Water discharge (WD) is a crucial element of river hydrodynamics. The assessment of WD in dam design and river engineering studies is crucial. Proper WD from a barrage or dam must be regulated to prevent downstream floods during rainy season. WD encompasses numerous complex nonlinear processes and factors that traditional approaches are not able to address such complexities. To address these chllanges, hybrid multi-objective optimisation Genetic algorithm(GA) based artificial neural network(ANN) (MOO-GA-ANN) with automated parameter tuning model is proposed to optimizes the variance-bias trade-off for WD estimation in Mahanadi river (MR), India. These approaches optimises the two conflicting responses bias and variance with all artificial neural network(ANN) model parameters optimisation simulateneously to enhance robustness and generalisation capability of the model. The MOO-GA-ANN is developed using hydro-climatic data (Tempearature(T), Rainfall(R), water level(WL), and Suspended sediment yield(SSY)) as input parameters to estimate WD at the most downstream gauge location(Tikarapara) in the MR. Multiple linear regression (MLR), standalone ANN and single-objective GA-based ANN (GA-ANN) models are used for comparison from the MOO-GA-ANN on the basis of the statistical error metrics performances evaluation. Results indicates that the hybridised MOO-GA-ANN model has produced the most accurate and efficient results compared to other models for estimating WD in MR. The proposed model can be potentially used to estimate WD at both ungauged or gauged sites in the absence of measured WD because of its high performance and simplicity of use.

Water discharge (WD) is essential component of river hydrodynamics. It is crucial to predict WD for constructing reservoirs and dams, operations, management and river engineering studies. WD includes various complex nonlinear processes which depends on many variables. The WD prediction is complicated using traditional methods because these are unable to handle the complex nonlinear processes of it. WD prediction using novel artificial intelligence (AI) techniques is highly encouraged for its effectiveness in flood forecasting. A hybrid AI approaches is useful for WD prediction in Mahanadi River (MR), India. Support vector machine (SVM) is integrated with Genetic algorithm (GA) (GA-SVM) for optimization of all associated SVM parameters. Temperature(T), rainfall (R), water level (WL) and sediment yield (SY) are used as inputs parameters for prediction of WD. The standard statistical measures are used for the performance evaluation of GA-SVM model. Hybrid GA-SVM with automated parameter tuning model is developed for prediction of WD at Tikarapara location. It is outermost downstream station in the MR using hydro-climatical parameters. The developed GA-SVM model is compared to SVM, and multiple linear regression (MLR) to evaluate the ability of the models to predict the WD. The proposed GA-SVM model is provided satisfactory and better results for estimation of WD in the MR among all comparative models. This modelling approaches can be applied for the prediction of WD at gauge or ungauged places in MR due to its easiness in implement and satisfactory results.

Predicting rainfall is one of the most difficult and important aspects of the hydrologic cycle. This is mostly because it exhibits dynamics that are variable across a great range of time and space scales. Flash flooding, which is the result of heavy rain, is a life-threatening effect. Forecasting of rainfall and flood warning system for regular catchments is a complex and challenging task. Rainfall forecasting is an important component in the water resources studies program, including projects such as river training works and flood warning systems design. The backpropagation algorithm configuration for a multilayered artificial neural network is easier to train compared to other methods and that is why it is used broadly. Recent artificial intelligence and specifically in conevtional-based techniques for finding results for complex processes like rainfall patterns, which are highly unpredictable, irregular, and influenced by many factors, can be difficult to analyze presenting new avenues for modeling rainfall forecasting. One such technique is artificial neural networks (ANNs), which are capable of performing a nonlinear mapping between inputs and outputs. Current studies regarding ANN indicate that the two biggest challenges which are selecting the right network design and making the training process efficient. This study will implement a hybrid genetic algorithm combined with artificial neural network (GA-ANN) model for short-term rainfall prediction using rainfall data obtained from recording rain gauges installed at various locations of one of the biggest rivers of India- Mahanadi catchment area in Orissa. These study results indicated that when the ANN network was properly structured and used coupling with GA, the results were generalized and satisfactory.

The authors would like to make the following corrections about the published paper [...]

COVID-19 prediction models are highly welcome and necessary for authorities to make informed decisions. Traditional models, which were used in the past, were unable to reliably estimate death rates due to procedural flaws. The genetic algorithm in association with an artificial neural network (GA-ANN) is one of the suitable blended AI strategies that can foretell more correctly by resolving this difficult COVID-19 phenomena. The genetic algorithm is used to simultaneously optimise all of the ANN parameters. In this work, GA-ANN and ANN models were performed by applying historical daily data from sick, recovered, and dead people in India. The performance of the designed hybrid GA-ANN model is validated by comparing it to the standard ANN and MLR approach. It was determined that the GA-ANN model outperformed the ANN model. When compared to previous examined models for predicting mortality rates in India, the hypothesized hybrid GA-ANN model is the most competent. This hybrid AI (GA-ANN) model is suggested for the prediction due to reasonably better performance and ease of implementation.

