Peramalan Aliran Masukan Waduk Mrica Menggunakan ModelThomas-Fieringdan Jaringan Syaraf Tiruan ANFIS

Inflow forecasting in hydrology processes is important tool in water resources management,planning, and utilization. The fulfillment of this operational hydrology isvery applicable, especially where onlyan insufficient amount of data collected over an insufficient length of time is available. The Thomas-Fiering Method is one of the most useful and widely used synthetic flow models. In last year’s, ArtificialNeural Network (ANN)method and Fuzzy Logic have introduced in hydrological processes. Mrica hydropower reservoir in Central Java, Indonesia, has suffered water sustainability andenergy sustainability problems since the reservoir management used simple-operator judged waterinflow forecasting method. In this paper, an ANN and Fuzzy Logic hybrid algorithm calledAdaptive Neuro-Fuzzy Inference System(ANFIS) and Thomas-Fiering model are employed to estimate water inflow to the Mrica reservoir. ANFIS performs better for long-range inflow forecasting, while Thomas-Fiering model was better for short-range forecasting

Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. Temporal data arise in these real-world applications often involves a mixture of long-term and short-term patterns, for which traditional approaches such as Autoregressive models and Gaussian Process may fail. In this paper, we proposed a novel deep learning framework, namely Long- and Short-term Time-series network (LSTNet), to address this open challenge. LSTNet uses the Convolution Neural Network (CNN) and the Recurrent Neural Network (RNN) to extract short-term local dependency patterns among variables and to discover long-term patterns for time series trends. Furthermore, we leverage traditional autoregressive model to tackle the scale insensitive problem of the neural network model. In our evaluation on real-world data with complex mixtures of repetitive patterns, LSTNet achieved significant performance improvements over that of several state-of-the-art baseline methods. All the data and experiment codes are available online.Comment: Accepted by SIGIR 201

Neural networks and their application in water management

U radu su detaljnije opisane neuralne mreže koje su dnaas sve češće u primjeni pri rješavanju problema iznimno visokog stupnja složenosti. Uz definiranje neuralnih mreža, dana je njihova podjela, prikaz strukture i osobina te je izdvojen pregled povijesnog razvoja. Istaknuta je primjenu neuralnih mreža unutar područja vodnog gospodarstva i to prije svega na području Hrvatske. Pri tome su ukratko opisane najznačajnije neuralne mreže koje su do danas razvijene i rabljene u praksi.Neural networks, nowadays increasingly used for solving problems of exceptionally high level of complexity, are described in great detail. After definition of neural networks, their classification is given, and their structure and properties are presented. An overview of their historic development is also given. An emphasis is placed on the use of neural networks in water management, especially in the territory of Croatia. At that, most significant neural networks developed so far and used in current practice are briefly presented

Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction

This paper examines the forecasting performance of ARIMA and artificial neural networks model with published stock data obtained from New York Stock Exchange. The empirical results obtained reveal the superiority of neural networks model over ARIMA model. The findings further resolve and clarify contradictory opinions reported in literature over the superiority of neural networks and ARIMA model and vice versa

Combining group method of data handling models using artificial bee colony algorithm for time series forecasting

Time series forecasting which uses models to predict future values based on some historical data is an important area of forecasting, and has gained the attention of researchers from various related fields of study. In line with its popularity, various models have been introduced for producing accurate time series forecasts. However, to produce an accurate forecast is not an easy feat especially when dealing with nonlinear data due to the abstract nature of the data. In this study, a model for accurate time series forecasting based on Artificial Bee Colony (ABC) algorithm and Group Method of Data Handling (GMDH) models with variant transfer functions, namely polynomial, sigmoid, radial basis function and tangent was developed. Initially, in this research, the GMDH models were used to forecast the time series data followed by each forecast that was combined using ABC. Then, the ABC produced the weight for each forecast before aggregating the forecasts. To evaluate the performance of the developed GMDH-ABC model, input data on tourism arrivals (Singapore and Indonesia) and airline passengers’ data were processed using the model to produce reliable forecast on the time series data. To validate the evaluation, the performance of the model was compared against benchmark models such as the individual GMDH models, Artificial Neural Network (ANN) model and combined GMDH using simple averaging (GMDH-SA) model. Experimental results showed that the GMDH-ABC model had the highest accuracy compared to the other models, where it managed to reduce the Root Mean Square Error (RMSE) of the conventional GMDH model by 15.78% for Singapore data, 28.2% for Indonesia data and 30.89% for airline data. As a conclusion, these results demonstrated the reliability of the GMDH-ABC model in time series forecasting, and its superiority when compared to the other existing models

Multivariate Statistical Analysis for Water Demand Modeling

The actual level of water demand is the driving force behind the hydraulic dynamics in water distribution systems. Consequently, it is crucial to estimate it as accurately as possible in order to result in reliable simulation models. In this paper, a copula-based multivariate analysis has been proposed and used for demand prediction for given return period. The analysis is applied to water consumption data collected in the water distribution network of Palermo (Italy). The approach showed to produce consisted demand patterns and to be a powerful tool to be coupled with water distribution network models for design or analysis problems. (C) 2014 Published by Elsevier Ltd