Estimation of soil dispersivity using soft computing approaches

The accurate estimation of soil dispersivity (α) is required for characterizing the transport of contaminants in soil. The in situ measurement of α is costly and time-consuming. Hence, in this study, three soft computing methods, namely adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and gene expression programming (GEP), are used to estimate α from more readily measurable physical soil variables, including travel distance from source of pollutant (L), mean grain size (D ₅₀), soil bulk density (ρ _b), and contaminant velocity (V _c). Based on three statistical metrics [i.e., mean absolute error, root-mean-square error (RMSE), and coefficient of determination (R ²)], it is found that all approaches (ANN, ANFIS, and GEP) can accurately estimate α. Results also show that the ANN model (with RMSE = 0.00050 m and R ² = 0.977) performs better than the ANFIS model (with RMSE = 0.00062 m and R ² = 0.956), and the estimates from GEP are almost as accurate as those from ANFIS. The performance of ANN, ANFIS, and GEP models is also compared with the traditional multiple linear regression (MLR) method. The comparison indicates that all of the soft computing methods outperform the MLR model. Finally, the sensitivity analysis shows that the travel distance from source of pollution (L) and bulk density (ρ _b) have, respectively, the most and the least effect on the soil dispersivity.

     

Comparison of Landsat 8 OLI and Landsat 7 ETM+ for estimating grassland LAI using model inversion and spectral indices: case study of Mpumalanga,South Africa

The leaf area index (LAI) is the key biophysical indicator used to assess the condition of rangeland. In this study, we investigated the implications of narrow spectral response, high radiometric resolution (12 bits), and higher signal-to-noise ratio of the Landsat 8 Operational Land Imager (OLI) sensor for the estimation of LAI. The Landsat 8 LAI estimates were compared to that of its predecessors, namely Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (8 bits). Furthermore, we compared the radiative transfer model (RTM) and spectral indices approaches for estimating LAI on rangeland systems in South Africa. The RTM was inverted using artificial neural network (ANN) and lookup table (LUT) algorithms. The accuracy of the models was higher for Landsat 8 OLI, where ANN (root mean squared error, RMSE = 0. 13; R² = 0. 89), LUT (RMSE = 0. 25; R² = 0. 50), compared to Landsat 7 ETM+, where ANN (RMSE = 0. 35; R² = 0. 60), LUT (RMSE = 0. 38; R² = 0. 50). Compared to an empirical approach, the RTM provided higher accuracy. In conclusion, Landsat 8 OLI provides an improvement for the estimation of LAI over Landsat 7 ETM+. This is useful for rangeland monitoring.     

Comparison of individual and combined ANN models for prediction of air and dew point temperature

Predicted air and dew point temperatures can be valuable in decision making in many areas including protecting crops from damage, avoiding heat stress on animals and humans, and in planning related to energy management. Current web-based artificial neural network (ANN) models on the Automated Environment Monitoring Network (AEMN) in Georgia predict hourly air and dew point temperature for twelve prediction horizons, using 24 models. The observed air temperature may approach the observed dew point temperature, but never goes below it. Current web based ANN models have prediction errors which, when the air and dew point temperatures are close, may cause air temperature to be predicted below the dew point temperature. Herein this error is referred to as a prediction anomaly. The goal of this research was to improve the prediction accuracy of existing air and dew point temperature ANN models by combining the two weather variables into a single ANN model for each prediction horizon. The objectives of this study were to reduce the mean absolute error (MAE) of prediction and to reduce the number of prediction anomalies. The combined models produced a reduction in the air temperature MAE for ten of twelve prediction horizons with an average reduction in MAE of 1.93 %. The combined models produced a reduction in the dew point temperature MAE for only six of twelve prediction horizons with essentially no average decrease in MAE. However, the combined models showed a marked reduction in prediction anomalies for all twelve prediction horizons with an average reduction of 34.1 %. The reduction in prediction anomalies ranged from 4.6 % at the one-hour horizon to 60.5 % at the eleven-hour horizon.     

