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Evaluating Calibration and Spectral Variable Selection Methods for Predicting Three Soil Nutrients Using Vis-NIR Spectroscopy

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REMOTE SENSING 2021年第19期13卷 4000-4000页

作者： Guo, Peng Li, Ting Gao, Han Chen, Xiuwan Cui, Yifeng Huang, Yanru Peking Univ Inst Remote Sensing & Geog Informat Syst Beijing 100871 Peoples R China Sichuan Agr Univ Coll Resources Chengdu 611130 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China

Soil nutrients, including soil available potassium (SAK), soil available phosphorous (SAP), and soil organic matter (SOM), play an important role in farmland soil productivity, food security, and agricultural management. Spectroscopic analysis has proven to be a rapid, nondestructive, and effective technique for predicting soil properties in general and potassium, phosphorous, and organic matter in particular. However, the successful estimation of soil nutrient content by visible and near-infrared (Vis-NIR) reflectance spectroscopy depends on proper calibration methods (including preprocessing transformation methods and multivariate methods for regression analysis) and the selection of appropriate variable selection techniques. In this study, raw spectrum and 13 preprocessing transformations combined with 2 variable selection methods (competitive adaptive reweighted sampling (CARS) and the successive projections algorithm (SPA)) and 2 regression algorithms (support vector machine (SVM) and partial least squares regression (PLSR)), for a total of 56 calibration methods, were investigated for modeling and predicting the above three soil nutrients using hyperspectral Vis-NIR data (400-2450 nm). The results show that first-order derivatives based on logarithmic and inverse transformations (FD-LGRs) can provide better predictions of soil available potassium and phosphorous, and the best form of soil organic matter transformation is SG+MSC. CARS was superior to the SPA in selecting effective variables, and the PLSR model outperformed the SVM models. The best estimation accuracies (R-2, RMSE) for soil available potassium, phosphorous, and organic matter were 0.7532, 32.3090 mg/kg;0.7440, 6.6910 mg/kg;and 0.9009, 3.2103 g/kg, respectively, and their corresponding calibration methods were (FD-LGR)/SPA/PLSR, (FD-LGR)/SPA/PLSR, and SG+MSC/CARS/SVM, respectively. Overall, for the prediction of the soil nutrient content, organic matter was superior to available phosphorous, foll

关键词： spectral analysis preprocessing transformation methods spectral variable selection regression algorithms soil nutrients

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Characterizing Complexity and Self-Similarity Based on Fractal and Entropy Analyses for Stock Market Forecast Modelling

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EXPERT SYSTEMS WITH APPLICATIONS 2020年第0期144卷 113098-000页

作者： Karaca, Yeliz Zhang, Yu-Dong Muhammad, Khan Univ Massachusetts Med Sch Worcester MA 01655 USA Univ Leicester Dept Informat Leicester LE1 7RH Leics England Sejong Univ Dept Software Seoul South Korea

Complex systems constitute components that interact with one another and involve phenomena which are not always easy to understand in terms of their components and interactions. Alternative mathematical models have been developed so that the users' tasks can be facilitated and an actual assistance can be provided for decision-making processes in case of every encountered incident which requires critical decision-making. Within this framework, financial systems can be regarded as complex systems with their volatile and vulnerable nature along with various parameters and interactions involved. Forecasting shifts in stock indices is crucial to validate the potential strategies of monetary mechanisms. Therefore, forecasting is an essential step in financial decision-making to manage data selection and attain robust prediction. Our purpose is to optimize the stock indices' forecasting model in the stock indices dataset, constructed from the daily values. The following steps were applied to demonstrate the critical significance of Hurst exponent (HE) computed by Rescaled Range (10) fractal analysis when used as indicator in conjunction with Shannon entropy (SE) and Renyi entropy (RE) for the future forecasting ability of the stock indices. With this aim, the following stages were performed with indicators obtained from the applications and added into the dataset in the respective order. The first stage consists of: i) HE indicator, ii) Entropy based SE and RE indicators, iii) HE, SE and RE indicators. As the second stage, stock indices day-to-day valuation was evaluated using Multi Layer regression (MLR), Support Vector regression (SVR) and Feed Forward Back Propagation (FFBP) algorithms, applied for each indicator for comparative analysis. When compared with earlier works, no relevant work exists in the literature in which the algorithms and above-mentioned indicators have been used in conjunction with one another. This paper, through the multistage methodology and propo

