Honey Yield Forecast Using Radial Basis Functions

Honey yields are difficult to predict and have been usually associated with weather conditions. Although some specific meteorological variables have been associated with honey yields, the reported relationships concern a specific geographical region of the globe for a given time frame and cannot be used for different regions, where climate may behave differently. In this study, Radial Basis Function (RBF) interpolation models were used to explore the relationships between weather variables and honey yields. RBF interpolation models can produce excellent interpolants, even for poorly distributed data points, capable of mimicking well unknown responses providing reliable surrogates that can be used either for prediction or to extract relationships between variables. The selection of the predictors is of the utmost importance and an automated forward-backward variable screening procedure was tailored for selecting variables with good predicting ability. Honey forecasts for Andalusia, the first Spanish autonomous community in honey production, were obtained using RBF models considering subsets of variables calculated by the variable screening procedure

Week Ahead Electricity Price Forecasting Using Artificial Bee Colony Optimized Extreme Learning Machine with Wavelet Decomposition

Electricity price forecasting is one of the more complex processes, due to its non-linearity and highly varying nature. However, in today\u27s deregulated market and smart grid environment, the forecasted price is one of the important data sources used by producers in the bidding process. It also helps the consumer know the hourly price in order to manage the monthly electricity price. In this paper, a novel electricity price forecasting method is presented, based on the Artificial Bee Colony optimized Extreme Learning Machine (ABC-ELM) with wavelet decomposition technique. This has been attempted with two different input data formats. Each data format is decomposed using wavelet decomposition, Daubechies Db4 at level 6; all the decomposed data are forecasted using the proposed method and aggregate is formed for the final prediction. This prediction has been attempted in three different electricity markets, in Finland, Switzerland and India. The forecasted values of the three different countries, using the proposed method are compared with various other methods, using graph plots and error metrics and the proposed method is found to provide better accuracy

