Applications of Maximum Entropy (MaxEnt) Model in Conservation of Bird Diversity

2022 ◽  
Vol 10 (01) ◽  
pp. 20-26
Author(s):  
艳 李
Keyword(s):  
Maximum Entropy ◽  
Bird Diversity ◽  
Maxent Model

scholarly journals Leak Detection in Water Pipes Based on Maximum Entropy Version of Least Square Twin K-Class Support Vector Machine

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1247
Author(s):  
Mingyang Liu ◽  
Jin Yang ◽  
Wei Zheng
Keyword(s):  
Support Vector Machine ◽  
Maximum Entropy ◽  
Leak Detection ◽  
Least Square ◽  
Support Vector ◽  
Maxent Model ◽  
Water Pipelines ◽  
The Impact ◽  
Multi Classification ◽  
Improved Support Vector Machine

Numerous novel improved support vector machine (SVM) methods are used in leak detection of water pipelines at present. The least square twin K-class support vector machine (LST-KSVC) is a novel simple and fast multi-classification method. However, LST-KSVC has a non-negligible drawback that it assigns the same classification weights to leak samples, including outliers that affect classification, these outliers are often situated away from the main leak samples. To overcome this shortcoming, the maximum entropy (MaxEnt) version of the LST-KSVC is proposed in this paper, called the MLT-KSVC algorithm. In this classification approach, classification weights of leak samples are calculated based on the MaxEnt model. Different sample points are assigned different weights: large weights are assigned to primary leak samples and outliers are assigned small weights, hence the outliers can be ignored in the classification process. Leak recognition experiments prove that the proposed MLT-KSVC algorithm can reduce the impact of outliers on the classification process and avoid the misclassification color block drawback in linear LST-KSVC. MLT-KSVC is more accurate compared with LST-KSVC, TwinSVC, TwinKSVC, and classic Multi-SVM.

A MAXIMUM ENTROPY BASED DATA-DRIVEN 3D SCENE GENERATION

2013 ◽  
Vol 07 (01) ◽  
pp. 69-85 ◽  
Author(s):  
MESFIN A. DEMA ◽  
HAMED SARI-SARRAF
Keyword(s):  
Model Selection ◽  
Virtual Environments ◽  
Maximum Entropy ◽  
Selection Criterion ◽  
3D Models ◽  
Data Driven ◽  
Maxent Model ◽  
Model Retrieval ◽  
Training Examples ◽  
3D Scene

Due to overwhelming use of 3D models in video games and virtual environments, there is a growing interest in 3D scene generation, scene understanding and 3D model retrieval. In this paper, we introduce a data-driven 3D scene generation approach from a Maximum Entropy (MaxEnt) model selection perspective. Using this model selection criterion, new scenes can be sampled by matching a set of contextual constraints that are extracted from training and synthesized scenes. Starting from a set of randomly synthesized configurations of objects in 3D, the MaxEnt distribution is iteratively sampled and updated until the constraints between training and synthesized scenes match, indicating the generation of plausible synthesized 3D scenes. To illustrate the proposed methodology, we use 3D training desk scenes that are composed of seven predefined objects with different position, scale and orientation arrangements. After applying the MaxEnt framework, the synthesized scenes show that the proposed strategy can generate reasonably similar scenes to the training examples without any human supervision during sampling.

Estimating Earthquake Probabilities by Jaynes’s Method of Maximum Entropy

2021 ◽  
Author(s):  
Fred L. Bookstein
Keyword(s):  
New Zealand ◽  
Maximum Entropy ◽  
Epistemic Uncertainty ◽  
Passage Time ◽  
Time Dependent ◽  
Earthquake Hazards ◽  
Maxent Model ◽  
Alpine Fault ◽  
Scientific Disciplines ◽  
Recurrence Intervals

