Precipitation-Runoff Modeling Using Artificial Neural Networks and Conceptual Models

The potential of multiple linear regression (MLR) and artificial neural network (ANN) techniques in predicting transient water levels over a groundwater basin were compared. MLR and ANN modeling was carried out at 17 sites in Japan, considering all significant inputs: rainfall, ambient temperature, river stage, 11 seasonal dummy variables, and influential lags of rainfall, ambient temperature, river stage and groundwater level. Seventeen site-specific ANN models were developed, using multi-layer feed-forward neural networks trained with Levenberg-Marquardt backpropagation algorithms. The performance of the models was evaluated using statistical and graphical indicators. Comparison of the goodness-of-fit statistics of the MLR models with those of the ANN models indicated that there is better agreement between the ANN-predicted groundwater levels and the observed groundwater levels at all the sites, compared to the MLR. This finding was supported by the graphical indicators and the residual analysis. Thus, it is concluded that the ANN technique is superior to the MLR technique in predicting spatio-temporal distribution of groundwater levels in a basin. However, considering the practical advantages of the MLR technique, it is recommended as an alternative and cost-effective groundwater modeling tool.

Utilizing Artificial Neural Networks for Establishing Hearing-Loss Predicting Models Based on a Longitudinal Dataset and Their Implications for Managing the Hearing Conservation Program

Forecasts of future events are required in many of the activities associated with the planning and operation of the components of a water resource system. For the hydrologic component, there is a need for both short- and long-term forecasts of river flow events in order to optimize the system or to plan for future expansion or reduction. This paper presents the comparison of different artificial neural network (ANN) techniques in short- and long-term continuous and intermittent daily streamflow forecasting. The studies in modelling the intermittent series are quite limited because of the complexity of fitting models in to these series. The available conventional models necessitate the adjustment of numerous parameters for calibration. Three different ANN techniques, namely, feed-forward back propagation (FFBP), generalized regression neural networks, and radial basis function-based neural networks (RBF) are applied to continuous and intermittent river flow data of two Turkish rivers for short-range and long-range forecasting studies. The k-fold partitioning method is employed for preparing the ANN training data successfully. In general, the forecasting performance of RBF is found to be superior to the other two ANN techniques and a time series model in terms of the selected performance criteria. It was observed that the FFBP method had some drawbacks such as a local minima problem and negative flow generation.

Artificial Neural Network (ANN) techniques have recently been applied to many different fields and have demonstrated their capabilities in solving complex problems. In a business environment, the techniques have been applied to predict bond ratings and stock price performance. In these applications, ANN techniques outperformed widely-used multivariate statistical techniques. The purpose of this paper is to compare the ANN method with the Discriminant Analysis (DA) method in order to understand the merits of ANN that are responsible for the higher level of performance. The paper provides an overview of the basic concepts of ANN techniques in order to enhance the understanding of this emerging technique. The similarities and differences between ANN and DA techniques in representing their models are described. This study also proposes a method to overcome the limitations of the ANN approach, Finally, a case study using a data set in a business environment demonstrates the superiority of ANN over DA as a method of classification of observations.

One of the key factors for sustainability in agricultural systems, particularly, for cultivation of date palms, is the identification of the performance parameters of the pollen grains of male date palms (Phoenix dactylifera L.). This study was carried out to predict the performance parameters of pollen grains using an artificial neural network (ANN) model. The morphological features of spathe length, spathe weight, number of pinnae per leaf, leaf length, leaf width, length of the pinna part, pinna length, pinna width, length of the spathe stem, and spathe width, as well as the concentrations of minerals such as Mg, N, K, P, and Ca in their leaves were used as inputs to the ANN model. For this purpose, we collected the required data from nine male date palms grown in Saudi Arabia. The ANN model utilized in this work included an input layer with 15 parameters, a hidden layer of 30 neurons, and an output layer with 8 neurons. The ANN model was trained with 27 patterns. Seven patterns were utilized for testing purposes. The coefficient of determination (R2) obtained between the observed and predicted performance parameters’ values using the testing dataset was 0.902 for the number of strands per spathe, 0.967 for strand length, 0.963 for the number of flowers per strand, 0.941 for the number of flowers per spathe, 0.985 for the weight of pollen grains per spathe, 0.810 for the pollen grains’ viability, 0.936 for the pollen grains’ length, and 0.992 for the pollen grains’ width. The length of the spathe stem had the most critical effect on how the ANN model predicted the values of the dependent variables, i.e., the number of strands per spathe, with a percentage of contribution of 17.66%; the weight of pollen grains per spathe, with 17.85%; the pollen grains’ length, with 19.78%, and the pollen grains’ width, with a percentage of contribution of 30.59%. Spathe weight had the most critical influence on strand length and pollen grains’ viability, with percentages of 26.29% and 14.92%, respectively. Leaf width had the most critical effect on the number of flowers per spathe, with a percentage of 12.55%. The elemental concentration of K in the male date palm leaves had the most critical effect on the number of flowers per strand, with a percentage of 13.98%. It was therefore concluded that using a modeling process with the ANN technique can help estimate the performance parameters of male date palms’ pollen grains for different purposes, such as providing a starting point for mathematical analyses associated with the physiological mechanisms of male date palm. Moreover, the outcomes of this research work can be supportive as a practical tool in this field of study.

