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1.
Ground vibration is one of the common environmental effects of blasting operation in mining industry, and it may cause damage to the nearby structures and the surrounding residents. So, precise estimation of blast-produced ground vibration is necessary to identify blast-safety area and also to minimize environmental effects. In this research, a hybrid of adaptive neuro-fuzzy inference system (ANFIS) optimized by particle swarm optimization (PSO) was proposed to predict blast-produced ground vibration in Pengerang granite quarry, Malaysia. For this goal, 81 blasting were investigated, and the values of peak particle velocity, distance from the blast-face and maximum charge per delay were precisely measured. To demonstrate the performance of the hybrid PSO–ANFIS, ANFIS, and United States Bureau of Mines empirical models were also developed. Comparison of the predictive models was demonstrated that the PSO–ANFIS model [with root-mean-square error (RMSE) 0.48 and coefficient of determination (R 2) of 0.984] performed better than the ANFIS with RMSE of?1.61 and R 2 of 0.965. The mentioned results prove the superiority of the newly developed PSO–ANFIS model in estimating blast-produced ground vibrations.  相似文献   

2.
Drought is accounted as one of the most natural hazards. Studying on drought is important for designing and managing of water resources systems. This research is carried out to evaluate the ability of Wavelet-ANN and adaptive neuro-fuzzy inference system (ANFIS) techniques for meteorological drought forecasting in southeastern part of East Azerbaijan province, Iran. The Wavelet-ANN and ANFIS models were first trained using the observed data recorded from 1952 to 1992 and then used to predict meteorological drought over the test period extending from 1992 to 2011. The performances of the different models were evaluated by comparing the corresponding values of root mean squared error coefficient of determination (R 2) and Nash–Sutcliffe model efficiency coefficient. In this study, more than 1,000 model structures including artificial neural network (ANN), adaptive neural-fuzzy inference system (ANFIS) and Wavelet-ANN models were tested in order to assess their ability to forecast the meteorological drought for one, two, and three time steps (6 months) ahead. It was demonstrated that wavelet transform can improve meteorological drought modeling. It was also shown that ANFIS models provided more accurate predictions than ANN models. This study confirmed that the optimum number of neurons in the hidden layer could not be always determined using specific formulas; hence, it should be determined using a trial-and-error method. Also, decomposition level in wavelet transform should be delineated according to the periodicity and seasonality of data series. The order of models with regard to their accuracy is as following: Wavelet-ANFIS, Wavelet-ANN, ANFIS, and ANN, respectively. To the best of our knowledge, no research has been published that explores coupling wavelet analysis with ANFIS for meteorological drought and no research has tested the efficiency of these models to forecast the meteorological drought in different time scales as of yet.  相似文献   

3.
Suspended sediment load prediction of river systems: GEP approach   总被引：1，自引：1，他引：0  
This study presents gene expression programming (GEP), an extension of genetic programming, as an alternative approach to modeling the suspended sediment load relationship for the three Malaysian rivers. In this study, adaptive neuro-fuzzy inference system (ANFIS), regression model, and GEP approaches were developed to predict suspended load in three Malaysian rivers: Muda River, Langat River, and Kurau River [ANFIS (R 2?=?0.93, root mean square error (RMSE)?=?3.19, and average error (AE)?=?1.12) and regression model (R 2?=?0.63, RMSE?=?13.96, and AE?=?12.69)]. Additionally, the explicit formulations of the developed GEP models are presented (R 2?=?0.88, RMSE?=?5.19, and AE?=?6.5). The performance of the GEP model was found to be acceptable compare to ANFIS and better than the conventional models.  相似文献   

4.
This paper describes the application of multi-layer perceptron (MLP), radial basis network and adaptive neuro-fuzzy inference system (ANFIS) models for computing dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD) levels in the Karoon River (Iran). Nine input water quality variables including EC, PH, Ca, Mg, Na, Turbidity, PO4, NO3 and NO2, which were measured in the river water, were employed for the models. The performance of these models was assessed by the coefficient of determination R 2, root mean square error and mean absolute error. The results showed that the computed values of DO, BOD and COD using both the artificial neural network and ANFIS models were in close agreement with their respective measured values in the river water. MLP was also better than other models in predicting water quality variables. Finally, the sensitive analysis was done to determine the relative importance and contribution of the input variables. The results showed that the phosphate was the most effective parameters on DO, BOD and COD.  相似文献   

5.

