Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information

The complex temporal heterogeneity of rainfall coupled with mountainous physiographic context makes a great challenge in the development of accurate short-term rainfall forecasts. This study aims to explore the effectiveness of multiple rainfall sources (gauge measurement, and radar and satellite products) for assimilation-based multi-sensor precipitation estimates and make multi-step-ahead rainfall forecasts based on the assimilated precipitation. Bias correction procedures for both radar and satellite precipitation products were first built, and the radar and satellite precipitation products were generated through the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), respectively. Next, the synthesized assimilated precipitation was obtained by merging three precipitation sources (gauges, radars and satellites) according to their individual weighting factors optimized by nonlinear search methods. Finally, the multi-step-ahead rainfall forecasting was carried out by using the adaptive network-based fuzzy inference system (ANFIS). The Shihmen Reservoir watershed in northern Taiwan was the study area, where 641 hourly data sets of thirteen historical typhoon events were collected. Results revealed that the bias adjustments in QPESUMS and PERSIANN-CCS products did improve the accuracy of these precipitation products (in particular, 30-60% improvement rates for the QPESUMS, in terms of RMSE), and the adjusted PERSIANN-CCS and QPESUMS individually provided about 10% and 24% contribution accordingly to the assimilated precipitation. As far as rainfall forecasting is concerned, the results demonstrated that the ANFIS fed with the assimilated precipitation provided reliable and stable forecasts with the correlation coefficients higher than 0.85 and 0.72 for one- and two-hour-ahead rainfall forecasting, respectively. The obtained forecasting results are very valuable information for the flood warning in the study watershed during typhoon periods. © 2013 Elsevier B.V

An Experimental-Intelligent Method to Predict Noise Value of Drilling in Dimension Stone Industry

The noise of drilling in the dimension stone business is unbearable for both the workplace and the people who work there. In order to reduce the negative effects drilling has on the health of the environment, the drilling noise has to be measured, assessed, and controlled. The main purpose of this work is to investigate an experimental-intelligent method to predict the noise value of drilling in the dimension stone industry. For this purpose, 135 laboratory tests are designed on five types of rocks (four types of hard rock and one type of soft rock): and their results are measured in the first step. In the second step, due to the unpredicted and uncertain issues in this case, artificial intelligence (AI) approaches are applied, and the modeling is conducted using three intelligent systems (IS): namely an adaptive neuro-fuzzy inference system-SCM (ANFIS-SCM): an adaptive neuro-fuzzy inference system-FCM (ANFIS-FCM): and the radial basis function network (RBF) neural network. 75% of the samples are considered for training, and the rest for testing. Several models are constructed, and the results indicate that although there is no significant difference between the models according to the performance indices, the proposed construction of ANFIS-SCM can be considered as an efficient tool in the evaluation of drilling noise. Finally, several scenarios are designed with different input modes, and the results obtained prove that the types of rock and the drill bits are more important than the operational characteristics of the machine

Dynamic non-linear system modelling using wavelet-based soft computing techniques

