РОЗРОБКА ІНФОРМАЦІЙНОЇ ТЕХНОЛОГІЇ КОРОТКОСТРОКОВОГО ПРОГНОЗУВАННЯ НЕЧІТКИХ ЧАСОВИХ РЯДІВ

Создана информационная технология для прогнозирования нечетких временных рядов на базе нечеткой логики с использованием генетического алгоритма. Рассмотренный подход лежит в основе разработанного в среде Eclipse программного продукта Fuzzy_Forecassting на языке программирования Java.It was created information technology to make forecast for fuzzy time series based on fuzzy logic and genetic algorithm. This approach was used in the developed software product Fuzzy_Forecassting which was created in Eclipse on the programming language Java.Створено інформаційну технологію для побудови прогнозів нечітких часових рядів на базі нечіткої логіки з використанням генетичного алгоритму. Розглянутий підхід покладено до основи розробленого програмного продукту Fuzzy_Forecassting, який створено у середовищі Eclipse на мові програмування Java

Developing new models for flyrock distance assessment in open-pit mines

Peer ReviewedObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPostprint (published version

Supervised intelligent committee machine method for hydraulic conductivity estimation

Hydraulic conductivity is the essential parameter for groundwater modeling and management. Yet estimation of hydraulic conductivity in a heterogeneous aquifer is expensive and time consuming. In this study; artificial intelligence (AI) models of Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Multilayer Perceptron Neural Network associated with Levenberg-Marquardt (ANN), and Neuro-Fuzzy (NF) were applied to estimate hydraulic conductivity using hydrogeological and geoelectrical survey data obtained from Tasuj Plain Aquifer, Northwest of Iran. The results revealed that SFL and NF produced acceptable performance while ANN and MFL had poor prediciton. A supervised intelligent committee machine (SICM), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of the hydraulic conductivity in Tasuj plain. The performance of SICM was also compared to those of the simple averaging and weighted averaging intelligent committee machine (ICM) methods. The SICM model produced reliable estimates of hydraulic conductivity in heterogeneous aquifers

Forecasting model for the change in stage of reservoir water level

Reservoir is one of major structural approaches for flood mitigation. During floods, early reservoir water release is one of the actions taken by the reservoir operator to accommodate incoming heavy rainfall. Late water release might give negative effect to the reservoir structure and cause flood at downstream area. However, current rainfall may not directly influence the change of reservoir water level. The delay may occur as the streamflow that carries the water might take some time to reach the reservoir. This study is aimed to develop a forecasting model for the change in stage of reservoir water level. The model considers the changes of reservoir water level and its stage as the input and the future change in stage of reservoir water level as the output. In this study, the Timah Tasoh reservoir operational data was obtained from the Perlis Department of Irrigation and Drainage (DID). The reservoir water level was categorised into stages based on DID manual. A modified sliding window algorithm has been deployed to segment the data into temporal patterns. Based on the patterns, three models were developed: the reservoir water level model, the change of reservoir water level and stage of reservoir water level model, and the combination of the change of reservoir water level and stage of reservoir water level model. All models were simulated using neural network and their performances were compared using on mean square error (MSE) and percentage of correctness. The result shows that the change of reservoir water level and stage of reservoir water model produces the lowest MSE and the highest percentage of correctness when compared to the other two models. The findings also show that a delay of two previous days has affected the change in stage of reservoir water level. The model can be applied to support early reservoir water release decision making. Thus, reduce the impact of flood at the downstream area

Rainfall Prediction in the Northeast Region of Thailand using Cooperative Neuro-Fuzzy Technique

Accurate rainfall forecasting is a crucial task for reservoir operation and flood prevention because it can provide an extension of lead-time for flow forecasting. This study proposes two rainfall time series prediction models, the Single Fuzzy Inference System and the Modular Fuzzy Inference System, which use the concept of cooperative neuro-fuzzy technique. This case study is located in the northeast region of Thailand and the proposed models are evaluated by four monthly rainfall time series data. The experimental results showed that the proposed models could be a good alternative method to provide both accurate results and human-understandable prediction mechanism. Furthermore, this study found that when the number of training data was small, the proposed model provided better prediction accuracy than artificial neural network

HydroPredicT_Extreme: A probabilistic method for the prediction of extremal high-flow hydrological events