In this study, a database developed from existing literature about permeability of cracked rock was established. The performance of Support Vector Machine (SVM) combined with optimisation algorithms: Genetic Algorithm (GA) and Particle Swarm Optimisation Algorithm (PSO) in predicting the permeability of cracked rock masses (CRM) is evaluated. Also, the sensitivity analysis of the influence factors to the permeability of CRM is conducted. The results indicate that the hybrid GA–SVM and hybrid PSO–SVM models can accurately predict the permeability of CRM in terms of the statistical performance criteria: Coefficient of Determination R2, Regression Coefficient R and Mean Residual Error (MSE); Additionally, optimisation algorithms: PSO and GA can improve significantly the predictive performance of the SVM model. Based on the sensitivity analysis, crack angle is the most important factor to change the permeability of CRM, followed by confining pressure.

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6.
This paper evaluates the performance of three soft computing techniques, namely Gene-Expression Programming (GEP) (Zakaria et al 2010), Feed Forward Neural Networks (FFNN) (Ab Ghani et al 2011), and Adaptive Neuro-Fuzzy Inference System (ANFIS) in the prediction of total bed material load for three Malaysian rivers namely Kurau, Langat and Muda. The results of present study are very promising: FFNN (R 2 = 0.958, RMSE = 0.0698), ANFIS (R 2 = 0.648, RMSE = 6.654), and GEP (R 2 = 0.97, RMSE = 0.057), which support the use of these intelligent techniques in the prediction of sediment loads in tropical rivers.  相似文献   

7.
A reliable prediction of dispersion coefficient can provide valuable information for environmental scientists and river engineers as well. The main objective of this study is to apply intelligence techniques for predicting longitudinal dispersion coefficient in rivers. In this regard, artificial neural network (ANN) models were developed. Four different metaheuristic algorithms including genetic algorithm (GA), imperialist competitive algorithm (ICA), bee algorithm (BA) and cuckoo search (CS) algorithm were employed to train the ANN models. The results obtained through the optimization algorithms were compared with the Levenberg–Marquardt (LM) algorithm (conventional algorithm for training ANN). Overall, a relatively high correlation between measured and predicted values of dispersion coefficient was observed when the ANN models trained with the optimization algorithms. This study demonstrates that the metaheuristic algorithms can be successfully applied to make an improvement on the performance of the conventional ANN models. Also, the CS, ICA and BA algorithms remarkably outperform the GA and LM algorithms to train the ANN model. The results show superiority of the performance of the proposed model over the previous equations in terms of DR, R 2 and RMSE.  相似文献   

8.
The authors investigate the hydrological and geochemical characteristics of the Jamari (30430 km2) and Jiparana (60350 km2) river basins (Amazonia), during the period 1978–1984. A spectral analysis of Fourier is applied to time series of mean monthly river discharges, in order to assess the contribution (7 to 8%) of the surface runoff to the total river flow. The mean annual runoff coefficient calculated for the Jiparana river basin (36%), is higher than for the Jamari (32%), and this coefficient increases during the study period, only for the Jiparana. The total specific suspended sediment discharge calculated for both rivers shows the same value 13 t/km2/y, and the estimated suspended sediment concentration in the surface runoff is slightly superior for the Jiparana river (0.3 g/l) than for the Jamari one (0.2 g/l). The river suspended sediments are mainly composed of kaolinite, quartz and feldspar, but the Jiparana is more enriched in quartz. For both rivers, the dominant clay mineral is the kaolinite which is in agreement with the rock weathering type determined for both basins using the Tardy's weathering index: the monosiallitisation. The total chemical erosion rate calculated after correction for the atmospheric inputs (ions and CO2), is higher for the Jiparana (10.11 t/km2/y) than for the Jamari river basin (7.75 t/km2/y). These values are lower than the mechanical denudation rate calculated previously for both river basins.  相似文献   

