scholarly journals Using Artificial Neural Network to Model Water Discharge and Chemistry in a River Impacted by Acid Mine Drainage

2021 ◽  
Vol 9 (2) ◽  
pp. 63-79
Author(s):  
Toluwaleke Ajayi ◽  
Dina L.Lopez ◽  
Abiodun E.Ayo-Bali
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acid Mine Drainage ◽  
Mine Drainage ◽  
Water Discharge ◽  
Acid Mine ◽  
Artificial Neural ◽  
Model Water

scholarly journals Optimising Brewery-Wastewater-Supported Acid Mine Drainage Treatment vis-à-vis Response Surface Methodology and Artificial Neural Network

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1485
Author(s):  
Enoch A. Akinpelu ◽  
Seteno K. O. Ntwampe ◽  
Abiola E. Taiwo ◽  
Felix Nchu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Acid Mine Drainage ◽  
Response Surface ◽  
Mine Drainage ◽  
Sulphate Reduction ◽  
Optimum Conditions ◽  
Acid Mine ◽  
Artificial Neural

This study investigated the use of brewing wastewater (BW) as the primary carbon source in the Postgate medium for the optimisation of sulphate reduction in acid mine drainage (AMD). The results showed that the sulphate-reducing bacteria (SRB) consortium was able to utilise BW for sulphate reduction. The response surface methodology (RSM)/Box–Behnken design optimum conditions found for sulphate reduction were a pH of 6.99, COD/SO42− of 2.87, and BW concentration of 200.24 mg/L with predicted sulphate reduction of 91.58%. Furthermore, by using an artificial neural network (ANN), a multilayer full feedforward (MFFF) connection with an incremental backpropagation network and hyperbolic tangent as the transfer function gave the best predictive model for sulphate reduction. The ANN optimum conditions were a pH of 6.99, COD/SO42− of 0.50, and BW concentration of 200.31 mg/L with predicted sulphate reduction of 89.56%. The coefficient of determination (R2) and absolute average deviation (AAD) were estimated as 0.97 and 0.046, respectively, for RSM and 0.99 and 0.011, respectively, for ANN. Consequently, ANN was a better predictor than RSM. This study revealed that the exclusive use of BW without supplementation with refined carbon sources in the Postgate medium is feasible and could ensure the economic sustainability of biological sulphate reduction in the South African environment, or in any semi-arid country with significant brewing activity and AMD challenges.

scholarly journals Artificial Neural Network Model for Estimation of Suspended Sediment Load in Krishna River Basin, India

2020 ◽  
Vol 9 (3) ◽  
pp. 3701-3705
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Suspended Sediment ◽  
River Basin ◽  
Flood Control ◽  
Sediment Load ◽  
Water Discharge ◽  
Suspended Sediment Load ◽  
Artificial Neural

The correct assessment of amount of sediment during design, management and operation of water resources projects is very important. Efficiency of dam has been reduced due to sedimentation which is built for flood control, irrigation, power generation etc. There are traditional methods for the estimation of sediment are available but these cannot provide the accurate results because of involvement of very complex variables and processes. One of the best suitable artificial intelligence technique for modeling this phenomenon is artificial neural network (ANN). In the current study ANN techniques used for simulation monthly suspended sediment load at Vijayawada gauging station in Krishna river basin, Andhra Pradesh, India. Trial & error method were used during the optimization of parameters that are involved in this model. Estimation of suspended sediment load (SSL) is done using water discharge and water level data as inputs. The water discharge, water level and sediment load is collected from January 1966 to December 2005. This approach is used for modelled the SSL. By considering the results, ANN has the satisfactory performance and more accurate results in the simulation of monthly SSL for the study location.

scholarly journals Monthly Suspended Sediment Load Estimation using Artificial Neural Network and Traditional Models in Krishna River Basin, India

2020 ◽  
Vol 9 (3) ◽  
pp. 613-618
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Suspended Sediment ◽  
River Basin ◽  
Sediment Yield ◽  
Sediment Load ◽  
Water Discharge ◽  
Tectonic Activity ◽  
Precipitation Seasonality ◽  
Artificial Neural

The measurement of sediment yield is essential for getting the information of the mass balance between sea and land. It is difficult to directly measure the suspended sediment because it takes more time and money. One of the most common pollutants in the aquatic environment is suspended sediments. The sediment loads in rivers are controlled by variables like canal slope, basin volume, precipitation seasonality and tectonic activity. Water discharge and water level are the major controlling factor for estimate the sediment load in the Krishna River. Artificial neural network (ANN) is used for sediment yield modeling in the Krishna River basin, India. The comparative results show that the ANN is the easiest model for the suspended sediment yield estimates and provides a satisfactory prediction for very high, medium and low values. It is also noted that the Multiple Linear Regressions (MLR) model predicted an many number of negative sediment outputs at lower values. This is entirely unreality because the suspended sediment result can not be negative in nature. The ANN is provided better results than traditional models. The proposed ANN model will be helpful where the sediment measures are not available.

scholarly journals Flood Risk Prediction for a Hydropower System using Artificial Neural Network