Accurate estimation of evaporation from agricultural fields and water bodies is needed for the efficient utilisation and management of water resources at the watershed and regional scale. In this study, multiple linear regression (MLR) and artificial neural network (ANN) techniques are used for the estimation of monthly pan evaporation. The modelling approach includes the various combination of six measured climate parameters consisting of maximum and minimum air temperature, maximum and minimum relative humidity, sunshine hours and wind speed of two stations, namely Gangtok in Sikkim and Imphal in the Manipur states of the northeast hill region of India. Average monthly evaporation varies from 0.62 to 2.68 mm/day for Gangtok, whereas it varies from 1.4 to 4.3 mm/day for Imphal during January and June, respectively. Performance of the developed MLR and ANN models was compared using statistical indices such as coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) with measured pan evaporation values. Correlation analysis revealed that temperature, wind speed and sunshine hour had positive correlation, whereas relative humidity had a negative correlation with pan evaporation. Results showed a slightly better performance of the ANN models over the MLR models for the prediction of monthly pan evaporation in the study area.

An improved noise model for pseudomorphic high electron mobility transistors (PHEMT) based on the combination of the artificial neural network (ANN) and conventional equivalent circuit modeling technique is presented. The frequency dispersion of the gate noise model parameter P, drain noise model parameter R, and the correlation coefficient C have been taken into account by using an ANN model. The influence of the gate leakage current can be accommodated by using the proposed noise model. The noise model parameters are determined directly from on wafer noise parameters measurement based on the noise correlation matrix technique. Good prediction for noise parameters and significant improvements of the accuracy of noise parameters are obtained up to 26 GHz for 2 × 40 μm gate width (number of gate fingers × unit gate width) 0.25 μm Double Heterojunction δ-doped PHEMTs over a wide range of bias points. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.

A single artificial neural network (ANN) model is inadequate for predicting phytoplankton biomass in a large lake due to its high spatial heterogeneity. In this study, ANN was combined with a clustering technique to simulate phytoplankton biomass in a large lake (Lake Poyang) using a 7-year dataset. Two ANN models (named ANN_Downstream and ANN_Upstream) were developed for the downstream and upstream areas based on the k-means clustering results of 17 sampling sites at Lake Poyang, China. They performed better than ANN_Poyang (an ANN model for the whole lake), indicating the success of the clustering technique in improving ANN models for predicting phytoplankton biomass in different sub-regions of the large lake. A sensitivity analysis based on ANN_Downstream and ANN_Upstream showed that phytoplankton dynamics responded differently to environmental variables in different sub-regions of Lake Poyang. This case study demonstrated the good performance of ANN models in describing phytoplankton dynamics, and the potential of coupling ANN with a clustering technique to describe the spatial heterogeneity of natural ecosystems.

Monthly streamflow forecasting during spring-summer runoff season using snow telemetry (SNOTEL) precipitation and snow water equivalent (SWE) as predictors in the Rio Grande Headwaters Basin in Colorado was investigated. The transfer-function noise (TFN) models with SNOTEL precipitation input were built for monthly streamflow. Then, one-month-ahead forecasts of TFN models for the spring-summer runoff season were modified with SWE using an artificial neural networks (ANN) technique denoted in this study as hybrid TFN+ANN. The results indicated that the hybrid TFN+ANN approach not only demonstrated better generalization capability but also improved one-month-ahead forecast accuracy significantly when compared with single TFN and ANN models. The normalized root mean squared errors (NRMSE) of one-month-ahead forecasts of TFN, ANN, and TFN+ANN approaches for spring-summer runoff season were 0.38, 0.30, and 0.25. These findings accentuate that the presented stochastic hybrid modeling approach is an advantageous...

Purpose – This paper aims to study the comparison between a response surface methodology (RSM) and artificial neural network (ANN) in the modelling and prediction of surface roughness during endmilling of glass-fibre-reinforced polymer composites. Design/methodology/approach – Aiming to achieve this goal, several milling experiments were performed with polycrystalline diamond inserts at different machining parameters, namely, feed rate, cutting speed, depth of cut and fibre orientation angle. Mathematical model is created using central composite face-centred second-order in RSM and the adequacy of the model was verified using analysis of variance. ANN model is created using the back propagation algorithm. Findings – With regard to the machining test, it was observed that feed rate is the dominant parameter that affects the surface roughness, followed by the fibre orientation. The comparison results show that models provide accurate prediction of surface roughness in which ANN performs better than RSM. Originality/value – The data predicted from ANN are very nearer to experimental results compared to RSM; therefore, this ANN model can be used to determine the surface roughness for various fibre-reinforced polymer composites and also for various machining parameters.

