Ecophysiological modeling of grapevine water stress in Burgundy terroirs by a machine-learning approach.

13 pagesInternational audienceIn a climate change scenario, successful modeling of the relationships between plant-soil-meteorology is crucial for a sustainable agricultural production, especially for perennial crops. Grapevines (Vitis vinifera L. cv Chardonnay) located in eight experimental plots (Burgundy, France) along a hillslope were monitored weekly for 3 years for leaf water potentials, both at predawn (Ψpd) and at midday (Ψstem). The water stress experienced by grapevine was modeled as a function of meteorological data (minimum and maximum temperature, rainfall) and soil characteristics (soil texture, gravel content, slope) by a gradient boosting machine. Model performance was assessed by comparison with carbon isotope discrimination (δ13C) of grape sugars at harvest and by the use of a test-set. The developed models reached outstanding prediction performance (RMSE < 0.08 MPa for Ψstem and < 0.06 MPa for Ψpd), comparable to measurement accuracy. Model predictions at a daily time step improved correlation with δ13C data, respect to the observed trend at a weekly time scale. The role of each predictor in these models was described in order to understand how temperature, rainfall, soil texture, gravel content and slope affect the grapevine water status in the studied context. This work proposes a straight-forward strategy to simulate plant water stress in field condition, at a local scale; to investigate ecological relationships in the vineyard and adapt cultural practices to future conditions

Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence. These techniques pretend to generate new business knowledge transforming sets of "raw data" into business value. One of the principal applications of these techniques is related to the design of predictive analytics for the improvement of advanced CBM (condition-based maintenance) strategies and energy production forecasting. These advanced techniques can be used to transform control system data, operational data and maintenance event data to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation. One of the systems where these techniques can be applied with massive potential impact are the legacy monitoring systems existing in solar PV energy generation plants. These systems produce a great amount of data over time, while at the same time they demand an important e ort in order to increase their performance through the use of more accurate predictive analytics to reduce production losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of the problems to address. This paper presents a review and a comparative analysis of six intelligent optimization modelling techniques, which have been applied on a PV plant case study, using the energy production forecast as the decision variable. The methodology proposed not only pretends to elicit the most accurate solution but also validates the results, in comparison with the di erent outputs for the di erent techniques

Optimised Big Data analytics for health and safety hazards prediction in power infrastructure operations

© 2020 Elsevier Ltd Forecasting imminent accidents in power infrastructure projects require a robust and accurate prediction model to trigger a proactive strategy for risk mitigation. Unfortunately, getting ready-made machine learning algorithms to eliminate redundant features optimally is challenging, especially if the parameters of these algorithms are not tuned. In this study, a particle swarm optimization is proposed both for feature selection and parameters tuning of the gradient boosting machine technique on 1,349,239 data points of an incident dataset. The predictive ability of the proposed method compared to conventional tree-based methods revealed near-perfect predictions of the proposed model on test data (classification accuracy − 0.878 and coefficient of determination − 0.93) for the two outcome variables ACCIDENT and INJURYFREQ. The high predictive power obtained reveals that injuries do not occur in a chaotic fashion, but that underlying patterns and trends exist that can be uncovered and captured via machine learning when applied to sufficiently large datasets. Also, key relationships identified will assist safety managers to understand possible risk combinations that cause accidents; helping to trigger proactive risk mitigation plans

Grapes and Wine

Grape and Wine is a collective book composed of 18 chapters that address different issues related to the technological and biotechnological management of vineyards and winemaking. It focuses on recent advances, hot topics and recurrent problems in the wine industry and aims to be helpful for the wine sector. Topics covered include pest control, pesticide management, the use of innovative technologies and biotechnologies such as non-thermal processes, gene editing and use of non-Saccharomyces, the management of instabilities such as protein haze and off-flavors such as light struck or TCAs, the use of big data technologies, and many other key concepts that make this book a powerful reference in grape and wine production. The chapters have been written by experts from universities and research centers of 9 countries, thus representing knowledge, research and know-how of many regions worldwide

Effects of abiotic factors on ecosystem health of Taihu Lake, China based on eco-exergy theory

A lake ecosystem is continuously exposed to environmental stressors with non-linear interrelationships between abiotic factors and aquatic organisms. Ecosystem health depicts the capacity of system to respond to external perturbations and still maintain structure and function. In this study, we explored the effects of abiotic factors on ecosystem health of Taihu Lake in 2013, China from a system-level perspective. Spatiotemporal heterogeneities of eco-exergy and specific eco-exergy served as thermodynamic indicators to represent ecosystem health in the lake. The results showed the plankton community appeared more energetic in May, and relatively healthy in Gonghu Bay with both higher eco-exergy and specific eco-exergy; a eutrophic state was likely discovered in Zhushan Bay with higher eco-exergy but lower specific eco-exergy. Gradient Boosting Machine (GBM) approach was used to explain the non-linear relationships between two indicators and abiotic factors. This analysis revealed water temperature, inorganic nutrients, and total suspended solids greatly contributed to the two indicators that increased. However, pH rise driven by inorganic carbon played an important role in undermining ecosystem health, particularly when pH was higher than 8.2. This implies that climate change with rising CO 2 concentrations has the potential to aggravate eutrophication in Taihu Lake where high nutrient loads are maintained

Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems

The implementation of artificial intelligence (AI), together with robotics, sensors, sensor networks, Internet of Things (IoT), and machine/deep learning modeling, has reached the forefront of research activities, moving towards the goal of increasing the efficiency in a multitude of applications and purposes related to environmental sciences. The development and deployment of AI tools requires specific considerations, approaches, and methodologies for their effective and accurate applications. This Special Issue focused on the applications of AI to environmental systems related to hazard assessment in urban, agriculture, and forestry areas

機械学習技術の視点による通勤交通手段選択と自動車所有に関する研究

過去数十年の間に，世界は急速な都市化プロセスを経験し，人々の生活には自動車が急速に普及した。モータリゼーションは我々に経済発展の機会を与えると同時に，生活の質に影響を与える地球環境に負荷をかけている。都市の自己増殖は，自家用車の所有と使用の増加を引き起こす主要な理由である。旅行モードの選択，車両所有パターン，およびそれらの決定要因に対して影響力のあるメカニズムを理解することは，土地利用と交通計画上の政策決定に大いに役立つ。この課題は，グローバリゼーションの時代に持続可能な交通の発展を目指す途上国において，大いに注目されている。本研究では，多項ロジットモデル，ニューラルネットワーク，ランダムフォレストを用いて，プノンペン市における将来のインパクトレベルと車両所有パターンの予測を行った。交通手段選択に関して，本研究では，勾配ブースティングマシン，機械学習アルゴリズム，およびLIMEを適用して，インドネシアのジャカルタ市の大都市圏における複数交通手段によるトリップパターンとその決定要因を推定した。両方の分析は，国際協力機構（JICA）から提供された世帯インタビュー調査データを使用した。分析結果は，家計収入がプノンペンのモータリゼーションに影響を与える最も強力な変数であることを示した。合計旅行回数などの個々の旅行特性の補足，通勤目的で行われた移動回数と全体の移動距離は全て，分類子として効果的に作用した。ジャカルタ市におけるケーススタディにおいては，単一交通機関の旅行に影響を与える要因として旅行費用や移動時間といった限られた変数が選ばれる一方で，複数交通機関の旅行については幅広い変数の影響を受けていることが示された。さらに，機械学習アプローチによる予測においては，精度を予測するという点だけでなく，統計的アプローチと比較して不均衡なカテゴリを処理するという点でも優れていたことが認められた。特に，グラディエントブースティングマシンは，ビッグデータで課題を解決する際，優れた潜在能力があることが示された。これら二つの結果は，旅行行動分析の分野に関して機械学習技術を適用する優位性を示し，他の分析に関しても，機械学習技術が応用できる可能性を示唆している。In the last decades, the world has seen the rapid urbanization process with the boom of motorized vehicles. The motorization, on one hand, gives opportunities for economic development and on the other hand, it puts pressure on the environment that affects the quality of life. The self-proliferating of the city is identified as a major that causes the rise of private vehicle ownership and usage. Understanding the influential mechanism of the travel mode choice, vehicle ownership patterns, and their determinants will greatly help policymaking for land use and transportation. This issue has been paid even greater attention in developing countries that aspire to reach sustainable transportation development goals in the era of globalization.In this study, the Multinomial logit model, Neural Networks and Random Forest were applied to examine the features’ impact level and to also predict vehicle ownership patterns in Phnom Penh city. Regarding travel mode choice, this study introduces the application of Gradient Boosting Machine, a Machine Learning algorithm, and Local Interpretable Model-agnostic Explanations technique to investigate the multi-mode trip pattern and its determinants in the metropolitan area of Jakarta city, Indonesia. Both analyses used the household interview survey data provided by the Japan International Cooperation Agency (JICA). The results indicate that household income is the most powerful variable affecting motorization in Phnom Penh. Supplementation of individual trip characteristics such as total number of trips made, number of trips made for work purposes and overall travel distance all make effective contributions as classifiers. The results from the case study of Jakarta city show that there was a limit of features (travel cost, time, etc.) that affected the single-mode trip while the multi-mode travel was influenced by the wide range of variables. Furthermore, it is acknowledged that the machine-learning approach outperformed not only in terms of predicting accuracy but also in dealing with unbalanced categories when compared with the statistical approach. Especially, the Gradient Boosting Machine indicated the impressive potentiality in solving the subject with big data. This detection supplies the advantages of applying machine learning techniques in terms of, but not limited to, the field of travel behavior.室蘭工業大学 (Muroran Institute of Technology)博士（工学