The journal and authors retract the article entitled “An Evolutionary Technique for Building Neural Network Models for Predicting Metal Prices” [...]

In this research, a neural network (NN) model for metal price forecasting based on an evolutionary approach is proposed. Both the neural network model’s network parameters and network architecture are selected automatically. The time series metal price data set is used to construct a novel fitness function that takes into account both error minimizations and the reproduction of the auto-correlation function. Calculating the average entropy values allowed the selection of the input parameter count for the neural network model. Gold price forecasting was performed using the proposed methodology. The optimal hidden node number, learning rate, and momentum are 9, 0.026, and 0.76, respectively, according to the evolutionary-based NN model. The proposed strategy is shown to reduce estimation error while also reproducing the auto-correlation function of the time series data set by the validation results with gold price data. The performance of the proposed method is better than other current methods, according to a comparison study.

Traditional optimization of open pit mine design is a crucial component of mining endeavors and is influenced by many variables. The critical factor in optimization is the geological uncertainty, which relates to the ore grade. To deal with uncertainties related to the block economic values of mining blocks and the general problem of mine design optimization, under unknown conditions, the best ultimate pit limits and pushback designs are produced by a minimum cut algorithm. The push–relabel minimal cut algorithm provides a framework for computationally efficient representation and processing of the economic values of mining blocks under multiple scenarios. A sequential Gaussian simulation-based smoothing spline technique was created. To produce pushbacks, an efficient parameterized minimum cut algorithm is suggested. An analysis of Indian iron ore mining was performed. The developed mine scheduling algorithm was compared with the conventional algorithm, and the results show that when uncertainty is considered, the cumulative metal production is higher and there is an additional increase of about 5% in net present value. The results of this work help the mining industry to plan mines in such a way that can generate maximum profit from the deposits.

Rivers are the agents on earth and act as the main pathways for transporting the continental weathered materials into the sea. The estimation of suspended sediment yield (SSY) is important in the design, planning and management of water resources. The SSY depends on many factors and their interrelationships, which are very nonlinear and complex. The traditional approaches are unable to solve these complex nonlear processes of SSY. Thus, the development of a reliable and accurate model for estimating the SSY is essential. The goal of this research was to develop a single hybrid artificial intelligence model, which is a hybridization of the artificial neural network (ANN) and genetic algorithm (GA) (ANN-GA) for the estimation of SSY in the Mahanadi River (MR), India, by combining data from 11-gauge stations into a single hybrid generalized model and applying it to every gauging station for estimating the SSY. All parameters of the ANN model were optimized automatically and simultaneously using GA to estimate the SSY. The proposed model was developed considering the temporal monthly hydro-climatic data, such as temperature (T), rainfall (RF), water discharge (Q) and SSY and spatial data, including the rock type (RT), catchment area (CA) and relief (R), of all 11 gauging stations in the MR. The performances of the conventional sediment rating curve (SRC), ANN and multiple linear regression (MLR) were compared with the hybrid ANN-GA model. It was noticed that the ANN-GA model provided with greatest coefficient of correlation (0.8710) and lowest root mean square error (0.0088) values among all comparative SRC, ANN and MLR. Thus, the proposed ANN-GA is most appropriate model compared to other examined models for estimating SSY in the MR Basin, India, particularly at the Tikarapara measuring station. If no measures of SSY are available in the MR, then the modelling approach could be used to estimate SSY at ungauged or gauge stations in the MR Basin.

Rivers play a major role within ecosystems and society, including for domestic, industrial, and agricultural uses, and in power generation. Forecasting of suspended sediment yield (SSY) is critical for design, management, planning, and disaster prevention in river basin systems. It is difficult to forecast the SSY using conventional methods because these approaches cannot handle complicated non-stationarity and non-linearity. Artificial intelligence techniques have gained popularity in water resources due to handling complex problems of SSY. In this study, a fully automated generalized single hybrid intelligent artificial neural network (ANN)-based genetic algorithm (GA) forecasting model was developed using water discharge, temperature, rainfall, SSY, rock type, relief, and catchment area data of eleven gauging stations for forecasting the SSY. It is applied at individual gauging stations for SSY forecasting in the Mahanadi River which is one of India's largest peninsular rivers. All parameters of the ANN are optimized automatically and simultaneously using the GA. The multi-objective algorithm was applied to optimize the two conflicting objective functions (error variance and bias). The mean square error objective function was considered for the single-objective optimization model. Single and multi-objective GA-based ANN, autoregressive and multivariate autoregressive models were compared to each other. It was found that the single-objective GA-based ANN model provided the best accuracy among all comparative models, and it is the most suitable substitute for forecasting SSY. If the measurement of SSY is unavailable, then single-objective GA-based ANN modeling approaches can be recommended for forecasting SSY due to comparatively superior performance and simplicity of implementation.