关键词： Hurst exponent Shannon entropy Renyi entropy Artificial neural network regression algorithms Fractal analysis Complexity and Self-Similarity Stock indices forecasting

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Data Mining Approach for Feature Based Parameter Tunning for Mixed-Integer Programming Solvers

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Procedia Computer Science 2017年 108卷 715-724页

作者： Matheus G. Vilas Boas Haroldo G. Santos Rafael de S.O. Martins Luiz H.C. Merschmann Federal University of Ouro Preto Department of Computing Ouro Preto Brazil Federal University of Ouro Preto Department of Information Systems and Computing João Monlevade Brazil Federal University of Lavras Department of Computer Science Lavras Brazil

Integer Programming (IP) is the most successful technique for solving hard combinatorial optimization problems. Modern IP solvers are very complex programs composed of many different procedures whose execution is embedded in the generic Branch & Bound framework. The activation of these procedures as well the definition of exploration strategies for the search tree can be done by setting different parameters. Since the success of these procedures and strategies in improving the performance of IP solvers varies widely depending on the problem being solved, the usual approach for discovering a good set of parameters considering average results is not ideal. In this work we propose a comprehensive approach for the automatic tuning of Integer Programming solvers where the characteristics of instances are considered. Computational experiments in a diverse set of 308 benchmark instances using the open source COIN-OR CBC solver were performed with different parameter sets and the results were processed by data mining algorithms. The results were encouraging: when trained with a portion of the database the algorithms were able to predict better parameters for the remaining instances in 84% of the cases. The selection of a single best parameter setting would provide an improvement in only 56% of instances, showing that great improvements can be obtained with our approach.

关键词： feature based parameter tunning mixed-integer programming data mining regression algorithms coin-or branch cut

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pH Control at Carbonation Bays, in Rand Water's Zuikerbosch

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IFAC Proceedings Volumes 2000年第18期33卷 211-216页

作者： Sam Shorer ISA SAIMAC CSSA ICS

Raw water is channelled from the Vaal-Dam into the water treatment complex at Zuikerbosch. One of the first steps in the purification process is the injection of Calcium Hydroxide Ca(OH) 2 into the water. This starts the mechanism of coagulation of suspended solids and micro-material in the water and forms the basis for the subsequent sedimentation process, followed by chlorination and filtration processes. This introduction of Milk-Of-lime 1 and other chemicals causes a significant rise in the water's pH. It is essential that the pH of water leaving the purification plant and being pumped to consumers' reservoirs is at all times maintained at the value of 8.40 +/- 0.2 pH. CO 2 is used in the carbonation bays to reduce the pH of the water to the required level. This paper describes briefly various aspects associated with the carbonation process. It highlights the main considerations in designing a control system which automatically adjusts the CO 2 gas flow, in order to optimally maintain the pH, given high process variability and implementation constraints.

关键词： Feed-forward Non-linear control PID regression algorithms Variability Backup Control

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Estimating fractional vegetation cover of maize under water stress from UAV multispectral imagery using machine learning algorithms

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2021年 189卷 106414-106414页

作者： Niu, Yaxiao Han, Wenting Zhang, Huihui Zhang, Liyuan Chen, Haipeng Northwest A&F Univ Coll Mech & Elect Engn Yangling 712100 Shaanxi Peoples R China USDA ARS Water Management & Syst Res Unit 2150 Ctr AveBldg D Ft Collins CO 80526 USA Minist Agr Key Lab Agr Internet Things Yangling 712100 Shaanxi Peoples R China