The enormous number of complex systems results in the necessity of high-level and cost-efficient modelling structures for the operators and system designers. Model-based approaches offer a very challenging way to integrate a priori knowledge into the procedure. Soft computing based models in particular, can successfully be applied in cases of highly nonlinear problems. A further reason for dealing with so called soft computational model based techniques is that in real-world cases, many times only partial, uncertain and/or inaccurate data is available. Wavelet-Based soft computing techniques are considered, as one of the latest trends in system identification/modelling. This thesis provides a comprehensive synopsis of the main wavelet-based approaches to model the non-linear dynamical systems in real world problems in conjunction with possible twists and novelties aiming for more accurate and less complex modelling structure. Initially, an on-line structure and parameter design has been considered in an adaptive Neuro- Fuzzy (NF) scheme. The problem of redundant membership functions and consequently fuzzy rules is circumvented by applying an adaptive structure. The growth of a special type of Fungus (Monascus ruber van Tieghem) is examined against several other approaches for further justification of the proposed methodology. By extending the line of research, two Morlet Wavelet Neural Network (WNN) structures have been introduced. Increasing the accuracy and decreasing the computational cost are both the primary targets of proposed novelties. Modifying the synoptic weights by replacing them with Linear Combination Weights (LCW) and also imposing a Hybrid Learning Algorithm (HLA) comprising of Gradient Descent (GD) and Recursive Least Square (RLS), are the tools utilised for the above challenges. These two models differ from the point of view of structure while they share the same HLA scheme. The second approach contains an additional Multiplication layer, plus its hidden layer contains several sub-WNNs for each input dimension. The practical superiority of these extensions is demonstrated by simulation and experimental results on real non-linear dynamic system; Listeria Monocytogenes survival curves in Ultra-High Temperature (UHT) whole milk, and consolidated with comprehensive comparison with other suggested schemes. At the next stage, the extended clustering-based fuzzy version of the proposed WNN schemes, is presented as the ultimate structure in this thesis. The proposed Fuzzy Wavelet Neural network (FWNN) benefitted from Gaussian Mixture Models (GMMs) clustering feature, updated by a modified Expectation-Maximization (EM) algorithm. One of the main aims of this thesis is to illustrate how the GMM-EM scheme could be used not only for detecting useful knowledge from the data by building accurate regression, but also for the identification of complex systems. The structure of FWNN is based on the basis of fuzzy rules including wavelet functions in the consequent parts of rules. In order to improve the function approximation accuracy and general capability of the FWNN system, an efficient hybrid learning approach is used to adjust the parameters of dilation, translation, weights, and membership. Extended Kalman Filter (EKF) is employed for wavelet parameters adjustment together with Weighted Least Square (WLS) which is dedicated for the Linear Combination Weights fine-tuning. The results of a real-world application of Short Time Load Forecasting (STLF) further re-enforced the plausibility of the above technique

Estimation of daily soil temperature via data mining techniques in semi-arid climate conditions

This paper investigates the potential of data mining techniques to predict daily soil temperatures at 5-100 cm depths for agricultural purposes. Climatic and soil temperature data from Isfahan province located in central Iran with a semi-arid climate was used for the modeling process. A subtractive clustering approach was used to identify the structure of the Adaptive Neuro-Fuzzy Inference System (ANFIS), and the result of the proposed approach was compared with artificial neural networks (ANNs) and an M5 tree model. Result suggests an improved performance using the ANFIS approach in predicting soil temperatures at various soil depths except at 100 cm. The performance of the ANNs and M5 tree models were found to be similar. However, the M5 tree model provides a simple linear relation to predicting the soil temperature for the data ranges used in this study. Error analyses of the predicted values at various depths show that the estimation error tends to increase with the depth.Este artículo investiga el potencial de las técnicas de búsqueda y procesamiento de datos para pronosticar las temperaturas diarias del suelo a profundidades que van de los 5 a los 100 cm con propósitos agrícolas. Se utilizó la información climática y de temperatura del suelo de la provincia Ishafan, ubicada en el centro de Irán y de clima semiárido, para el proceso de modelamiento. Se usó un enfoque de agrupamiento sustractivo para identificar la estructura del Sistema de Inferencia Neuronal Difuso Adaptado (ANFIS, del inglés Adaptive Neuro-Fuzzy Inference System) y el resultado del acercamiento propuesto se comparó con redes artificiales neuronales (ANN) y el modelo tipo árbol M5. Los resultados sugieren un desempeño mejorado al usar el enfoque ANFIS en la predicción de las temperaturas del suelo en varios puntos de profundidad, excepto en los 100 cm. El desempeño de las redes artificiales neuronales y los modelos de árbol M5 fueron similares. Sin embargo, el modelo tipo árbol M5 provee una relación linear simple para predecir los rangos de datos de la temperatura del suelo utilizados en este estudio. Los análisis de error de los valores predichos a varias profundidades muestran que la estimación de error tiende a incrementarse con la profundidad

Multi-Output Adaptive Neuro-Fuzzy Inference System for Prediction of Dissolved Metal Levels in Acid Rock Drainage: a Case Study

Pyrite oxidation, Acid Rock Drainage (ARD) generation, and associated release and transport of toxic metals are a major environmental concern for the mining industry. Estimation of the metal loading in ARD is a major task in developing an appropriate remediation strategy. In this study, an expert system, the Multi-Output Adaptive Neuro-Fuzzy Inference System (MANFIS), was used for estimation of metal concentrations in the Shur River, resulting from ARD at the Sarcheshmeh porphyry copper deposit, southeast Iran. Concentrations of Cu, Fe, Mn and Zn are predicted using pH, sulphate (SO4) and magnesium (Mg) concentrations in the Shur River as input to the MANFIS. Three MANFIS models were implemented, Grid Partitioning (GP), the Subtractive Clustering Method (SCM) and the Fuzzy C-Means Clustering Method (FCM).A comparison was made between these three models and the results show the superiority of the MANFIS-SCM model. The results obtained indicate that the MANFIS-SCM model has potentialfor estimation of the metals with high a degree of accuracy and robustness