Estimating plot volume using lidar height and intensity distributional parameters

This study explored the feasibility of height distributional metrics and intensity values extracted from low-density airborne light detection and ranging (lidar) data to estimate plot volumes in dense Korean pine (Pinus koraiensis) plots. Multiple linear regression analyses were performed using lidar height and intensity distributional metrics. The candidate variables for predicting plot volume were evaluated using three data sets: total, canopy, and integrated lidar height and intensity metrics. All intensities of lidar returns used were corrected by the reference distance. Regression models were developed using each data set, and the first criterion used to select the best models was the corrected Akaike Information Criterion (AIC_c). The use of three data sets was statistically significant at R² = 0.75 (RMSE = 52.17 m³ ha^?1), R² = 0.84 (RMSE = 45.24 m³ ha^?1), and R² = 0.91 (RMSE = 31.48 m³ ha^?1) for total, canopy, and integrated lidar distributional metrics, respectively. Among the three data sets, the integrated lidar metrics-derived model showed the best performance for estimating plot volumes, improving errors up to 42% when compared to the other two data sets. This is attributed to supplementing variables weighted and biased to upper limits in dense plots with more statistical variables that explain the lower limits. In all data sets, intensity metrics such as skewness, kurtosis, standard deviation, minimum, and standard error were employed as explanatory variables. The use of intensity variables improved the accuracy of volume estimation in dense forests compared to prior research. Correction of the intensity values contributed up to a maximum of 58% improvement in volume estimation when compared to the use of uncorrected intensity values (R² = 0.78, R² = 0.53, and R² = 0.63 for total, canopy, and integrated lidar distributional metrics, respectively). It is clear that the correction of intensity values is an essential step for the estimation of forest volume.     

Comparison and validation of artificial intelligent techniques to estimate intestinal broiler microflora

This study was carried out to investigate the ability of major mathematical methods to estimate intestinal broiler microflora population. Artificial neural network (ANN), coactive neuro-fuzzy inference system (CANFIC), and artificial neural network genetic algorithm (ANNGA) were used in this respect. The lactic acid bacteria and Enterobacteriaceae were applied as models of microflora. Input and output variables were considered as time and microflora population, respectively. The best model of ANN, CANFIC, and ANNGA was determined based on the coefficient of determination and root mean square error criteria. The results of the current study have shown that ANN, ANNGA, and CANFIS are accurate methods to estimate lactic acid bacteria and Enterobacteriaceae. The highest accuracy of microflora estimation was related to 7 days of age. The efficiency of intelligent models to lactic acid bacteria and Enterobacteriaceae has shown that ANNGA had better prediction between mentioned models. The models estimated Enterobacteriaceae population better than that for lactic acid bacteria.     

Forecasting highway casualties under the effect of railway development policy in Turkey using artificial neural networks

This study presents forecast of highway casualties in Turkey using nonlinear multiple regression (NLMR) and artificial neural network (ANN) approaches. Also, the effect of railway development on highway safety using ANN models was evaluated. Two separate NLMR and ANN models for forecasting the number of accidents (A) and injuries (I) were developed using 27 years of historical data (1980–2006). The first 23 years data were used for training, while the remaining data were utilized for testing. The model parameters include gross national product per capita (GNP-C), numbers of vehicles per thousand people (V-TP), and percentage of highways, railways, and airways usages (TSUP-H, TSUP-R, and TSUP-A, respectively). In the ANN models development, the sigmoid and linear activation functions were employed with feed-forward back propagation algorithm. The performances of the developed NLMR and ANN models were evaluated by means of error measurements including mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R ²). ANN models were used for future estimates because NLMR models produced unreasonably decreasing projections. The number of road accidents and as well as injuries was forecasted until 2020 via different possible scenarios based on (1) taking TSUPs at their current trends with no change in the national transport policy at present, and (2) shifting passenger traffic from highway to railway at given percentages but leaving airway traffic with its current trend. The model results indicate that shifting passenger traffic from the highway system to railway system resulted in a significant decrease on highway casualties in Turkey.     

Non-tuned machine learning approach for hydrological time series forecasting

Stream-flow forecasting is a crucial task for hydrological science. Throughout the literature, traditional and artificial intelligence models have been applied to this task. An attempt to explore and develop better expert models is an ongoing endeavor for this hydrological application. In addition, the accuracy of modeling, confidence and practicality of the model are the other significant problems that need to be considered. Accordingly, this study investigates modern non-tuned machine learning data-driven approach, namely extreme learning machine (ELM). This data-driven approach is containing single layer feedforward neural network that selects the input variables randomly and determine the output weights systematically. To demonstrate the reliability and the effectiveness, one-step-ahead stream-flow forecasting based on three time-scale pattern (daily, mean weekly and mean monthly) for Johor river, Malaysia, were implemented. Artificial neural network (ANN) model is used for comparison and evaluation. The results indicated ELM approach superior the ANN model level accuracies and time consuming in addition to precision forecasting in tropical zone. In measureable terms, the dominance of ELM model over ANN model was indicated in accordance with coefficient determination (R ²) root-mean-square error (RMSE) and mean absolute error (MAE). The results were obtained for example the daily time scale R ² = 0.94 and 0.90, RMSE = 2.78 and 11.63, and MAE = 0.10 and 0.43, for ELM and ANN models respectively.