9.
Soil saturated hydraulic conductivity (Ks) is considered as soil basic hydraulic property, and its precision estimation is a key element in modeling water flow and solute transport processes both in the saturated and vadose zones. Although some predictive methods (e.g., pedotransfer functions, PTFs) have been proposed to indirectly predict Ks, the accuracy of these methods still needs to be improved. In this study, some easily available soil properties (e.g., particle size distribution, organic carbon, calcium carbonate content, electrical conductivity, and soil bulk density) are employed as input variables to predict Ks using a fuzzy inference system (FIS) trained by two different optimization techniques: particle swarm optimization (PSO) and genetic algorithm (GA). To verify the derived FIS, 113 soil samples were taken, and their required physical properties were measured (113 sample points?×?7 factors?=?791 input data). The initial FIS is compared with two methods: FIS trained by PSO (PSO-FIS) and FIS trained by GA (GA-FIS). Based on experimental results, all three methods are compared according to some evaluation criteria including correlation coefficient (r), modeling efficiency (EF), coefficient of determination (CD), root mean square error (RMSE), and maximum error (ME) statistics. The results showed that the PSO-FIS model achieved a higher level of modeling efficiency and coefficient of determination (R2) in comparison with the initial FIS and the GA-FIS model. EF and R2 values obtained by the developed PSO-FIS model were 0.69 and 0.72, whereas they were 0.63 and 0.54 for the GA-FIS model. Moreover, the results of ME and RMSE indices showed that the PSO-FIS model can estimate soil saturated hydraulic conductivity more accurate than the GA-FIS model with ME?=?10.4 versus 11.5 and RMSE?=?5.2 versus 5.5 for PSO-FIS and GA-FIS, respectively.  相似文献   

10.
Characteristics of ungauged catchments can be studied from the hydrological model parameters of gauged catchments. In this research, discharge prediction was carried out in ungauged catchments using HEC-HMS in the central Omo-Gibe basin. Linear regression, spatial proximity, area ratio, and sub-basin mean were amalgamated for regionalization. The regional model parameters of the gauged catchment and physical characteristics of ungauged catchments were collated together to develop the equations to predict discharge from ungauged catchments. From the sensitivity analysis, crop coefficient (CC), storage coefficient (R), constant rate (CR), and time of concentration (TC) are found to be more sensitive than others. The model efficiency was evaluated using Nash–Sutcliffe Efficiency (NSE) which was greater than 0.75, varying between ?10% and +10% and the coefficient of determination (R2) was approximated to be 0.8 during the calibration and validation period. The model parameters in ungauged catchments were determined using the regional model (linear regression), sub-basin mean, area ratio, and spatial proximity methods, and the discharge was simulated using the HEC-HMS model. Linear regression was used in the prediction where p-value ≤ 0.1, determination coefficient (R2) = 0.91 for crop coefficient (CC) and 0.99 for maximum deficit (MD). Constant rate (CR), maximum storage (MS), initial storage (IS), storage coefficient (R), and time of concentration (TC) were obtained. The result is that an average of 30 m3/s and 15 m3/s as the maximum monthly simulated flow for ungauged sub-catchments, i.e. Denchiya and Mansa of the main river basin .  相似文献   

11.
This study addresses the effects of rock characteristics and blasting design parameters on blast-induced vibrations in the Kangal open-pit coal mine, the Tülü open-pit boron mine, the K?rka open-pit boron mine, and the TKI Çan coal mine fields. Distance (m, R) and maximum charge per delay (kg, W), stemming (m, SB), burden (m, B), and S-wave velocities (m/s, Vs) obtained from in situ field measurements have been chosen as input parameters for the adaptive neuro-fuzzy inference system (ANFIS)-based model in order to predict the peak particle velocity values. In the ANFIS model, 521 blasting data sets obtained from four fields have been used (r 2 = 0.57–0.81). The coefficient of ANFIS model is higher than those of the empirical equation (r 2 = 1). These results show that the ANFIS model to predict PPV values has a considerable advantage when compared with the other prediction models.  相似文献   