Disastrous losses related to high-flow events have increased dramatically over the past decades largely due to an increase in flood-prone regions settlements and shift in hydrological trends largely due to Climate Change. To mitigate the societal impact of hydrological and hydraulic extremes, knowledge of the processes leading to these extreme events is vital. Hydrological modelling is one of the main tools in this quest for knowledge but comes with uncertainties. For that it is necessary to deeply study the impact of hydrological models’ structure on the magnitude and timing of extreme rainfall-runoff events. This paper is mainly aimed to show the development of a method called “HydroPredicT_Extreme” based on Bayesian Causal Modelling (BCM), a technique within Artificial Intelligence (AI). This method may enhance predictive capacity of extreme rainfall-runoff events. “HydroPredicT_ Extreme” follows an iterative methodology that comprise 2 main stages. First one comprises a mixed graphical/analytical method from Hydrograph. This stage is conditioned by two initial constraints which are, a) pluviometry station is representative of hydrograph downstream flow behaviour; b) there must be independence of events. This first stage comprises sub-phases such as: 1.1. Calculation of Response Time (RT) through a mixed graphical/analytical approach, 1.2 Subtraction of RT from the flow series to remove the Rainfall-Flow delay; 1.3 Calculation base flow rate; 1.4 Subtraction base-flow from flow series to work on absolute inputs. Second man stage is called Bayesian Causal Modelling Translation (BCMT) that comprises the 2.1 Learning, 2.2 Training, 2.3 Simulation through BCM modelling, 2.4 Sensitivity Analysis-Validation. This whole methodology will become a digital application and software that could be extrapolated to several similar case studies. This may be coupled with posterior devices for the prevention of catastrophic flood consequences in the form of MultiHazard-Early Warning System (MH-EWS) or others

Computing air demand using the Takagi–Sugeno model for dam outlets

An adaptive neuro-fuzzy inference system (ANFIS) was developed using the subtractive clustering technique to study the air demand in low-level outlet works. The ANFIS model was employed to calculate vent air discharge in different gate openings for an embankment dam. A hybrid learning algorithm obtained from combining back-propagation and least square estimate was adopted to identify linear and non-linear parameters in the ANFIS model. Empirical relationships based on the experimental information obtained from physical models were applied to 108 experimental data points to obtain more reliable evaluations. The feed-forward Levenberg-Marquardt neural network (LMNN) and multiple linear regression (MLR) models were also built using the same data to compare model performances with each other. The results indicated that the fuzzy rule-based model performed better than the LMNN and MLR models, in terms of the simulation performance criteria established, as the root mean square error, the Nash–Sutcliffe efficiency, the correlation coefficient and the Bias

Estimation of the Local Scour from a Cylindrical Bridge Pier Using a Compilation Wavelet Model and Artificial Neural Network

In the present study, an artificial neural network and its combination with wavelet theory are used as the computational tool to predict the depth of local scouring from the bridge pier. The five variables measured are the pier diameter of the bridge, the critical and the average velocities, the average diameter of the bed aggregates, and the flow depth. In this study, the neural wavelet method is used as a preprocessor. The data was passed through the wavelet filter and then passed to the artificial neural network. Among the various wavelet functions used for preprocessing, the dmey function results in the highest correlation coefficient and the lowest RMSE and is more efficient than other functions. In the wavelet-neural network compilation method, the neural network activator function was replaced by different wavelet functions. The results show that the neural network method with the Polywog4 wavelet activator function with a correlation coefficient of 87% is an improvement of 8.75% compared to the normal neural network model. By performing data filtering by wavelet and using the resulting coefficients in the neural network, the resulting correlation coefficient is 82%, only a 2.5% improvement compared to the normal neural network. By analyzing the results obtained from neural network methods, the wavelet-neural network predicted errors compared to experimental observations were 8.26, 1.56, and 1.24%, respectively. According to the evaluation criteria, combination of the best effective hydraulic parameters, the combination of wavelet function and neural network, and the number of neural network neurons achieved the best results

Prediction of the heating value of municipal solid waste : a case study of the city of Johannesburg

Abstract: In this study, a municipality-based model was developed for predicting the Lower heating value (LHV) of waste which is capable of overcoming the demerit of generalized model in capturing the peculiarity and characteristics of waste generated locally. The city of Johannesburg was used as a case study. Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy-Inference System (ANFIS) models were developed using the percentage composition of waste streams such as paper, plastics, organic, textile and glass as input variables and LHV as the output variable. The ANFIS model used three clustering techniques, namely Grid Partitioning (ANFIS-GP), Fuzzy C-means (ANFIS-FCM) and Subtractive Clustering (ANFIS-SC). ANN architectures with a range of 1-30 neurons in a single hidden layer were tested with three training algorithms and activation functions. The GP-clustered ANFIS model (ANFIS-GP) outperformed all other models with root mean square error (RMSE), mean absolute deviation (MAD), and mean absolute percentage error (MAPE) values of 0.1944, 0.1389 and 4.2982 respectively. Based on the result of this study, a GP-clustered ANFIS model is viable and recommended for predicting LHV of waste in a municipality