     

Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression

Hyperspectral remote sensing enables the large-scale mapping of canopy biochemical properties. This study explored the possibility of retrieving the concentration of nitrogen, phosphorus, potassium, calcium, magnesium, and sodium from mangroves in the Berau Delta, Indonesia. The objectives of the study were to (1) assess the accuracy of foliar chemistry retrieval, (2) compare the performance of models based on support vector regression (SVR), i.e. ?-SVR, ν-SVR, and least squares SVR (LS-SVR), to models based on partial least squares regression (PLSR), and (3) investigate which spectral transformations are best suited. The results indicated that nitrogen could be successfully modelled at the landscape level (R² = 0.67, root mean square error (RMSE) = 0.17, normalized RMSE (nRMSE) = 15%), whereas estimations of P, K, Ca, Mg, and Na were less encouraging. The developed nitrogen model was applied over the study area to generate a map of foliar N variation, which can be used for studying ecosystem processes in mangroves. While PLSR attained good results directly using all untransformed bands, the highest accuracy for nitrogen modelling was achieved using a combination of LS-SVR and continuum-removed derivative reflectance. All SVR techniques suffered from multicollinearity when using the full spectrum, and the number of independent variables had to be reduced by singling out the most informative wavelength bands. This was achieved by interpreting and visualizing the structure of the PLSR and SVR models.     

Prediction of rice crop yield using MODIS EVI−LAI data in the Mekong Delta,Vietnam

Predicting rice crop yield at the regional scale is important for production estimates that ensure food security for a country. This study aimed to develop an approach for rice crop yield prediction in the Vietnamese Mekong Delta using the Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and leaf area index (LAI). Data processing consisted of four main steps: (1) constructing time-series vegetation indices, (2) noise filtering of time-series data using the empirical mode decomposition (EMD), (3) establishment of crop yield models, and (4) model validation. The results indicated that the quadratic model using two variables (EVI and LAI) produced more accurate results than other models (i.e. linear, interaction, pure quadratic, and quadratic with a single variable). The highest correlation coefficients obtained at the ripening period for the spring–winter and autumn–summer crops were 0.70 and 0.74, respectively. The robustness of the established models was evaluated by comparisons between the predicted yields and crop yield statistics for 10 sampling districts in 2006 and 2007. The comparisons revealed satisfactory results for both years, especially for the spring–winter crop. In 2006, the root mean squared error (RMSE), mean absolute error (MAE), and mean bias error (MBE) for the spring–winter crop were 10.18%, 8.44% and 0.9%, respectively, while the values for the autumn–summer crop were 17.65%, 14.06%, and 3.52%, respectively. In 2007, the spring–winter crop also yielded better results (RMSE = 10.56%, MAE = 9.14%, MBE = 3.68%) compared with the autumn–summer crop (RMSE = 17%, MAE = 12.69%, MBE = 2.31%). This study demonstrates the merit of using MODIS data for regional rice crop yield prediction in the Mekong Delta before the harvest period. The methods used in this study could be transferable to other regions around the world.     

Improvements in the estimation of daily minimum air temperature in peninsular Spain using MODIS land surface temperature