머신러닝기법을이용한양파저장기간중품질평가에 관한 연구

학위논문(석사) -- 서울대학교대학원 : 농업생명과학대학 바이오시스템공학과, 2023. 2. 김기석.Onions are a major vegetable in Korea. Long-term storage is therefore required to accommodate the demand throughout the year. Hence, storage needs to be considered carefully to extend the shelf life of onions. Temperature and relative humidity in storage plays a significant role in changing the quality of the onions, so temperature and humidity control during storage should be done to maintain the quality of the onions. Mechanical properties, weight loss, and respiration rate were chosen as the quality attributes of onions observed for quality changes during storage. In addition, developing a prediction model for changes in the quality of onions using machine learning needs to be carried out considering previous research, which is limited and only uses chemical kinetic models to predict changes in the quality of onions in storage. In this study, we stored onion at 0-1°C, collected the environmental data, and did weekly destructive measurements for 10 weeks of storage periods from March to June 2022. We measured Bio-yield stress using a compression test, respiration rate, and weight loss based on a weight scale sensor installed inside the chamber. Based on the data collection, we constructed three machine learning models to make a quality estimation model for onion bio-yield strength and weight loss using environment data – time, temperature, and relative humidity. We used two datasets for bio-yield stress data with 100 data of 10 weeks measurement and 127 data of the augmentation dataset using polynomial interpolation degree 2. The machine learning technique used in this study were multiple linear regression (MLR), partial square-least regression (PLSR), and support vector regression(SVR). The data were divided into train and test datasets in a ratio of 80:20 with 10-fold cross-validation on the training dataset. Then the regression models were evaluated by coefficient determination (R2), root mean square regression error (RMSE), and mean absolute percentage error (MAPE). From our study, the bio-yield stress decreased along with time, but the weight loss showed an increasing trend, for the respiration rate shows a relatively same trend since onion is a non-climacteric type. Furthermore, for the quality estimation model, we reported that the SVR and MLR models could be used to predict the quality attributes of onions during storage with R2 values of >0.8 for bio-yield stress and R2 >0.99 for weight loss parameters.양파는 한국의 주요 채소이다. 따라서 연중 꾸준히 발생하는 수요를 수용하 기 위해서는 장기 보관이 필요하다. 그러므로, 양파의 저장 수명을 늘리기 위 해서는 양파를 저장하는 방법에 유의해야 한다. 보관 중 온도와 상대습도는 양파의 품질을 변화시키는 중요한 역할을 하므로 온도와 습도 조절은 양파의 품질을 유지하기 위해 반드시 이루어져야 한다. 기계적 특성과 호흡 속도는 저장 기간 동안 관찰되는 양파의 주요 품질 특성으로 선택되었다. 또한 기존 의 연구들이 저장된 양파의 품질 변화를 예측하기 위해 화학 역학을 기반으 로 하는 모델만 사용했다는 점을 고려할 때, 기계학습을 활용하여 예측모델 을개발하는방법은충분히고려될만하다. 본 연구에서는 2022년 3월부터 6월까지 10주간의 저장 기간 동안 양파를 0- 1°C로 저장하면서 30분마다 환경 데이터를 수집하였으며, 매주 1회 파괴 실험을 수행하였다. 저장고 내부에 설치된 센서에서 수집된 생체중 데이터와 환 경 데이터(시간, 온도, 상대습도)를 이용하여 양파의 생물체 항복 강도 및 생 체중 감소에 대한 예측 모델을 생성하기 위해 3가지 기계학습 기법을 사용하 였다. 결과적으로 생물체 항복 강도 데이터 100개와 2차 다항식 보간법을 사 용한 127개의 환경 데이터가 모델 개발에 사용되었다. 본 연구에서 사용된 기 계 학습 기법은 다중 선형 회귀(MLR), 부분 제곱 최소 회귀(PLSR), 서포트 벡 터 머신(SVR)이다. 데이터는 80:20의 비율로 트레이닝세트와 테스트세트로 나뉘었고, 트레이닝 세트의 학습 과정에서 10배 교차 검증이 수행되었다. 회 귀 모델의 평가 기준으로는 결정 계수(R2), 평균 제곱근 오차(RMSE) 및 평균 절대 백분율 오차(MAPE)를 사용하였다. 데이터 수집 결과 양파의 생물체 항복 강도는 시간이 지날수록 감소하였으며 비급등형 호흡을 하는 양파의 특성상 호흡수는 시간에 관계없이 유지되는 경 향을 보였기 때문에 생체중은 선형적으로 감소하였다. 기계학습 모델 개발 결 과, MVR 및 SLR 모델을 사용하여 저장 중 양파의 품질 특성을 예측할 수 있 었으며, 생물체 항복 강도를 예측한 결과의 경우 R2 값이 >0.8, 생체중 감소량 예측모델은 R2>0.99의결과를얻었다.Chapter 1. Introduction 1 1.1 Background 1 1.2 Objectives 5 1.3 Literature review 6 1.3.1 Onion quality 7 1.3.2 Wireless sensor network 9 1.3.3 Prediction model 11 Chapter 2. Materials and Methods 14 2.1 Sample and storage equipment 14 2.2 Biophysical data measurement 17 2.2.1 Mechanical Properties 17 2.2.2 Respiration rate 21 2.2.3 Weight loss 24 2.3 Quality estimation model 26 2.3.1 Data Augmentation 28 2.3.2 Multiple linear regression (MLR) 30 2.3.3 Partial least square regression (PLSR) 31 2.3.4 Support vector regression (SVR) 33 Chapter 3. Result and Discussion 36 3.1 Environmental storage data 36 3.2 Result of Biophysical measurement 38 3.2.1 Bio-yield stress 38 3.2.2 Respiration rate 41 3.2.3 Weight loss 43 3.3 Prediction model evaluation 46 3.3.1 Bio-yield stress 46 3.3.2 Weight loss rate 52 Chapter 4. Conclusion 55 References 57 Abstract in Korean 64석

Plant Reproductive Ecology

Plant reproductive ecology has emerged as an indispensable discipline for enhancing crop productivity and conserving biodiversity. The adaptive significance of variation in traits associated with floral biology, pollination, seed dispersal, and seedling establishment is an integral component of plant reproductive ecology and evolutionary biology. This book explores the diversity of flower symmetry and the evolutionary patterns of internal structures of generative organs in angiosperms. The rapidly emerging global crisis of declining pollinators poses a major threat to food security. As such, the book also covers the diversity of plant-pollinator interactions, the impact of non-native exotic plant communities on native plants and pollinators, and strategies for the restoration of pollinator communities

Inferring efficient operating rules in multireservoir water resource systems: A review

[EN] Coordinated and efficient operation of water resource systems becomes essential to deal with growing demands and uncertain resources in water-stressed regions. System analysis models and tools help address the complexities of multireservoir systems when defining operating rules. This paper reviews the state of the art in developing operating rules for multireservoir water resource systems, focusing on efficient system operation. This review focuses on how optimal operating rules can be derived and represented. Advantages and drawbacks of each approach are discussed. Major approaches to derive optimal operating rules include direct optimization of reservoir operation, embedding conditional operating rules in simulation-optimization frameworks, and inferring rules from optimization results. Suggestions on which approach to use depend on context. Parametrization-simulation-optimization or rule inference using heuristics are promising approaches. Increased forecasting capabilities will further benefit the use of model predictive control algorithms to improve system operation. This article is categorized under: Engineering Water > Water, Health, and Sanitation Engineering Water > MethodsThe study has been partially funded by the ADAPTAMED project (RTI2018-101483-B-I00) from the Ministerio de Ciencia, Innovacion Universidades (MICINN) of Spain, and by the postdoctoral program (PAID-10-18) of the Universitat Politecnica de Valencia (UPV).Macian-Sorribes, H.; Pulido-Velazquez, M. (2019). Inferring efficient operating rules in multireservoir water resource systems: A review. Wiley Interdisciplinary Reviews Water. 7(1):1-24. https://doi.org/10.1002/wat2.1400S12471Aboutalebi, M., Bozorg Haddad, O., & Loáiciga, H. A. (2015). Optimal Monthly Reservoir Operation Rules for Hydropower Generation Derived with SVR-NSGAII. 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Power quality and electromagnetic compatibility: special report, session 2

