ESTIMATING THE DIAMETER OF TREE USING THE NEURO-FUZZY INFERENCE SYSTEM AND ARTIFICIAL NEURAL NETWORKS FROM THE TOTAL HEIGHT VARIABLE

Studies that seek to identify potential techniques for obtaining diameter values at 1.30 m from the ground from tree height data are necessary, especially when considering the use of airborne Lidar in forest inventory activity. In this sense, this work aimed to evaluate two artificial intelligence tools for this purpose, namely the neuro-fuzzy inference systems and the artificial neural networks. Four models were tested to obtain estimates for the diameter variable, which were prepared by combining the independent variables useful area per plant, age and height. After processing, the statistics of bias, square root of the mean squared error in percentage, correlation and mean percentage error were calculated, in addition to the preparation of scatter plots and histogram of residues. It was observed that, for the estimation of the diameter in both techniques, the use of the model with all independent variables obtained the best values for the analysis statistics. It can be concluded that both tools can be used to estimate the diameter, with the neuro-fuzzy inference system being more suitable for its processing speed and small variability between the values obtained in different training sessions for the same database

PROGNOSE DA PRODUÇÃO FLORESTAL UTILIZANDO SISTEMA NEURO-FUZZY E RANDOM FOREST

O objetivo deste estudo foi avaliar o emprego das técnicas Random Forest (RF) e Sistema Neuro-Fuzzy (SNF) na prognose da produção florestal. Os dados utilizados foram provenientes de inventários florestais contínuos conduzidos em povoamentos de clones de eucalipto, localizados no sul da Bahia. O processamento dos dados foi realizado no software Matlab R2016a. Os dados foram divididos em 70% para de treinamento e 30% para validação. Os algoritmos usados para geração de regras no SNF foram Subtractive Clustering (SC) e Fuzzy-C-Means (FCM). O treinamento foi feito com o algoritmo híbrido (gradiente descente e mínimos quadrados) com o número de épocas variando de 1 a 20. As funções de pertinências associadas às variáveis de entradas foram do tipo gaussianas e a função linear na de saída. Foram treinadas várias RF variando o número de árvores de 50 a 850 e o número de observações por folhas de 5 a 35. A modelagem da produção florestal de povoamentos clonais de eucalipto pode ser realizada com SNF e RF. Os algoritmos SC e FCM fornecem estimativas acuradas na projeção de área basal e volume. A RF apresentou estatísticas inferiores em relação a SNF para prognose da produção florestal. Ambas as técnicas são boas alternativas para seleção de variáveis empregadas na modelagem da produção florestal. Palavras-chave: Inteligência artificial, ensemble learning, mensuração florestal

Recursive diameter prediction and volume calculation of eucalyptus trees using Multilayer Perceptron Networks

a b s t r a c t A major challenge in forest management is the ability to quickly and accurately predict bole volume of standing trees. This study presents a new model that uses Multilayer Perceptron (MLP) artificial neural networks for predicting tree diameters values. The model requires three diameter measures at the base of the tree, and recursively predicts other diameter measures. The predicted diameters allow for calculating tree volume using the Smalian method. The performance of the proposed model was satisfactory when compared with data obtained from tree scaling and volume equations

Experimental investigation and modelling of the heating value and elemental composition of biomass through artificial intelligence

Abstract: Knowledge advancement in artificial intelligence and blockchain technologies provides new potential predictive reliability for biomass energy value chain. However, for the prediction approach against experimental methodology, the prediction accuracy is expected to be high in order to develop a high fidelity and robust software which can serve as a tool in the decision making process. The global standards related to classification methods and energetic properties of biomass are still evolving given different observation and results which have been reported in the literature. Apart from these, there is a need for a holistic understanding of the effect of particle sizes and geospatial factors on the physicochemical properties of biomass to increase the uptake of bioenergy. Therefore, this research carried out an experimental investigation of some selected bioresources and also develops high-fidelity models built on artificial intelligence capability to accurately classify the biomass feedstocks, predict the main elemental composition (Carbon, Hydrogen, and Oxygen) on dry basis and the Heating value in (MJ/kg) of biomass...Ph.D. (Mechanical Engineering Science

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

Epidemiology and control strategies applied to ash dieback and chestnut ink disease