Air temperature (T_a) is a key variable in many environmental risk models and plays a very important role in climate change research. In previous studies we developed models for estimating the daily maximum (T_max), mean (T_mean), and minimum air temperature (T_min) in peninsular Spain over cloud-free land areas using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Those models were obtained empirically through linear regressions between daily T_a and daytime Terra-MODIS land surface temperature (LST), and then optimized by including spatio-temporal variables. The best T_mean and T_max models were satisfactory (coefficient of determination (R²) of 0.91–0.93; and residual standard error (RSE) of 1.88–2.25 K), but not the T_min models (R² = 0.80–0.81 and RSE = 2.83–3.00 K). In this article T_min models are improved using night-time Aqua LST instead of daytime Terra LST, and then refined including total precipitable water (W) retrieved from daytime Terra-MODIS data and the spatio-temporal variables curvature (c), longitude (λ), Julian day of the year (JD) and elevation (h). The best T_min models are based on the National Aeronautics and Space Administration (NASA) standard product MYD11 LST; and on the direct broadcast version of this product, the International MODIS/AIRS Processing Package (IMAPP) LST product. Models based on Sobrino’s LST1 algorithm were also tested, with worse results. The improved T_min models yield R² = 0.91–0.92 and RSE = 1.75 K and model validations obtain similar R² and RSE values, root mean square error of the differences (RMSD) of 1.87–1.88 K and bias = 0.11 K. The main advantage of the T_min models based on the IMAPP LST product is that they can be generated in nearly real-time using the MODIS direct broadcast system at the University of Oviedo.     

Quantifying suspended sediment concentration in subglacial sediment plumes discharging from two Svalbard tidewater glaciers using Landsat-8 and in situ measurements

Marine-terminating outlet glaciers discharge mass through iceberg calving, submarine melting, and meltwater run-off. While calving can be quantified by in situ and remote-sensing observations, meltwater run-off, the subglacial transport of meltwater, and submarine melting are not well constrained due to inherent difficulties observing the subglacial and proglacial environments at tidewater glaciers. Remote-sensing and in situ measurements of surface sediment plumes, and their suspended sediment concentration (SSC), have been used as a proxy for glacier meltwater run-off. However, this relationship between satellite reflectance and SSC has predominantly been established using land-terminating glaciers. Here, we use two Svalbard tidewater glaciers to establish a well-constrained relationship between Landsat-8 surface reflecance and SSC and argue that it can be used to measure relative meltwater run-off at tidewater glaciers throughout a summer melt season. We find the highest correlation between SSCs and Landsat-8 surface reflectance by using the red + NIR band combination (r² = 0.76). The highest correlation between SSCs and in situ field spectrometer measurements is in the 740–800 nm wavelength range (r² = 0.85), a spectral range not currently measured by Landsat. Additionally, we find that in situ and Landsat-8 measurements for surface reflectance of SSCs are not interchangeable and therefore establish a relationship for each detection method. We then use the Landsat-8 relationship to calculate total surface sediment load, finding a strong correlation between total surface sediment load and a proxy for meltwater run-off (r² ≥ 0.89). Our results establish a new metric to calculate SSCs from Landsat-8 surface reflectance and demonstrate how the SSC of subglacial sediment plumes can be used to monitor relative seasonal meltwater discharge at tidewater glaciers.     

Artificial neural networks application to predict the ultimate tensile strength of X70 pipeline steels

The paper presents some results of the research connected with the development of new approach based on the artificial neural network (ANN) of predicting the ultimate tensile strength of the API X70 steels after thermomechanical treatment. The independent variables in the model are chemical compositions (carbon equivalent), based upon the International Institute of Welding equation (CE_IIW), the carbon equivalent, based upon the chemical portion of the Ito-Bessyo carbon equivalent equation (CE_Pcm), the sum of the niobium, vanadium and titanium concentrations (VTiNb), the sum of the niobium and vanadium concentrations (NbV), the sum of the chromium, molybdenum, nickel and copper concentrations (CrMoNiCu), Charpy impact energy at ?10 °C (CVN) and yield strength at 0.005 offset (YS). For purpose of constructing these models, 104 different data were gathered from the experimental results. The data used in the ANN model is arranged in a format of seven input parameters that cover the chemical compositions, yield stress and Charpy impact energy, and output parameter which is ultimate tensile strength. In this model, the training, validation and testing results in the ANN have shown strong potential for prediction of relations between chemical compositions and mechanical properties of API X70 steels.     

Estimating storm discharge and water quality data uncertainty: A software tool for monitoring and modeling applications