12.
Comparison of FFNN and ANFIS models for estimating groundwater level   总被引：3，自引：2，他引：1  
Prediction of water level is an important task for groundwater planning and management when the water balance consistently tends toward negative values. In Maheshwaram watershed situated in the Ranga Reddy District of Andhra Pradesh, groundwater is overexploited, and groundwater resources management requires complete understanding of the dynamic nature of groundwater flow. Yet, the dynamic nature of groundwater flow is continually changing in response to human and climatic stresses, and the groundwater system is too intricate, involving many nonlinear and uncertain factors. Artificial neural network (ANN) models are introduced into groundwater science as a powerful, flexible, statistical modeling technique to address complex pattern recognition problems. This study presents the comparison of two methods, i.e., feed-forward neural network (FFNN) trained with Levenberg–Marquardt (LM) algorithm compared with a fuzzy logic adaptive network-based fuzzy inference system (ANFIS) model for better accuracy of the estimation of the groundwater levels of the Maheshwaram watershed. The statistical indices used in the analysis were the root mean square error (RMSE), regression coefficient (R 2) and error variation (EV).The results show that FFNN-LM and ANFIS models provide better accuracy (RMSE = 4.45 and 4.94, respectively, R 2 is 93% for both models) for estimating groundwater levels well in advance for the above location.  相似文献   

13.
Flood forecasting in large rivers with data-driven models   总被引：1，自引：1，他引：0  
Results from the application of adaptive neuro-fuzzy inference system (ANFIS) to forecast water levels at 3 stations along the mainstream of the Lower Mekong River are reported in this paper. The study investigated the effects of including water levels from upstream stations and tributaries, and rainfall as inputs to ANFIS models developed for the 3 stations. When upstream water levels in the mainstream were used as input, improvements to forecasts were realized only when the water levels from 1 or at most 2 upstream stations were included. This is because when there are significant contributions of flow from the tributaries, the correlation between the water levels in the upstream stations and stations of interest decreases, limiting the effectiveness of including water levels from upstream stations as inputs. In addition, only improvements at short lead times were achieved. Including the water level from the tributaries did not significantly improve forecast results. This is attributed mainly to the fact that the flow contributions represented by the tributaries may not be significant enough, given that there could be large volume of flow discharging directly from the catchments which are ungauged, into the mainstream. The largest improvement for 1-day forecasts was obtained for Kratie station where lateral flow contribution was 17 %, the highest for the 3 stations considered. The inclusion of rainfall as input resulted in significant improvements to long-term forecasts. For Thakhek, where rainfall is most significant, the persistence index and coefficient of efficiency for 5-lead-day forecasts improved from 0.17 to 0.44 and 0.89 to 0.93, respectively, whereas the root mean square error decreased from 0.83 to 0.69 m.  相似文献   

14.
The present research was carried out by using artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), cokriging (CK) and ordinary kriging (OK) using the rainfall and streamflow data for suspended sediment load forecasting. For this reason, the time series of daily rainfall (mm), streamflow (m3/s), and suspended sediment load (tons/day) data were used from the Kojor forest watershed near the Caspian Sea between 28 October 2007 and 21 September 2010 (776 days). Root mean square error, efficiency coefficient, mean absolute error, and mean relative error statistics are used for evaluating the accuracy of the ANN, ANFIS, CK, and OK models. In the first part of the study, various combinations of current daily rainfall, streamflow and past daily rainfall, streamflow data are used as inputs to the neural network and neuro-fuzzy computing technique so as to estimate current suspended sediment. Also, the accuracy of the ANN and ANFIS models are compared together in suspended sediment load forecasting. Comparison results reveal that the ANFIS model provided better estimation than the ANN model. In the second part of the study, the ANN and ANFIS models are compared with OK and CK. The comparison results reveal that CK was a better estimation than the OK. The ANFIS and ANN models also provided better estimation than the OK and CK models.  相似文献   