Main goal of forest diseases’ management is to reduce economic, biological and aesthetic damages and biodiversity loss caused by plant parasites. The many strategies used can be grouped under two main actions, prevention (prophylaxis in some early writings) and therapy (treatment or cure). Prevention is limited primarily by the lack of knowledge of the organisms involved, including host plants. Mathematical models have been used to extend the understanding of plant disease epidemiology on a number of fronts, providing an opportunity for a more rational use of resources on expensive field trials and representing a step towards more sustainable control measures. From a curative point of view, current efforts by scientists have focused on developing diseases management (Pest Management = PM) concepts in order to balance the benefits of pesticides with the ecological concerns of their residues contaminating the environment. In this thesis, the two PM principles were applied from an innovative point of view on two case studies: ash dieback caused by Hymenoscyphus fraxineus, which can be considered the most serious disease for Fraxinus genus in Europe, and chestnut ink disease, caused by Phytophthora cambivora and P. cinnamomi. In the first part of the thesis, the two diseases are introduced, in order to permit the evaluation of similarities and differences (chapter I). Subsequently, from chapter II to chapter V, the experimental trials performed are described. In particular, in chapter II a study of the ecological niche of H. fraxineus, with the characterization of the environmental variables associated with naturally infected zones, is reported. This procedure was realized with Species Distribution Models (SDM), widely utilized in the ecological field and only recently applied to plant pathology. The presence of the pathogen was highly correlated to three summer predictors: abundant precipitation, high soil moisture and low air temperature, in comparison with the averages of the study area. The ensemble forecasting technique was then applied to obtain a prediction of the potential distribution of the pathogen at European scale, considering the distribution maps of Fraxinus excelsior and Fraxinus angustifolia, susceptible to the parasite. At last, an innovative method of network analysis permitted to identify the suitable areas that are not reachable by the pathogen with a natural spread. Chapter III reports a study conducted to evaluate six fungicides for their potential to control ash dieback. Initially, in vitro tests of the active ingredients against five different strains of the pathogen indicated thiabendazole, propiconazole and allicin as the most effective fungicides, with lower median lethal doses than procloraz. In contrast, copper sulphate and potassium phosphite were totally ineffective. Subsequently, the antifungal activities of the best three compounds were investigated in planta against H. fraxineus by trunk injection on European ashes inoculated with an indigenous strain. The test was preceded by preliminary trials to maximize the efficacy of injections; in the experimental conditions highest speed was reached with the addition of 1.2 % acetic acid to the aqueous solution and making treatments in early morning or late afternoon. Considering the results of in planta trial, thiabendazole and allicin significantly slowed down the growth of the necroses in the growing season, in contrast propiconazole injections were impracticable. The studies in chapters IV and V recall the methodologies applied to ash dieback, with application to chestnut ink disease complex. In particular, in chapter IV fuzzy logic theory was applied considering the environmental variables, such as minimum winter temperature, summer drought, slope's aspect, streams' distance and soil's permeability, that mainly can influence the development of the disease. The model was validated with a broad field survey conducted in a chestnut area in Treviso province. Moreover, uncertainty maps (regarding model structure, inputs and parameters) were produced for the correct interpretation of the prediction. Great part of the chestnut area in the study zone resulted as suitable for the development of ink disease, whereas only the 18.8 %, corresponding to higher elevation zones, presented inferior risks. In a second study (chapter V), a comparative efficacy trial on four potassium phosphite formulations by means of endotherapy against chestnut ink disease is performed. P. cinnamomi was isolated with baiting technique from symptomatic chestnuts and was inoculated on 50 asymptomatic trees. As a result of endotherapic treatments, the unique solution that significantly slowed down necroses' growth was potassium phosphite (35 %) with an addition of 0.1 % micronutrient solution. An additional endotherapic trial was conducted in a preliminary way in the chestnut where P. cinnamomi was isolated, with the main aim to evaluate growth stimulation of active growing callus next to the shape flame necroses by the injected solution of potassium phosphite 70 %. In this case, results did not highlight a significant difference between treated trees and water control ones, probably for the need of longer times for older trees. On the base of the achieved results, epidemiological modelling and endotherapic treatments, applied both to ash dieback and chestnut ink disease, can represent fundamental tools in the management of these important diseases and should be applied in an Integrated Pest Management (IPM) approach, together with appropriate cultural techniques to maximize benefits

Continuous adsorption studies of pharmaceuticals in multicomponent mixtures by agroforestry biochar