Crop water stress is an inevitable and increasing challenge for agriculture. To improve crop water use efficiency, management of water stress and accurate estimation of crop traits were required. Among crop traits used to detect crop growth status and predict yield, fractional vegetation cover (FVC) is of great significance. We conducted studies in a maize field located in Inner Mongolia, China with different irrigation levels during 2018 and 2019 growing seasons. UAV RGB imagery was captured to investigate the effect of image sensors on thresholds obtained by the fixed-threshold method (proposed in our recent study), and to provide reference FVC (FVCUAV_R) for FVC models based on UAV multispectral imagery. Five vegetation indices (VIs), calculated from UAV multispectral imagery, and three regression algorithms (RF: random forest, ANN: artificial neural network, and MLR: multivariate linear regression) were used to build the FVC model suitable for different growing seasons, growth stages, and crop water stress. The results showed that there was a change in thresholds obtained using the fixedthreshold method based on different image sensors, but this change did not make a big difference on the accuracy of FVCUAV_R, with the R2 difference of 0.01 and the RMSE difference of 0.01. As for the three FVC regression models, RF model was the most suitable model when these models established in 2018 were used to estimate maize FVC in 2019 for different growth stages and water stress. The low estimation accuracy for high FVC levels was the reason why MLR model could not be used in the other maize growing season. This study provides a low cost and easy way to estimate maize FVC and its inter-field variability under various water status in different maize growing seasons or growth stages.

关键词： Threshold method regression algorithms UAV RGB images Model suitability FVC maps

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A Novel Method for Soil Organic Matter Determination by Using an Artificial Olfactory System

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SENSORS 2019年第15期19卷 3417-3417页

作者： Zhu, Longtu Jia, Honglei Chen, Yibing Wang, Qi Li, Mingwei Huang, Dongyan Bai, Yunlong Jilin Univ Key Lab Bion Engn Minist Educ Changchun 130022 Jilin Peoples R China Jilin Univ Coll Biol & Agr Engn Changchun 130022 Jilin Peoples R China Jilin Prov Soil & Fertilizer Stn Changchun 130031 Jilin Peoples R China Jilin Agr Univ Coll Informat Changchun 130118 Jilin Peoples R China

Soil organic matter (SOM) is a major indicator of soil fertility and nutrients. In this study, a soil organic matter measuring method based on an artificial olfactory system (AOS) was designed. An array composed of 10 identical gas sensors controlled at different temperatures was used to collect soil gases. From the response curve of each sensor, four features were extracted (maximum value, mean differential coefficient value, response area value, and the transient value at the 20th second). Then, soil organic matter regression prediction models were built based on back-propagation neural network (BPNN), support vector regression (SVR), and partial least squares regression (PLSR). The prediction performance of each model was evaluated using the coefficient of determination (R-2), root-mean-square error (RMSE), and the ratio of performance to deviation (RPD). It was found that the R-2 values between prediction (from BPNN, SVR, and PLSR) and observation were 0.880, 0.895, and 0.808. RMSEs were 14.916, 14.094, and 18.890, and RPDs were 2.837, 3.003, and 2.240, respectively. SVR had higher prediction ability than BPNN and PLSR and can be used to accurately predict organic matter contents. Thus, our findings offer brand new methods for predicting SOM.