Estimating The Spatial Distribution of Snow Water Equivalent Using in situ and Remote Sensing Observations

Mountain snowpack is one of the primary surface water sources for about one-sixth of the global population. More than 75% of the total runoff originates from mountain snowpacks in the Western U.S. Snowmelt water recharges reservoirs and aquifers gradually in the melting season, providing vital water supplies for urban and agricultural areas. Therefore, accurately monitoring the spatial and temporal distribution of mountain snowpack &ndash; often measured as snow water equivalent (SWE) &ndash; is crucial for effective water management. While existing SWE estimation approaches remain highly uncertain, particularly when applied over large mountainous regions, the remotely-sensed snow data provide new opportunities to better characterize the spatial distributions of mountain snowpack. This dissertation investigates the approaches that optimally blend satellite, airborne, and ground snow observations to improve (near) real-time SWE estimation over mountainous terrain. The second chapter of this dissertation evaluates the accuracy of existing SWE estimation models in Sierra Nevada California. Five large-scale SWE datasets at fine spatial resolutions (&lt;= 1000 m) are comprehensively validated and compared with the Airborne Snow Observatory (ASO) SWE data in the Tuolumne River Basin (2013-2017), and ground snow pillow and snow course SWE observations across the Sierra Nevada (2004-2014). These SWE datasets include REC-INT, REC-ParBal, a Sierra Nevada SWE reanalysis (REC-DA), and two operational SWE datasets from the Snow Data Assimilation System (SNODAS) and the National Water Model (NWM-SWE), respectively. The results show that the REC-DA overall provides the most accurate SWE estimates across the Sierra Nevada (R2 = 0.87, MAE = 66 mm, PBIAS = 8.3%), followed by the REC-ParBal (R2 = 0.73, MAE = 83 mm, PBIAS = -6.4%), which is the least biased SWE estimates. Generally, SNODAS (R2 = 0.66, MAE = 106 mm, PBIAS = 9.3%) and REC-INT (R2 = 0.61, MAE = 131 mm, PBIAS = -28.3%) exhibit comparable but lower accuracy than the earlier mentioned two datasets, while NWM-SWE (R2 = 0.49, MAE = 142 mm, PBIAS = -25.2%) shows the least accuracy among the five SWE datasets. Given that REC-DA is not applicable in real-time, in the third chapter, a SWE data-fusion framework is developed, which integrates the historical SWE patterns derived from REC-DA into a statistically-based linear regression model (LRM) to estimate SWE in real-time. To investigate the influence of satellite-observed daily mean fractional snow-covered area (DMFSCA) on SWE estimation accuracy, two LRMs are compared: a baseline regression model (LRM-baseline) in which physiographic data and historical SWE patterns are used as independent variables, and an FSCA-informed regression model (LRM-FSCA) in which the DMFSCA from MODIS satellite imagery is included as an additional independent variable. By incorporating DMFSCA, LRM-FSCA outperforms LRM-baseline with improved R2 from 0.54 to 0.60, and reduced PBIAS from 2.6% to 2.2% in snow pillow cross-validation. The improvement in LRM-FSCA&rsquo;s performance is more significant during snow accumulation periods than during the snowmelt seasons. Compared to the ASO SWE, the LRM-FSCA explains 85% of the variance on average, which is at least 21% higher than the operational SNODAS (R2 = 0.64) and NWM-SWE (R2 = 0.33) in comparison. In chapter 4, a SWE bias correction framework (SWE-BCF) is developed that incorporates the ASO SWE and machine learning (ML) algorithms to further improve LRM SWE estimates in real-time. The performance of a wide range of commonly used machine learning algorithms is examined in the SWE-BCF including Gaussian Process Regression (GPR), Support Vector Machine (SVM), Bayesian Regularized Neural Networks (BRNN), Random Forest (RF), and Gradient Boosting Machine (GBM). The results indicate that all ML algorithms are capable of improving LRM-SWE accuracy substantially. While no single model performs significantly better than others, GPR, overall, shows the best performance with a 20% (0.14) increase in mean R2 value, a 31% (51 mm) reduction in mean RMSE, and a 61% (18.0%) reduction in absolute PBIAS compared with the original LRM using ASO SWE data for model validation. RF shows the most robust and stable performance in SWE bias correction with a 10% (0.08) increase in median R2 and a 41% (50 mm) reduction in median RMSE compared with the original LRM.</p