An adaptive neuro fuzzy inference system to model the uniaxial compressive strength of cemented hydraulic backfill

Purpose. The purpose of this paper is to develop the models for predicting the uniaxial compressive strength (UCS) of cemented hydraulic backfill (CHB), a widely used technique for filling underground voids created by mining operations as it provides the high strength required for safe and economical working environment and allows the use of waste rock from mining operations as well as tailings from mineral processing plants as ingredients. Methods. In this study, different modelling techniques such as conventional linear, nonlinear multiple regression and one of the evolving soft computing methods, adaptive neuro fuzzy inference system (ANFIS), were used for the prediction of UCS, the main criterion used to design backfill recipe. Findings. Statistical performance indices used to evaluate the efficiency of the developed models indicated that the ANFIS model can effectively be implemented for designing CHB with desired UCS. As proved by the performance indicators ANFIS model gives more compatible results with the expert opinion and current literature than conventional modelling techniques. Originality. In order to construct the models a very large database, containing more than 1600 UCS test results, was used. In addition to widely used conventional regression based modelling techniques, one of the evolving soft computing methods, ANFIS was employed. Numerical examples showing the implementation of constructed models were provided. Practical implementation. As proved by the statistical performance indicators, the developed models can be used for a reliable prediction of the UCS of CHB. However, more accurate results can be achieved by expanding the database and by constructing improved models using the algorithm presented in this paper.Мета. Побудова моделей для прогнозування межі міцності при одноосьовому стисканні цементної гідравлічної закладки для заповнення вироблених просторів шахт. Методика. Для досягнення поставленої мети були використані різні методи моделювання: лінійна та нелінійна множинна регресія, а також порівняно недавно став популярним метод програмування – адаптивне нейронечітке логічне виведення (ANFIS). За їх допомогою було спрогнозовано зміну міцності на одноосьове стискання, що є ключовим показником для визначення складу закладної суміші. Для побудови моделей використана значна база даних, яка включає результати більш ніж 1600 випробувань на одноосьове стискання. Лабораторними дослідженнями також визначалися властивості закладних матеріалів і суміші. Результати. Модель ANFIS дала найкращу продуктивність з урахуванням статистичних показників ефективності, таких як середня абсолютна процентна похибка і змінний обліковий запис. Статистичні показники продуктивності, які використовуються для оцінки ефективності розроблених моделей, свідчать, що моделювання за допомогою ANFIS дозволяє отримати результати, які більше відповідають експертній оцінці та даним з сучасної літератури, ніж інформація, отримана за допомогою традиційного моделювання. Встановлено, що на відміну від регресивного моделювання, ANFIS не вимагає заздалегідь визначених математичних рівнянь для взаємозв’язку між вхідними та вихідними змінними і використовує наданий набір даних для ефективного визначення структури моделі. Наукова новизна. Вперше для прогнозування міцності при одноосьовому стисканні були використані не лише традиційні способи моделювання, засновані на регресії, а й інноваційний метод програмування – адаптивне нейронечітке логічне виведення ANFIS. У статті наведені чисельні приклади впровадження нових побудованих моделей. Практична значимість. Статистичні індикатори продуктивності показали, що розроблені моделі можуть бути використані для надійного прогнозування міцності при одноосьовому стисканні й оптимальної рецептури закладної суміші. Однак, щоб отримати більш точні результати, необхідно мати більш широку базу даних і створити більш досконалі моделі на основі алгоритму, запропонованому в даній статті.Цель. Построение моделей для прогнозирования предела прочности при одноосном сжатии цементной гидравлической закладки для заполнения выработанных пространств шахт. Методика. Для достижения поставленной цели были использованы различные методы моделирования: линейная и нелинейная множественная регрессия, а также сравнительно недавно ставший популярным метод программирования – адаптивный нейронечеткий логический вывод (ANFIS). С их помощью было спрогнозировано изменение прочности на одноосное сжатие, что является ключевым показателем для определения состава закладочной смеси. Для построения моделей использована обширная база данных, которая включает результаты более чем 1600 испытаний на одноосное сжатие. Лабораторными исследованиями также определялись свойства закладочных материалов и смеси. Результаты. Модель ANFIS дала наилучшую производительность с учетом статистических показателей эффективности, таких как средняя абсолютная процентная погрешность и переменная учетная запись. Статистические показатели производительности, используемые для оценки эффективности разработанных моделей, свидетельствуют, что моделирование с помощью ANFIS позволяет получить результаты, которые более соответствуют экспертной оценке и данным из современной литературы, чем информация, полученная при помощи традиционного моделирования. Установлено, что в отличие от регрессионного моделирования, ANFIS не требует заранее определенных математических уравнений для взаимосвязи между входными и выходными переменными и использует предоставленный набор данных для эффективного определения структуры модели. Научная новизна. Впервые для прогнозирования прочности при одноосном сжатии были использованы не только традиционные способы моделирования, основанные на регрессии, но и инновационный метод программирования – адаптивный нейронечеткий логический вывод ANFIS. В статье приведены численные примеры внедрения новых построенных моделей. Практическая значимость. Статистические индикаторы производительности показали, что разработанные модели могут быть использованы для надежного прогнозирования прочности при одноосном сжатии и оптимальной рецептуры закладочной смеси. Однако, чтобы получить более точные результаты, необходимо иметь более широкую базу данных и создать более совершенные модели на основе алгоритма, предложенного в данной статье.The authors thank the staff and the managers of Jinfeng underground gold mine for their helps and cooperation during field and laboratory studies. The company is also acknowledged for the permission to use and publish the data

Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS

Water table forecasting plays an important role in the management of groundwater resources in agricultural regions where there are drainage systems in river valleys. The results presented in this paper pertain to an area along the left bank of the Danube River, in the Province of Vojvodina, which is the northern part of Serbia. Two soft computing techniques were used in this research: an adaptive neurofuzzy inference system (ANFIS) and an artificial neural network (ANN) model for one-month water table forecasts at several wells located at different distances from the river. The results suggest that both these techniques represent useful tools for modeling hydrological processes in agriculture, with similar computing and memory capabilities, such that they constitute an exceptionally good numerical framework for generating high-quality models

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

Molecular And Biochemical Analysis Of Water Stress Induced Responses In Grape

Water stress affects vine productivity, disease tolerance, and enological characteristics of grape. Florida Hybrid Bunch grape are developed through hybridization of local grape spp with Vitis vinifera. These cultivars are mostly grown in southeast region of United States. Water deficit conditions resulted due to failure of rains in the region has developed concern among Florida grape growers to increase water use efficiency of grape. The goal of this research is to identify genes and proteins differentially expressed in response to water stress and to correlate these changes with enological characteristics. Investigating transcripts and proteins will allow us to correlate them and confirm the involvement of specific genes responding to stress. Florida hybrid bunch ‘Suwannee’ grape plants were maintained under green house conditions. Water stress was induced by withholding irrigation. The leaf samples were collected from both irrigated and stressed plants at 5, 10, 15 and 20 day interval. We generated over 200 Subtractive Hybridization PCR products from control and water stressed leaf tissues. Cloning, sequencing and transcript analysis revealed that, 54 genes related to drought and defense regulated pathways out of 125 characterized transcripts. Proteins were extracted from leaf tissue with trichloroacetic acid /acetone and separated by two-dimensional polyacrylamide gel electrophoresis (2D-PAGE). The proteins were sequenced in LC/Mass Spectrophotometer. The most important differentially expressed genes include sucrose synthase, actin, isoprene synthase, ABF3, SNF1 related protein kinase, WRKY type transcription factors, AP2, ASR2, glyoxalase I and, cytochrome b which play significant role in cell permeability, transportation, photosynthesis and, maintenance in osmotic stress. We have found that ribulose bisphosphate carboxylase and phosphoribulokinase, which play major role in photosynthesis, were suppressed in response to water stress in Florida hybrid bunch. The results suggested that water stress affects expression of cDNAs associated with defense and drought regulated functions. Such profiling studies will be used to explicate specific pathways disconcerted by water deficit treatments, and in the identification of varietal differences