关键词： artificial olfactory system soil organic matter gas sensor array prediction methods regression algorithms

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Operational framework to predict field level crop biomass using remote sensing and data driven models

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2022年 108卷 102725-102725页

作者： Servia, Hemant Pareeth, Sajid Michailovsky, Claire, I de Fraiture, Charlotte Karimi, Poolad IHE Delft Inst Water Educ Dept Land & Water Management Westvest 7 NL-2611 AX Delft Netherlands Wageningen Univ Water Resources Management Grp Wageningen Netherlands

Remote Sensing (RS) based monitoring provides opportunities to acquire timely and reliable information on crop growth at diverse scales. Crop yield forecasts can help decision makers to formulate policies on maintaining national food reserves, sustaining food supply chains and attaining national food security. Inter field crop yield heterogeneity arising from varied field management and agricultural practices necessitates this forecasting to be done at field-level. Such field level information can aid farmers to identify gaps in water and farm management practices and take corrective actions, if needed. So far, no scalable tools are available to predict crop yields at field level that leverage the availability of open-access high-resolution RS data. This research implements an operational framework to predict field level crop biomass by evaluating different regression algorithms to develop data driven models, leveraging historical and near real time open-access high resolution optical satellite data from Sentinel-2, radar data from Sentinel-1 and evapotranspiration (ETa) and Net Primary Production (NPP) data from Food and Agriculture Organization's (FAO) Water Productivity through Open-access Remotely sensed data (WaPOR) platform. NPP is used as a proxy for biomass production/yield. Five of the most commonly used regression algorithms were tested to build a data-driven model for sugarcane NPP prediction in Wonji-Shoa sugarcane estate, located in the Awash Basin, Ethiopia. The models tested were the Multivariate Linear regression (MLR), Stepwise Multivariate Linear regression (SMLR), Boosted regression Trees (BRT), Support Vector regression (SVR), and Random Forest regression (RFR). The results revealed that for seasonal sugarcane NPP predictions, the linear regression models (MLR and SMLR) yielded more accurate predictions than the non-linear machine learning models (BRT, SVR and RFR) tested. The highest accuracy was achieved for MLR models for which estimates with

关键词： Operational framework Crop biomass prediction Data driven models regression algorithms Remote sensing Cumulated vegetation indices Food security

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Comparative evaluation of Machine Learning algorithms and Physical based models for the prediction of Vessel Speed in real life applications. 2021

Comparative evaluation of Machine Learning algorithms and Ph...

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25th Pan-Hellenic Conference on Informatics

作者： Kiriakos Alexiou Efthimios Pariotis Theodoros Zannis Stylianos Polyzos Helen Leligou Univercity of West Attica Greece Hellenic Naval Academy Greece University of West Attica Greece

ISBN: (纸本)9781450395557

Abstract: The fourth quarter of the year 2021 might be the start of new global energy crisis. A natural gas shortage in Europe has driven prices to record highs and consequently increased the price of Diesel. The prementioned increase of the fuel prices constitutes the most important reason for an equal increase of operating costs in shipping companies. On the other hand, the huge fires that broke out in many parts of southern Europe in the summer of 2021, have made it clear to governments that stricter environmental protection measures need to be taken. The existing European Union (EU) regulation specifies a maximum Sulphur content of marine fuels from of 0.50%. Stricter environmental protection rules are expected to further reduce the profit margin in shipping. To compensate the above two adverse effects, the shipping companies have to either renew their fleet with new and more environmentally friendly ships, or try to optimize the performance of their existing fleet. The prediction of a key aspect of the performance of a ship, such us the speed that achieves with a given engine torque is a crucial tool in both designing of new vessel and optimize the performance in existing ones. In this paper, author compares the performance of a well-known physically based system simulation software (Simcenter Amesim) against Machine Learning (ML) algorithms in the prediction of an ocean-going vessel speed at various operating conditions according to a real-life sea route over a period of six days. Two different predictive ML algorithms are evaluated: Artificial Neural Network (ANN) and Vector Linear regression (VLR), since based on the literature, they are suggested as the most suitable for this type of problems. Actual data from the onboard Automated Data Logging & Monitoring (ADLM) system have been used. The study shows that both pathways (either using system simulation or ML), are adequate for vessel speed prediction with reasonable accuracy. Specifically, the simulation to

关键词： Simcenter Amesim Simulation Artificial Intelligence (AI) Artificial Neural Network (ANN) Prediction regression algorithms Data Mining Machine Learning (ML)

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