, has better stability than using the back-propagation neural network (BPNN) model. Despite the fact that RBFNN was more reliable than BPNN, it still made a conservative estimate for the actual monitoring data. The error rate of RBFNN was still higher than the correction of BPNN 4-3-1-1. This should have significant benefit in the future application of the instantaneous prediction or the development of related intelligent instantaneous control equipment.     

基于BP神经网络的贵阳市空气质量指数预报模型

采用贵阳市2013年1月1日—2015年12月31日的空气质量指数(AQI)日均值,常规的地面和高空观测资料,基于不同季节,调整BP神经网络的隐藏层个数和隐藏层节点数,建立不同的BP神经网络预报模型,进行参数检验,最终选取预报效果最好的模型带入实况进行检验。结果表明,夏季的预报效果最好,采用的模型TS评分为81.6%,平均绝对误差为9.1,正确率为97.4%,用该模型检验预报效果,实况和预报的相关系数为0.71,平均误差为9;而冬季的预报效果明显低于其他季节,采用的模型TS评分为65.7%,平均绝对误差为19.5,正确率为72.9%,用该模型检验预报效果,实况和预报的相关系数为0.79,平均误差为19。而且BP神经网络模型的预报效果同隐藏层个数与隐藏层节点数没有显著关系。     

Developing a fuzzy neural network-based support vector regression (FNN-SVR) for regionalizing nitrate concentration in groundwater

The aim of this study is to develop a fuzzy neural network-based support vector regression model (FNN-SVR) for mapping crisp-input and fuzzy-output variables. In this model, an artificial neural network (ANN) estimator based on multilayer perceptron (MLP) is considered as the kernel function of the SVR, whereas asymmetric triangular fuzzy H-level sets are assumed for model parameters including weight and biases of the ANN model. A genetic algorithm (GA) with real coding is implemented to optimize the model parameters during the training phase. To evaluate the efficiency and applicability of the proposed model, it is applied for simulating and regionalizing nitrate concentration in Karaj Aquifer in Iran. The goodness-of-fit criteria indicate a better performance of the FNN-SVR compared to some benchmark models such as geostatistic techniques as well as traditional SVR models with linear, quadratic, polynomial, and Gaussian kernel functions for modeling nitrate concentrations in groundwater.     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

人工神经网络在水环境质量评价中的应用

为了将人工神经网络应用于水环境质量评价，应用了人工神经网络Ｂ—Ｐ算法，构造了水环境质量评价模型，该模型应用于实例评价结果表明，人工神经网络用于环境质量评价具有客观性，通用性和实用性。