In this study, the adsorption of a multicomponent mixture of active pharmaceutical compounds, such as Venlafaxine (VLX), Trazodone (TRZ) and Fluoxetine (FLX), was studied in a biochar fixed-bed column. The selection of appropriate biochar (eucalyptus, grapevine cane and holm tree biochar) as an adsorbent was carried out through batch assays. An insight into the adsorption mechanism and its correlation with the chosen biochars was performed, showing that electron donor/acceptor interaction is the main mechanism involved. Equilibrium and kinetic batch adsorption experiments were performed and the results demonstrated that eucalyptus biochar was the most viable option for the removal of the pollutants, individually and combined. Column adsorption experiments were performed and Thomas, Yoon-Nelson and Yan models were adjusted to the breakthrough curves. This multicomponent system exhibited a synergetic behavior for TRZ and an antagonist for VLX and FLX, when compared to the single and multicomponent systems previously evaluated in batch assays. The treatment of real wastewaters, spiked with pollutants, has demonstrated the removal efficiency of multicomponent mixtures. Finally, the adsorbent regeneration by elution in different solutions was also investigated and methanol proved to be the most effective eluent for the column regenerationThis work has been finantially supported by the project CTM2017-87326-R funded by MCIN/ AEI/10.13039/501100011033/ FEDER "Una manera de hacer Europa", project ED431C 2021/43 funded by Xunta de Galicia and ERDF, and ERA-NET Cofund WaterWorks2015 Call funded by the EU and FCT/UEFISCDE/FORMAS through the REWATER International Research project. This work was also supported by UIDB/50006/2020 and UIDP/50006/2020 by the Fundação para a Ciência e a Tecnologia (FCT Portugal)/Ministério da Ciência, Tecnologia e Ensino Superior (MCTES Portugal) through national funds. Manuela M. Moreira (project CEECIND/02702/2017) also acknowledge for her financial support financed by national funds through FCT and to REQUIMTE/LAQV. Funding for open access charge: Universidade de Vigo/CISUGinfo:eu-repo/semantics/publishedVersio

FORECASTING CLIMATE AND LAND USE CHANGE IMPACTS ON ECOSYSTEM SERVICES IN HAWAIʻI THROUGH INTEGRATION OF HYDROLOGICAL AND PARTICIPATORY MODELS

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2018

Soil Erosion and Sustainable Land Management (SLM)

This Special Issue titled “Soil Erosion and Sustainable Land Management” presents 13 chapters organized into four main parts. The first part deals with assessment of soil erosion that covers historical sediment dating to understand past environmental impacts due to tillage; laboratory simulation to clarify the effect of soil surface microtopography; integrated field observation and the random forest machine learning algorithm to assess watershed-scale soil erosion assessment; and developing the sediment delivery distributed (SEDD) model for sub-watershed erosion risk prioritization. In Part II, the factors controlling soil erosion and vegetation degradation as influenced by topographic positions and climatic regions; long-term land use change; and improper implementation of land management measures are well dealt with. Part III presents different land management technologies that could reduce soil erosion at various spatial scales; improve land productivity of marginal lands with soil microbes; and reclaim degraded farmland using dredged reservoir sediments. The final part relates livelihood diversification to climate vulnerability as well as the coping strategy to the adverse impacts of soil erosion through sustainable land management implementation which opens prospects for policy formulation. The studies cover regions of Africa, Europe, North America and Asia, being dominantly conducted under the framework of international scientific collaborations through employing a range techniques and scales, from the laboratory to watershed scales. We believe those unique features of the book could attract the interest of the wider scientific community worldwide

The Effect of Hydrology on Soil Erosion

This Special Issue includes manuscripts about soil erosion and degradation processes and the accelerated rates due to hydrological processes and climate change. The new research included in this issue focuses on measurements, modeling, and experiments in field or laboratory conditions developed at different scales (pedon, hillslope, and catchment). This Special Issue received investigations from different parts of the world such as Ethiopia, Morocco, China, Iran, Italy, Portugal, Greece, and Spain, among others. We are happy to see that all papers presented findings characterized as unconventional, provocative, innovative, and methodologically new. We hope that the readers of the journal Water can enjoy and learn about hydrology and soil erosion using the published material, and share the results with the scientific community, policymakers, and stakeholders to continue this amazing adventure, facing plenty of issues and challenges