Uncertainty estimates corresponding to measured hydrologic and water quality data can contribute to improved monitoring design, decision-making, model application, and regulatory formulation. With these benefits in mind, the Data Uncertainty Estimation Tool for Hydrology and Water Quality (DUET-H/WQ) was developed from an existing uncertainty estimation framework for small watershed discharge, sediment, and N and P data. Both the software and its framework-basis utilize the root mean square error propagation methodology to provide uncertainty estimates instead of more rigorous approaches requiring detailed statistical information, which is rarely available. DUET-H/WQ lists published uncertainty information for data collection procedures to assist the user in assigning appropriate data-specific uncertainty estimates and then calculates the uncertainty for individual discharge, concentration, and load values. Results of DUET-H/WQ application in several studies indicated that substantial uncertainty can be contributed by each procedural category (discharge measurement, sample collection, sample preservation/storage, laboratory analysis, and data processing and management). For storm loads, the uncertainty was typically least for discharge (±7–23%), greater for sediment (±16–27%) and dissolved N and P (±14–31%) loads, and greater yet for total N and P (±18–36%). When these uncertainty estimates for individual values were aggregated within study periods (i.e. total discharge, average concentration, and total load), uncertainties followed the same pattern (Q < TSS < dissolved N and P < total N and P). This rigorous demonstration of uncertainty in discharge and water quality data illustrates the importance of uncertainty analysis and the need for appropriate tools. It is our hope that DUET-H/WQ contributes to making uncertainty estimation a routine data collection and reporting procedure and thus enhances environmental monitoring, modeling, and decision-making. Hydrologic and water quality data are too important for scientists to continue to ignore the inherent uncertainty.     

Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network

The purpose of this article is to evaluate and predict blast-induced ground vibration at Shur River Dam in Iran using different empirical vibration predictors and artificial neural network (ANN) model. Ground vibration is a seismic wave that spreads out from the blasthole when explosive charge is detonated in a confined manner. Ground vibrations were recorded and monitored in and around the Shur River Dam, Iran, at different vulnerable and strategic locations. A total of 20 blast vibration records were monitored, out of which 16 data sets were used for training of the ANN model as well as determining site constants of various vibration predictors. The rest of the 4 blast vibration data sets were used for the validation and comparison of the result of ANN and different empirical predictors. Performances of the different predictor models were assessed using standard statistical evaluation criteria. Finally, it was found that the ANN model is more accurate as compared to the various empirical models available. As such, a high conformity (R ² = 0.927) was observed between the measured and predicted peak particle velocity by the developed ANN model.     

Comparison of fuzzy inference system (FIS), FIS with artificial neural networks (FIS + ANN) and FIS with adaptive neuro-fuzzy inference system (FIS + ANFIS) for inventory control

Conventional inventory models mostly cope with a known demand and adequate supply, but are not realistic for many industries. In this research, the fuzzy inference system (FIS) model, FIS with artificial neural network (ANN) model and FIS with adaptive neuro-fuzzy inference system (ANFIS) model in which both supply and demand are uncertain were applied for the inventory system. For FIS model, the generated fuzzy rules were applied to draw out the fuzzy order quantity continuously. The order quantity was adjusted according to the FIS model with the evaluation algorithm for the inventory model. The output of FIS model was also used as data for FIS + ANN and FIS + ANFIS models. The FIS + ANFIS model was studied with three membership functions; trapezoidal and triangular (Trap), Gaussian and bell shape. Inventory costs of the proposed models were compared with the stochastic economic order quantity (EOQ) models based on previous data of a case study factory. The results showed that the FIS + ANFIS_Gauss model gave the best performance of total inventory cost saving by more than 75 % compared to stochastic EOQ model.     

An optimal model for estimating suspended sediment concentration from Landsat TM images in the Caofeidian coastal waters

Suspended sediment concentration (SSC) is one of the most critical parameters in water quality and environmental evaluations. Remote sensing has the potential for monitoring the dynamics and spatial distribution of SSC efficiently. The primary objective of this study is to develop retrieval models that are reliable and sensitive to SSC levels in the Caofeidian area, a new seaport in northeast China, based on Landsat-5 Thematic Mapper (TM) images and a set of in situ data sets, including spectral reflectance data and water quality data. The study finds that the band reflectance ratio and binary combination factor (i.e. the ratio of the reflectance to the particle size) are more effective than single band reflectance, and a non-linear model is more potent than a linear model for predicting SSC in the Caofeidian waters. A quadratic polynomial regression model of the R_TM3/R_TM2 ratio is proposed as the optimal retrieval model after evaluating various models with respect to different sensitive factors. The accuracy of the model is acceptable with a relative error and a root mean square error of 25.35% and 7.22 mg l^–1, respectively; the correlation coefficient between the observed and estimated SSCs is 0.986. This study also indicates that the band reflectance ratio and binary combination factor are effective in weakening and even partially eliminating the effects of the changes in the sediment type (i.e. particle size and refractive index). And the band reflectance ratio is more efficient. Using the proposed model and TM data, SSC levels for the entire region were estimated. Such results can serve as a baseline for future environmental monitoring efforts.     