The scheduling of open-pit mine production is a large-scale, mixed-integer linear programming problem that is computationally expensive. The purpose of this study is to create a computationally efficient algorithm for solving open-pit production scheduling problems with uncertain geological parameters. To demonstrate the effectiveness of the proposed research, a case study of an Indian iron ore mine is presented. Multiple realizations of the resource models were developed and integrated within the stochastic production scheduling framework to capture uncertainty and incorporate it into the mine plan. In this case study, two hybrid methods were developed to evaluate their performance. Model 1 is a combined branch and cut with the longest path, whereas Model 2 is a sequential parametric maximum flow and branch and cut. The results show that both methods produce similar materials, ore, metal, and risk profiles; however, Model 2 generates slightly more (4 percent) discounted cash flow from this study mine than Model 1. The results also show that Model 2's computational time is 46.64 percent less than that of Model 1.

Rivers are dynamic geological agents on the earth which transport the weathered materials of the continent to the sea. Estimation of suspended sediment yield (SSY) is essential for management, planning, and designing in any river basin system. Estimation of SSY is critical due to its complex nonlinear processes, which are not captured by conventional regression methods. Rainfall, temperature, water discharge, SSY, rock type, relief, and catchment area data of 11 gauging stations were utilized to develop robust artificial intelligence (AI), similar to an artificial-neural-network (ANN)-based model for SSY prediction. The developed highly generalized global single ANN model using a large amount of data was applied at individual gauging stations for SSY prediction in the Mahanadi River basin, which is one of India’s largest peninsular rivers. It appeared that the proposed ANN model had the lowest root-mean-squared error (0.0089) and mean absolute error (0.0029) along with the highest coefficient of correlation (0.867) values among all comparative models (sediment rating curve and multiple linear regression). The ANN provided the best accuracy at Tikarapara among all stations. The ANN model was the most suitable substitute over other comparative models for SSY prediction. It was also noticed that the developed ANN model using the combined data of eleven stations performed better at Tikarapara than the other ANN which was developed using data from Tikarapara only. These approaches are suggested for SSY prediction in river basin systems due to their ease of implementation and better performance.

The standard optimization of open-pit mine design and production scheduling, which is impacted by a variety of factors, is an essential part of mining activities. The metal uncertainty, which is connected to supply uncertainty, is a crucial component in optimization. To address uncertainties regarding the economic value of mining blocks and the general problem of mine design optimization, a minimum-cut network flow algorithm is employed to give the optimal ultimate pit limits and pushback designs under uncertainty. A structure that is computationally effective and can manage the joint presentation and treatment of the economic values of mining blocks under various circumstances is created by the push re-label minimum-cut technique. In this study, the algorithm is put to the test using a copper deposit and shows similarities to other stochastic optimizers for mine planning that have already been created. Higher possibilities of reaching predicted production targets are created by the algorithm’s earlier selection of more certain blocks with blocks of high value. Results show that, in comparison to a conventional approach using the same algorithm, the cumulative metal output is larger when the uncertainty in the metal content is taken into consideration. There is also an additional 10% gain in net present value.

This study involves a working limestone mine that supplies limestone to the cement factory. The two main goals of this paper are to (a) determine how long an operating mine can continue to provide the cement plant with the quality and quantity of materials it needs, and (b) explore the viability of combining some limestone from a nearby mine with the study mine limestone to meet the cement plant's quality and quantity goals. These objectives are accomplished by figuring out the maximum net profit for the ultimate pit limit and production sequencing of the mining blocks. The issues were resolved using a branch-and-cut based sequential integer and mixed integer programming problem. The study mine can exclusively feed the cement plant for up to 15 years, according to the data. However, it was also noted that the addition of the limestone from the neighboring mine substantially increased the mine's life (85 years). The findings also showed that, when compared with the production planning formulation that the company is now using, the proposed approach creates 10% more profit. The suggested method also aids in determining the desired desirable quality of the limestone that will be transported from the nearby mine throughout each production stage.