2019 ◽  
Vol 8 (4) ◽  
pp. 6177-6181
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Water Flow ◽  
Performance Metrics ◽  
Water Discharge ◽  
Mean Square ◽  
Ann Model ◽  
Elman Neural Network ◽  
Artificial Neural

Hydropower scheme would experience issue relating to high flooding especially at low lying area due to extreme raining season. To mitigate the potential risk of flooding and improve the hydroelectric regulation, a flow prediction is needed to estimate the discharge of water flow at hydroelectric reservoirs. Artificial Neural Network (ANN) model were used in this research to forecast the water discharge of hydroelectric station. The discharge flow predictions were made based on fore bay elevation, inflow and the discharge of water flow. Elman Neural Network architecture was selected as ANN method and its performance was evaluated by considering the number of hidden nodes and training methods. ANN model performance were assessed using performance metrics such as Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE) and Sum Square Error (SSE). The result indicate that ANN model showed the best applicability for discharge prediction with small performance metric.

scholarly journals Prediction of water discharge and sediment in teak forested area using artificial neural network model

2017 ◽  
Vol 6 (1) ◽  
pp. 73
Author(s):  
Nining Wahyuningrum
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Discharge ◽  
Ann Modeling ◽  
Highly Nonlinear ◽  
Condition Modeling ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Relationship Of ◽  
The Relationship

Information on the relationship of rainfall with discharge and sediment are required in watershed management.This relationship is known to be highly nonlinear and complex. Although discharge and sediment has been monitored continuously, but sometimes the information is not or less complete. In this condition, modeling is indispensable.The research objective is to create a model to predict the monthly direct runoff and sediment using Artificial Neural Network (ANN).The model was tested using rainfall data at t-3 and t-4 as input, and discharge and sediment at t+3 and t+4 as output. The data used is the data from 2001 to 2014. The results showed that of some models tested there are two models for the prediction of discharge and two models for sediment.The model was chosen because it has the smallest MSE, the largest R2 and satisfying K (0.5 to 0.65).Thus, these models can be used to predict discharge andsediment for a period of t+3 and t+4. Prediction of discharge of t+3 and t+4 may use Q t+3 = 0,64 Q t-3 + 0,05 and Q t+4 = 0,65 Q t-4 + 0,074 res pectively, while for predicting sediment of t+3 and t+4 may use equations QS t+3 = 0,45 QS t-3 + 0,052 and QS t+4 = 0,45 QS t-4 + 0,052. This ANN modeling can be applied to predict the flow and sediment in other locations with an architecture adapted to the conditions of available data.

scholarly journals Application of neural network techniques to predict the heavy metals in acid mine drainage from South African mines

2021 ◽  
Author(s):  
John Kabuba ◽  
Andani Valentia Maliehe
Keyword(s):  
Neural Network ◽  
Heavy Metals ◽  
Transfer Function ◽  
Acid Mine Drainage ◽  
South African ◽  
Mine Drainage ◽  
Mean Squared Error ◽  
Coefficient Of Determination ◽  
Output Layer ◽  
Acid Mine

Abstract Acid Mine Drainage (AMD) is the formation and movement of highly acid water rich in heavy metals. Prediction of heavy metals in the AMD is important in developing any appropriate remediation strategy. This paper attempts to predict heavy metals in the AMD (Zn, Fe, Mn, Si and Ni) from South African mines using Neural Network (NN) techniques. The Backpropagation (BP) neural network model has three layers with the input layer (pH, SO42− and TDS) and output layer (Cu, Fe, Mn and Zn). After BP training, the NN techniques were able to predict heavy metals in AMD with a tangent sigmoid transfer function (tansig) at hidden layer with 5 neurons and linear transfer function (purelin) at output layer. The Levenberg-Marquardt back-propagation (trainlm) algorithm was found as the best of 10 BP algorithms with mean-squared error (MSE) value of 0.00041 and coefficient of determination (R) for all (training, validation and test) value of 0.99984. The results indicate that NN can be considered as an easy and cost-effective technique to predict heavy metals in the AMD.

scholarly journals Estimation of Suspended Sediment Yield using Artificial Neural Network Model

2020 ◽  
Vol 9 (3) ◽  
pp. 3249-3253
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Suspended Sediment ◽  
River Basin ◽  
Sediment Yield ◽  
Sediment Load ◽  
Water Discharge ◽  
Suspended Sediment Yield ◽  
Godavari River ◽  
Artificial Neural

Estimation of the suspended sediment yield is important for the planning and management of water resources and protection of the environment. Environmental change influences sediment generation and the transport and the consequent sediment load in river. In this study, artificial intelligence-based technique like the artificial neural network (ANN) is proposed for sediment yield estimation in the Godavari river basin, India. The ANN is one of the appropriate data-mining techniques that help model the complex phenomenon of sedimentation. In this study the prediction of the suspended sediment load is done using the ANN techniques by using the water discharge and water level data from 1970 to 2015 as inputs at Polavaram gauge station in Godavari river basin, India. The results demonstrate that the ANN shows a satisfactory performance based on the root mean squared error (RMSE), mean square error (MSE), mean absolute error (MAE) and correlation coefficient (r) error statistics and provided more accurate results.

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