Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Potential of support-vector regression for forecasting stream flow

Vodotok je važan za hidrološko proučavanje zato što određuje varijabilnost vode i magnitudu rijeke. Inženjerstvo vodnih resursa uvijek se bavi povijesnim podacima i pokušava procijeniti prognostičke podatke kako bi se osiguralo bolje predviđanje za primjenu kod bilo kojeg vodnog resursa, na pr. projektiranja vodnog potencijala brane hidroelektrana, procjene niskog protoka, i održavanja zalihe vode. U radu se predstavljaju tri računalna programa za primjenu kod rješavanja ovakvih sadržaja, tj. umjetne neuronske mreže - artificial neural networks (ANNs), prilagodljivi sustavi neuro-neizrazitog zaključivanja - adaptive-neuro-fuzzy inference systems (ANFISs), i support vector machines (SVMs). Za stvaranje procjene korištena je Rijeka Telom, smještena u Cameron Highlands distriktu Pahanga, Malaysia. Podaci o dnevnom prosječnom protoku rijeke Telom, kao što su količina padavina i podaci o vodostaju, koristili su se za period od ožujka 1984. do siječnja 2013. za podučavanje, ispitivanje i ocjenjivanje izabranih modela. SVM pristup je dao bolje rezultate nego ANFIS i ANNs kod procjenjivanja dnevne prosječne fluktuacije vodotoka.Stream flow is an important input for hydrology studies because it determines the water variability and magnitude of a river. Water resources engineering always deals with historical data and tries to estimate the forecasting records in order to give a better prediction for any water resources applications, such as designing the water potential of hydroelectric dams, estimating low flow, and maintaining the water supply. This paper presents three soft-computing approaches for dealing with these issues, i.e. artificial neural networks (ANNs), adaptive-neuro-fuzzy inference systems (ANFISs), and support vector machines (SVMs). Telom River, located in the Cameron Highlands district of Pahang, Malaysia, was used in making the estimation. The Telom River’s daily mean discharge records, such as rainfall and river-level data, were used for the period of March 1984 – January 2013 for training, testing, and validating the selected models. The SVM approach provided better results than ANFIS and ANNs in estimating the daily mean fluctuation of the stream’s flow

Prediction of Runoff Coefficient under Effect of Climate Change Using Adaptive Neuro Inference System

دفعت الخصائص المعقدة لآلية جريان الأمطار ، جنبًا إلى جنب مع سماتها غير الخطية والشكوك المتأصلة ، العلماء إلى استكشاف مناهج بديلة مستوحاة من الظواهر الطبيعية. من أجل معالجة هذه العقبات ، تم استخدام الشبكات العصبية الاصطناعية (ANN) والأنظمة الضبابية (FL) كبدائل مجدية للنماذج الفيزيائية التقليدية. علاوة على ذلك ، يعتبر شراء البيانات الشاملة أمرًا ضروريًا للتحليل الدقيق والنمذجة. كان الهدف الأساسي لهذه الدراسة هو استخدام البيانات المناخية ذات الصلة مثل ؛ هطول الأمطار (P) ودرجة الحرارة (T) والرطوبة النسبية (Rh) وسرعة الرياح (Ws) للتنبؤ بمعامل الجريان السطحي باستخدام نموذج نظام الاستدلال العصبي الضبابي التكيفي (ANFIS). تم استخدام نطاقات مختلفة (60:40 ؛ 70:30 ؛ 80:20) لمرحلتي التدريب والاختبار. تم استخدام النموذج للتنبؤ بمعامل الجريان السطحي في حوض نهر أكسو في مقاطعة أنطاليا في تركيا. أجرت الدراسة تحليلاً مقارناً للنتائج ، مع مراعاة مؤشرات الأداء المختلفة للنموذج ، مثل متوسط ​​الخطأ المطلق (MAE) ، ومعامل كفاءة ناش-ساتكليف (NSE) ، وجذر متوسط ​​الخطأ التربيعي (RMSE) ، والارتباط. معامل (R2). بناءً على النتائج المقدمة ، أظهر النطاق (60:40) أفضل النتائج كما يتضح من قيم RMSE و MAE المنخفضة وقيم R2 و NSE العالية (RMSE: 0.056 ، MAE: 1.92 ، NSE: 0.868 ، R2 : 0.996). استنتج أن نموذج ANFIS يتنبأ بشكل رائع بمعاملات الجريان السطحي بمستوى استثنائي من الدقة ، كما تشير نتائج الدراسة إلى أنه يمكن تحقيق تقدير دقيق لمعامل الجريان السطحي باستخدام بيانات الأرصاد الجوية دون دمج بيانات أكثر تعقيدًا وترابطًا.The complex characteristics of the rainfall- runoff mechanism, along with its non-linear attributes and inherent uncertainties, have prompted scholars to explore alternative approaches inspired by natural phenomena. In order to tackle these obstacles, artificial neural networks (ANN) and fuzzy systems (FL) have been utilised as feasible substitutes for conventional physical models. Furthermore, the procurement of comprehensive data is considered essential for precise analysis and modelling. This study's primary objective was to use pertinent climatic data such as; Precipitation (P), Temperature (T), Relative humidity (Rh), and Wind speed (Ws) to predict the runoff coefficient using the Adaptive Neuro-Fuzzy Inference System (ANFIS) model. Different ranges (60:40; 70:30; 80:20) were used for the training and testing phases. The model was employed to predict the runoff coefficient in the Aksu river basin in Antalya province in Turkey. The study conducted a comparative analysis of the results, taking into account various performance indicators of the model, such as mean absolute error (MAE), Nash-Sutcliffe efficiency coefficient (NSE), root mean square error (RMSE), and correlation coefficient (R2). Based on the findings presented, the (60:40) range showed the best results as evidenced by its low RMSE and MAE values and its high R2 and NSE values (RMSE:0.056, MAE:1.92, NSE:0.868, R2 :0.996). It was concluded that the ANFIS model magnificently predicts runoff coefficients with an exceptional level of precision, also the study findings indicate that accurate runoff coefficient estimation can be achieved using meteorological data without incorporating more intricate and interrelated data