Suspended sediment yield (SSY) prediction plays a crucial role in the planning of water resource management and design. Accurate sediment prediction using conventional models is very difficult due to many complex processes. We developed a fully automatic highly generalized accurate and robust artificial intelligence models for SSY prediction in Godavari River Basin, India. The genetic algorithm (GA), hybridized with an artificial neural network (ANN) (GA-ANN), is a suitable artificial intelligence model for SSY prediction. The GA is used to concurrently optimize all ANN's parameters. The GA-ANN was developed using daily water discharge, with water level as the input data to estimate the daily SSY at Polavaram, which is the farthest gauging station in the downstream of the Godavari River Basin. The performances of the GA-ANN model were evaluated by comparing with ANN, sediment rating curve (SRC) and multiple linear regression (MLR) models. It is observed that the GA-ANN contains the highest correlation coefficient (0.927) and lowest root mean square error (0.053) along with lowest biased (0.020) values among all the comparative models. The GA-ANN model is the most suitable substitute over traditional models for SSY prediction. The hybrid GA-ANN can be recommended for estimating the SSY due to comparatively superior performance and simplicity of applications.

This paper explores the soft-computing approaches for estimating suspended sediment yield (SSY). In the management of water resources, the SSY estimation is an important issue. The SSY estimation is essential for getting the information about mass balancing between the land and ocean.  The traditional methods for measuring the SSY require larger magnitudes of time and significant financial investments. Also, the SSY depends on numerous variables and their internal relationship which are extremely nonlinear and complex in nature. Thus, traditional methods are not capable to handle the complex nonlinear sedimentation behaviors and unable to accurate estimation of SSY. The multilayer perceptron (MLP) artificial neural network (ANN)-based genetic algorithm (GA) model is used for SSY prediction which resolves complex sedimentations problems. In this proposed model, the GA optimized all ANN’s model parameters simultaneously. The input functional parameters that impact the SSY in the Krishna River are water discharge and water level. This paper presents artificial intelligence-based sediment yield estimation algorithms at Waddepally gauge station in Krishna River, India. The GA is used for the optimization of the performance of ANN in accurately estimating the SSY. The hybrid GA-based ANN (GA-ANN) has produced most accurate and efficient results for estimation of SSY in Krishna River.

Open pit mine production scheduling is a computationally expensive large-scale mixed-integer linear programming problem. This research develops a computationally efficient algorithm to solve open pit production scheduling problems under uncertain geological parameters. The proposed solution approach for production scheduling is a two-stage process. The stochastic production scheduling problem is iteratively solved in the first stage after relaxing resource constraints using a parametric graph closure algorithm. Finally, the branch-and-cut algorithm is applied to respect the resource constraints, which might be violated during the first stage of the algorithm. Six small-scale production scheduling problems from iron and copper mines were used to validate the proposed stochastic production scheduling model. The results demonstrated that the proposed method could significantly improve the computational time with a reasonable optimality gap (the maximum gap is 4%). In addition, the proposed stochastic method is tested using industrial-scale copper data and compared with its deterministic model. The results show that the net present value for the stochastic model improved by 6% compared to the deterministic model.

This paper is based on segmentation and detection of the color barrels. The main goal is to identify the barrels, there may be one or more barrels in the test and the training set in the given RGB images after managing segmentation color. Besides, Gaussian Mixture Model, Bayesian Model, linear regression method and other techniques are used to obtain more precise results of training images as well as prediction. We identify different color barrels such as red, blue, green and the barrel mean, variance and distance is approximately estimated by importing functions in this class by using linear regression model. GMM that is modeled as pixels and color classes, for the categorization of pixels by the class of color, and then we can differentiate the area of pixels of the barrel from non-barrel. The distance is estimated by two purposes interior One to detect the barrel and other to find the bounding box information counting four vertexes, height and width of the barrel.

Open pit mine production scheduling assigns mining blocks in different production periods for maximising profits after satisfying geotechnical and operational constraints. In this paper, two Open pit mine production scheduling models were applied in an African copper deposit. The first model is a traditional model with more tight resource constraints; the second model is a more robust model where resource constraints are relaxed by penalizing the objective function. Both the models were solved using two step algorithms: (a) year wise production scheduling using a sequential branch-and-cut algorithm; and (b) an iterative longest path algorithm to improve the solution generated from branch-and-cut. Results demonstrated that due to the tight constraints in Model 1, the optimizer was unable to generate a feasible solution after the first period, therefore the lower limit metal production constraint was eliminated to generate a feasible solution; however, Model 2 was able to generate a feasible solution for all periods. Results show that both the models generated nearly the same amount of ore, waste, metal content, and mine life. Model 2 generates relatively more net present value as compared to Model 1, whereas, the computational time required for solving the scheduling problem is relatively less for Model 1 than for Model 2.