15.
Bordbar  Mojgan  Neshat  Aminreza  Javadi  Saman  Pradhan  Biswajeet  Dixon  Barnali  Paryani  Sina 《Natural Hazards》2022,110(3):1799-1820

The main objective of this study is to integrate adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and artificial neural network (ANN) to design an integrated supervised committee machine artificial intelligence (SCMAI) model to spatially predict the groundwater vulnerability to seawater intrusion in Gharesoo-Gorgan Rood coastal aquifer placed in the northern part of Iran. Six hydrological GALDIT parameters (i.e., G groundwater occurrence, A aquifer hydraulic conductivity, L level of groundwater above sea level, D distance from the shore, I impact of the existing status of seawater intrusion in the region, and T thickness of the aquifer) were considered as inputs for each model. In the training step, the values of GALDIT’s vulnerability index were conditioned by using the values of TDS concentration in order to obtain the conditioned vulnerability index (CVI). The CVI was considered as the target for each model. After training the models, each model was tested using a separate TDS dataset. The results indicated that the ANN and ANFIS algorithms performed better than the SVM algorithm. The values of correlation were obtained as 88, 87, and 80% for ANN, ANFIS, and SVM models, respectively. In the testing step of the SCMAI model, the values of RMSE, R2, and r were obtained as 6.4, 0.95, and 97%, respectively. Overall, SCMAI model outperformed other models to spatially predicting vulnerable zones. The result of the SCMAI model confirmed that the western zones along the shoreline had the highest vulnerability to seawater intrusion; therefore, it seems critical to consider emergency protection plans for study area.

Graphic abstract
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16.
The waters of the Seine river estuary, located in a highly anthropogenicized area in the northern part of France, are of poor microbiological quality; the concentrations of faecal bacteria usually exceed the European Union bathing and recreational water directives. The aim of the present study was to identify the main sources of the faecal pollution of the Seine estuary in order to help define priorities for management and sanitation efforts. Budgets of faecal coliform (FC) inputs to the estuary were established for various hydrological conditions. Main sources of FC were the outfalls of the treated effluents of the wastewater treatment plants (WWTPs) located along the estuary, the faecal bacteria brought in through the tributaries of the Seine estuary, and the faecal bacteria transported by the Seine river flow at the estuary entrance at Poses dam. In order to quantify these inputs, FC were enumerated during sampling campaigns conducted for various hydrological conditions in the Seine at the entrance of the estuary, in the tributaries close to their confluence with the estuary, and in the effluents of some WWTPs located along the estuary. The importance of the flux of FC transported by the Seine river flow at the estuary entrance at Poses dam decreased from 92% of the total FC input when the flow rate was high (717 m3 s−1) to 5% when flow rate was low (143 m3 s−1). The release of the domestic wastewaters of the large city of Paris located 120 km upstream from the entrance of the estuary was mainly responsible for this microbiological pollution. At low flow rates, the tributaries represent the most important source of FC (64–76% for flow rates of the Seine at Poses at approximately 150 m3 s−1), mainly from the Robec and Eure rivers. The treated wastewater of the WWTPs located along the estuary was the second source of FC for low flow conditions (19–30%); it was less important for high to intermediate flow rate conditions.  相似文献   

17.
A new constructed wetland was built to purify one polluted river in Taiwan, and this study was conducted to evaluate the treatment efficiency of the wetland. Due to the very limitation of available budget, several water quality items, which were stipulated by Taiwan’s Environmental Protection Administration for rivers, in the influent and effluent of wetland were analyzed and evaluated. These items included water temperature, pH, DO, BOD5, TSS, and NH4 +-N. The results showed that the average removal rates of total (unfiltered) BOD5, TSS and NH4 +-N were 36.9 %, 71.8 % and 47.1%, respectively. With the HRT more than 3.4 days, the wetland could treat the polluted river water effectively. Longer HRT in this wetland appeared no obvious improvement on the removal rate of TSS or NH4 +-N. However, BOD removal rate increased while the HRT (Hydraulic Retention Time) increased to about 5 days. In this wetland, the calculated mean first-order reaction rate constant (kT) for BOD5 was 0.15/day with a standard deviation of 0.13/day and for NH4 +-N was 0.24/ day with a standard deviation of 0.18/day. It is also concluded that there is a linear proportional relationship between BOD concentrations in the effluent of wetland and its influent mass loading rates, with the coefficient of determination (R2) of 0.6511. Similar result was seen for NH4 +-N as well, with the coefficient of determination (R2) of 0.5965. TSS removal rate was found to be linearly proportional to its influent mass loading rate, with the coefficient of determination (R2) of 0.4875.  相似文献   