In this paper, the modified single phase nine-level CHB-MLI is proposed based on the selective harmonic elimination pulse width modulation (SHE-PWM) using artificial neural network (ANN) technique. The number of proposed switching devices reduced in a now modified CHB-MLI compared with the conventional structure of CHB-MLI which decreases the switching frequency of the system, leading to a decrease in the total power switching losses. The switching angles of CHB-MLI in this paper are detected and controlled based on the ANN technique into selective harmonic elimination transcendental nonlinear equation which is considered as the popular and powerful method for minimizing the harmonic order at the output voltage waveform of the MLI and produces a low ratio of total harmonic distortion (THD%). This paper aims to design, simulate and build up new modified structure of the single-phase nine-levels CHB-MLI and predict the real-time switch in order to generate superior switching angles by using ANN technique. MATLAB simulation has been used for designing and simulating the nine-levels single-phase CHB-MLI with ANN technique for switching angles prediction. It is found the proposed technique able to reduce harmonic contents of output waveform.

<div class="htmlview paragraph">Artificial Neural Network(ANN) techniques are used to develop a system to detect High Impedance Faults(HIFs) in electric power distribution lines. Encouraging results were observed with a simple Multi-layer Perceptron(MLP) trained with the backpropagation learning algorithm. Although the results are not significantly better than those reported with other algorithmic approaches, ANN techniques have potential advantages over the other approaches; namely, ability to train the system easily to accommodate different feeder characteristics, ability to adapt and so become a better detector with experience and better fault tolerance. When these features are incorporated, the system is expected to perform better than existing systems.</div> <div class="htmlview paragraph">The system we developed for the current phase, the training strategies used, the tests conducted and the results obtained are discussed in this paper. Also background discussions on existing HIF detection techniques, and ANN techniques can be found in this paper. A feature extraction method based on fuzzy logic and a bin structure is discussed.</div>

این مطالعه به منظور بررسی پیش بینی شاخص های عملکرد تولیدی در مرغان تخم گذار با استفاده از شبکه های عصبی مصنوعی و رگرسیون غیرخطی چندگانه انجام شد. بررسی بر روی اطلاعات چهار دوره متوالی پرورش در یک واحد پرورش مرغ تخم گذار صورت گرفت. روش های داده-کاوی شامل رگرسیون خطی و غیر خطی، شبکه عصبی پرسپترون سه لایه، شبکه عصبی پرسپترون چهار لایه و شبکه عصبی با تابع پایه ای شعاعی بود. در این مدل ها از متغیرهای سن گله، میزان خوراک مصرفی و فصل تولید به عنوان متغیر پیشگو و شاخص های عملکرد تولیدی شامل درصد تخم-گذاری، وزن توده ای تخم مرغ تولیدی و ضریب تبدیل غذایی به عنوان متغیر پاسخ استفاده شد. نتایج نهایی رگرسیون های خطی نشان داد که برای تمامی متغیرهای وابسته مورد مطالعه متغیر مستقل سن گله معنی دار می باشد. بنابراین رگرسیون غیر خطی شاخص های عملکرد تولیدی در مقابل سن برای مقایسه با شبکه های عصبی مختلف مورد بررسی قرار گرفت و برای مقایسه کلیه مدل ها از ضریب تعیین (R2) و میانگین قدر مطلق خطا (MAE) استفاده شد. نتایج نشان داد بین شبکه های عصبی مصنوعی مختلف مورد مطالعه، شبکه با تابع پایه ای شعاعی بهتر از سایر مدل های در پیش‌بینی شاخص های عملکرد تولیدی مرغان تخم گذار عمل می کند.

Comparison of Artificial Neural Network and Multiple Regression Analysis Techniques in Predicting the Mechanical Properties of A3 56 Alloy

<abstract> <p>Coffee is a major industrial crop that creates high economic value in Thailand and other countries worldwide. A lack of certainty in forecasting coffee production could lead to serious operation problems for business. Applying machine learning (ML) to coffee production is crucial since it can help in productivity prediction and increase prediction accuracy rate in response to customer demands. An ML technique of artificial neural network (ANN) model, and a statistical technique of autoregressive integrated moving average (ARIMA) model were adopted in this study to forecast arabica coffee yields. Six variable datasets were collected from 2004 to 2018, including cultivated areas, productivity zone, rainfalls, relative humidity and minimum and maximum temperatures, totaling 180 time-series data points. Their prediction performances were evaluated in terms of correlation coefficient (R<sup>2</sup>), and root means square error (RMSE). From this work, the ARIMA model was optimized using the fitting model of (p, d, q) amounted to 64 conditions through the Akaike information criteria arriving at (2, 1, 2). The ARIMA results showed that its R<sup>2</sup> and RMSE were 0.7041 and 0.1348, respectively. Moreover, the R<sup>2</sup> and RMSE of the ANN model were 0.9299 and 0.0642 by the Levenberg-Marquardt algorithm with TrainLM and LearnGDM training functions, two hidden layers and six processing elements. Both models were acceptable in forecasting the annual arabica coffee production, but the ANN model appeared to perform better.</p> </abstract>