A comparative modeling study to estimate wear of concrete

This paper investigates the impacts of fuzzy genetic (FG), a new fuzzy logic model with genetic algorithm, artificial neural networks (ANN) and general linear model (GLM) approaches on abrasive wear of concrete. For this purpose, experimental studies were made to investigate the influence on wear of the following input parameters: hematite, cement, compressive strength and different loads on the experiments. In these models, 60 data sets were used. For training set, 48 data (80 %) were randomly selected and the residual data (12 data, 20 %) were test set. Model results were compared with experimental results. In this paper, main model performance criterion was root mean square errors. Also, sum of squared error and determination coefficient statistics were used as comparing criteria for the evaluation of models’ performances. Comparison results indicate that FG models are superior to ANN and GLM models in modeling of influence hematite, cement, compressive strength and loads on wear of concrete.     

ANN based simulation and experimental verification of analytical four- and five-parameters models of PV modules

In this article, artificial neural network (ANN) is adopted to predict photovoltaic (PV) panel behaviors under realistic weather conditions. ANN results are compared with analytical four and five parameter models of PV module. The inputs of the models are the daily total irradiation, air temperature and module voltage, while the outputs are the current and power generated by the panel. Analytical models of PV modules, based on the manufacturer datasheet values, are simulated through Matlab/Simulink environment. Multilayer perceptron is used to predict the operating current and power of the PV module. The best network configuration to predict panel current had a 3–7–4–1 topology. So, this two hidden layer topology was selected as the best model for predicting panel current with similar conditions. Results obtained from the PV module simulation and the optimal ANN model has been validated experimentally. Results showed that ANN model provide a better prediction of the current and power of the PV module than the analytical models. The coefficient of determination (R²), mean square error (MSE) and the mean absolute percentage error (MAPE) values for the optimal ANN model were 0.971, 0.002 and 0.107, respectively. A comparative study among ANN and analytical models was also carried out. Among the analytical models, the five-parameter model, with MAPE = 0.112, MSE = 0.0026 and R² = 0.919, gave better prediction than the four-parameter model (with MAPE = 0.152, MSE = 0.0052 and R² = 0.905). Overall, the 3–7–4–1 ANN model outperformed four-parameter model, and was marginally better than the five-parameter model.     

Northward drift of suspended sediments in the Yangtze estuary in spring

The spatial distribution of suspended sediment concentration (SSC) in spring (February–April) from 2007 to 2013 in the Yangtze (Changjiang) estuary and coastal seas was analysed using remote-sensing data. The results indicated that areas of high SSC drifted northwards along the coast about 100 km away from the Yangtze estuary. SSC increased considerably in both mass and area in 2013. The first principal component analysis (PCA) pattern explained the dominant spatial distribution pattern of SSC in the study area. This highly clustered controlling pattern may have been caused by the joint effects of Yangtze diluted water (YDW), the Taiwan Warm Current (TWC), coastal currents, northeasterly winds, and the shallow coastal shelf. The SSC dispersed and extended into a wide area, and the main range of sediment concentration was located in an area the shape of a trapezium (31.9° N–33.8° N, 122° E–125.5° E, 30.7° N–32.6° N). YDW and the TWC could be the main reason for this northward drift of SSC. The long-term sinking of suspended sediment in clustered areas may have contributed considerably to the formation of the shallow coastal shelf above the 60 m isobath.     

Neural network nonlinear modeling for hydrogen production using anaerobic fermentation

The potential of utilizing artificial neural network (ANN) model approach for simulate and predict the hydrogen yield in batch model using Clostridium saccharoperbutylacetonicum N1-4 (ATCC 13564) was investigated. A unique architecture has been introduced in this research to mimic the inter-relationship between three input parameters initial substrate, initial medium pH and reaction temperature (37 °C, 6.0 ± 0.2, 10), respectively, to predict hydrogen yield. Sixty data records from the experiment have been utilized to develop the ANN model. The results showed that the proposed ANN model provided significant level of accuracy for prediction with maximum error (10 %). Furthermore, a comparative analysis with a traditional approach Box–Wilson design (BWD) has proved that the ANN model output significantly outperformed the BWD. ANN model overcomes the limitation of the BWD approach with respect to the number of records, which is merely considering limited length of stochastic pattern for hydrogen yield (15 records).