18.
The Narmada River flows through the Deccan volcanics and transports water and sediments to the adjacent Arabian Sea. In a first-ever attempt, spatial and temporal (annual, seasonal, monthly and daily) variations in water discharge and sediment loads of Narmada River and its tributaries and the probable causes for these variations are discussed. The study has been carried out with data from twenty-two years of daily water discharge at nineteen locations and sediment concentrations data at fourteen locations in the entire Narmada River Basin. Water flow in the river is a major factor influencing sediment loads in the river. The monsoon season, which accounts for 85 to 95% of total annual rainfall in the basin, is the main source of water flow in the river. Almost 85 to 98% of annual sediment loads in the river are transported during the monsoon season (June to November). The average annual sediment flux to the Arabian Sea at Garudeshwar (farthest downstream location) is 34.29×106 t year−1 with a water discharge of 23.57 km3 year−1. These numbers are the latest and revised estimates for Narmada River. Water flow in the river is influenced by rainfall, catchment area and groundwater inputs, whereas rainfall intensity, geology/soil characteristics of the catchment area and presence of reservoirs/dams play a major role in sediment discharge. The largest dam in the basin, namely Sardar Sarovar Dam, traps almost 60–80% of sediments carried by the river before it reaches the Arabian Sea.  相似文献   

19.
This paper investigates the prediction of future earthquakes that would occur with magnitude 5.5 or greater using adaptive neuro-fuzzy inference system (ANFIS). For this purpose, the earthquake data between 1950 and 2013 that had been recorded in the region with 2°E longitude and 4°N latitude in Iran has been used. Thereupon, three algorithms including grid partition (GP), subtractive clustering (SC) and fuzzy C-means (FCM) were used to develop models with the structure of ANFIS. Since the earthquake data for the specified region had been reported on different magnitude scales, suitable relationships were determined to convert the magnitude scales into moment magnitude and all records uniformed based on the relationships. The uniform data were used to calculate seismicity indicators, and ANFIS was developed based on considered algorithms. The results showed that ANFIS-FCM with a high accuracy was able to predict earthquake magnitude.  相似文献   

20.
Various anthropogenic radionuclides and210Pb were analyzed in a 4.3-m-long core, sampled near the Rhône River mouth in March 1991, to evaluate the extent of industrial releases that accumulate in this area. The whole core was significantly marked by radionuclide inputs from the nuclear facilities located along the river (137Cs,134Cs,60Co). Irregular profiles in natural and artificial radionuclides should be related to variations in their respective inputs from the Rhône River to the Mediterranean Sea. Minimum concentrations were found during high flow periods. Using both the137Cs/134Cs profile in the core and the range of this ratio in Rhône waters, mean apparent accumulation rates were estimated to range between 37 cm yr?1 and 48 cm yr?1. This core would then represent a sedimentary record over a 7–10 year period. However, the presence of a signal from the Chernobyl accident, which occurred on April 26, 1986, was not clearly observed in the core. Inventories of both artificial and natural radionuclides were greater than expected from atmospheric inputs. The increased sedimentation occurring in close vicinity to the mouth of the Rhône River is thus responsible for trapping of elements transported by the river to the Mediterranean Sea. In this area, inventories of artificial radionuclides are well in excess of aerial deposition from Chernobyl and atmospheric weapons tests and are linked primarily to industrial releases.  相似文献   

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