River flow forecasting using an integrated approach of wavelet multi-resolution analysis and computational intelligence techniques

In this research an attempt is made to develop highly accurate river flow forecasting models. Wavelet multi-resolution analysis is applied in conjunction with artificial neural networks and adaptive neuro-fuzzy inference system. Various types and structure of computational intelligence models are developed and applied on four different rivers in Australia. Research outcomes indicate that forecasting reliability is significantly improved by applying proposed hybrid models, especially for longer lead time and peak values

Efficiency of coupled invasive weed optimization-adaptive neuro fuzzy inference system method to assess physical habitats in streams

This study presents a coupled invasive weed optimization-adaptive neuro fuzzy inference system method to simulate physical habitat in streams. We implement proposed method in Lar national park in Iran as one of the habitats of Brown trout in southern Caspian Sea basin. Five indices consisting of root mean square error (RMSE), mean absolute error (MAE), reliability index, vulnerability index and Nash–Sutcliffe model efficiency coefficient (NSE) are utilized to compare observed fish habitats and simulated fish habitats. Based on results, measurement indices demonstrate model is robust to assess physical habitats in rivers. RMSE and MAE are 0.09 and 0.08 respectively. Besides, NSE is 0.78 that indicates robustness of model. Moreover, it is necessary to apply developed habitat model in a practical habitat simulation. We utilize two-dimensional hydraulic model in steady state to simulate depth and velocity distribution. Based on qualitative comparison between results of model and observation, coupled invasive weed optimization-adaptive neuro fuzzy inference system method is robust and reliable to simulate physical habitats. We recommend utilizing proposed model for physical habitat simulation in streams for future studies

Comparison of Artificial Intelligence Techniques for river flow forecasting

International audienceThe use of Artificial Intelligence methods is becoming increasingly common in the modeling and forecasting of hydrological and water resource processes. In this study, applicability of Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN), and Auto-Regressive (AR) models for forecasting of daily river flow is investigated and Seyhan River and Cine River was chosen as case study area. For the Seyhan River, the forecasting models are established using combinations of antecedent daily river flow records. On the other hand, for the Cine River, daily river flow and rainfall records are used in input layer. For both stations, the data sets are divided into three subsets, training, testing and verification data set. The river flow forecasting models having various input structures are trained and tested to investigate the applicability of ANFIS and ANN and AR methods. The results of all models for both training and testing are evaluated and the best fit input structures and methods for both stations are determined according to criteria of performance evaluation. Moreover the best fit forecasting models are also verified by verification set which was not used in training and testing processes and compared according to criteria. The results demonstrate that ANFIS model is superior to the GRNN and FFNN forecasting models, and ANFIS can be successfully applied and provide high accuracy and reliability for daily river flow forecasting

Suspended sediment estimation of Ekbatan reservoir sub basin using Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Artificial Neural Networks (ANN), and sediment rating curves (SRC)

River morphodynamics and sediment transportMechanics of sediment transpor

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