The scope of Session 2 (S2) has been defined as follows by the Session Advisory Group and the Technical Committee: Power Quality (PQ), with the more general concept of electromagnetic compatibility (EMC) and with some related safety problems in electricity distribution systems. Special focus is put on voltage continuity (supply reliability, problem of outages) and voltage quality (voltage level, flicker, unbalance, harmonics). This session will also look at electromagnetic compatibility (mains frequency to 150 kHz), electromagnetic interferences and electric and magnetic fields issues. Also addressed in this session are electrical safety and immunity concerns (lightning issues, step, touch and transferred voltages). The aim of this special report is to present a synthesis of the present concerns in PQ&EMC, based on all selected papers of session 2 and related papers from other sessions, (152 papers in total). The report is divided in the following 4 blocks: Block 1: Electric and Magnetic Fields, EMC, Earthing systems Block 2: Harmonics Block 3: Voltage Variation Block 4: Power Quality Monitoring Two Round Tables will be organised: - Power quality and EMC in the Future Grid (CIGRE/CIRED WG C4.24, RT 13) - Reliability Benchmarking - why we should do it? What should be done in future? (RT 15

The technical efficiency of chrysanthemum flower farming: A stochastic frontier analysis

Over the years, improvements in standard of living and well-being have resulted in an increase in the demand for chrysanthemums, however, the recent COVID-19 pandemic has resulted in a fall in demand. As a result, this study investigates the technical efficiency of chrysanthemum farming and its major determinants. The study was conducted in Bumiaji Village, Bumiaji District, Batu, East Java, Indonesia between January and September 2022. Data was collected via interviews with chrysanthemum farmers using a questionnaire. A total of 35 chrysanthemum farms were selected using random sampling technique. The data was then analyzed using the stochastic frontier method combined with Maximum Likelihood Estimation (MLE). The results reveal that the efficiency of chrysanthemum farming is dominated by 0.91 to 0.93. (65.71 percent). Since technical efficiency is close to one, most chrysanthemum farmers are close to achieving maximum efficiency. The technical efficiency of chrysanthemum blooms was influenced by land area, inorganic fertilizers, organic fertilizers, and pesticides, but not by seeds or labor. The land area negatively impacts technical efficiency, implying that increasing land size decreases technological efficacy of chrysanthemum farming. Inorganic fertilizers, organic fertilizers, and pharmaceuticals have a positive effect or contribute to an increase in inorganic fertilizers, organic fertilizers, and pesticides. In terms of technical efficacy, chrysanthemum cultivation is close to its zenith. It is not necessary to exert effort to reach this ideal land, but inorganic fertilizers, organic fertilizers, and pesticides can assist

Predicting arsenic and heavy metals contamination in groundwater resources of Ghahavand plain based on an artificial neural network optimized by imperialist competitive algorithm

Background: The effects of trace elements on human health and the environment gives importance to the analysis of heavy metals contamination in environmental samples and, more particularly, human food sources. Therefore, the current study aimed to predict arsenic and heavy metals (Cu, Pb, and Zn) contamination in the groundwater resources of Ghahavand Plain based on an artificial neural network (ANN) optimized by imperialist competitive algorithm (ICA). Methods: This study presents a new method for predicting heavy metal concentrations in the groundwater resources of Ghahavand plain based on ANN and ICA. The developed approaches were trained using 75% of the data to obtain the optimum coefficients and then tested using 25% of the data. Two statistical indicators, the coefficient of determination (R2) and the root-mean-square error (RMSE), were employed to evaluate model performance. A comparison of the performances of the ICA-ANN and ANN models revealed the superiority of the new model. Results of this study demonstrate that heavy metal concentrations can be reliably predicted by applying the new approach. Results: Results from different statistical indicators during the training and validation periods indicate that the best performance can be obtained with the ANN-ICA model. Conclusion: This method can be employed effectively to predict heavy metal concentrations in the groundwater resources of Ghahavand plain. Keywords: Neural networks (computer), Groundwater, Models, Algorithms, Trace element

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