ABSTRACT An important midcentury statistical method, already applied in several other scientific disciplines, enables epistemic ignorance to be incorporated more fully in multidecadal earthquake forecasts. Codified by the physicist E. T. Jaynes in the 1950s, the method of maximum entropy (MaxEnt) is suited for settings of great epistemic uncertainty. Shortly after its initial formulation it was being applied by industrial engineers to rates of failure and expected lifetimes—engineering problems analogous to fault ruptures and their recurrence intervals. Here, I show how MaxEnt can improve upon previous estimates of time-dependent conditional probabilities in Cascadia, California, and New Zealand. The method formalizes probabilistic inferences from long geology-based earthquake histories as truncated-Gaussian curves of time-dependent hazard that match the observed mean and mean square of the historical recurrence data. For each example, rupture forecasts by Brownian passage time, lognormal, Weibull, or Poisson methods have been published previously. In the Cascadia example, the MaxEnt estimate for a 50 yr exposure, 8.3%, compares with previous estimates of up to 14% for passage time or lognormal models. For other datasets drawn from the third Uniform California Earthquake Rupture Forecast study of California earthquakes and from the southern Alpine fault of New Zealand, MaxEnt recurrence probabilities similarly differ by up to a factor of nearly 2.0 from estimates published earlier. The lognormal approach is a variant of the MaxEnt model in which the estimate’s constraints concern the logarithms of the observed recurrence intervals; there is a continuum of methods in between. The discrepancies between MaxEnt and these other probability claims are not trivial; they arise from assumptions built into other methods that do not correspond to the actual information content of the data at hand. Discrepancies like these are directly relevant to the communication of these aspects of earthquake hazards to stakeholders concerned with the consequent risks.

scholarly journals Application of a Maximum Entropy Model for Mineral Prospectivity Maps

Minerals ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 556 ◽  
Author(s):  
Binbin Li ◽  
Bingli Liu ◽  
Ke Guo ◽  
Cheng Li ◽  
Bin Wang
Keyword(s):  
Maximum Entropy ◽  
Controlling Factors ◽  
Controlling Factor ◽  
Geochemical Data ◽  
Potential Zone ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Copper Deposits ◽  
Maxent Model ◽  
Mineral Prospectivity

The effective integration of geochemical data with multisource geoscience data is a necessary condition for mapping mineral prospects. In the present study, based on the maximum entropy principle, a maximum entropy model (MaxEnt model) was established to predict the potential distribution of copper deposits by integrating 43 ore-controlling factors from geological, geochemical and geophysical data. The MaxEnt model was used to screen the ore-controlling factors, and eight ore-controlling factors (i.e., stratigraphic combination entropy, structural iso-density, Cu, Hg, Li, La, U, Na2O) were selected to establish the MaxEnt model to determine the highest potential zone of copper deposits. The spatial correlation between each ore-controlling factor and the occurrence of a copper mine was studied using a response curve, and the relative importance of each ore-controlling factor was determined by jackknife analysis in the MaxEnt model. The results show that the occurrence of copper ore is positively correlated with the content of Cu, Hg, La, structural iso-density and stratigraphic combination entropy, and negatively correlated with the content of Na2O, Li and U. The model’s performance was evaluated by the area under the receiver operating characteristic curve (AUC), Cohen’s maximized Kappa and true skill statistic (TSS) (training AUC = 0.84, test AUC = 0.8, maximum Kappa = 0.5 and maximum TSS = 0.6). The results indicate that the model can effectively integrate multi-source geospatial data to map mineral prospectivity.

scholarly journals Climate change impact on the distribution of Tossa jute using maximum entropy and educational global climate modelling

2021 ◽  
pp. 1-11
Author(s):  
Tanjinul Hoque Mollah ◽  
Sharmin Shishir ◽  
Momotaz ◽  
Md. Shahedur Rashid
Keyword(s):  
Climate Change ◽  
Maximum Entropy ◽  
Global Climate ◽  
Global Climate Model ◽  
Suitable Habitat ◽  
Climate Modelling ◽  
Important Species ◽  
Maxent Model ◽  
Threatened And Endangered Species ◽  
The Impact

Abstract Tossa (Corchorus olitorius L.) is a significant cash crop, cultivated commercially in the lower flood plain of Bangladesh. The climatic regimes in Bangladesh are changing as well as the world does. However, this species is threatened by climate change. Occurrences of data on threatened and endangered species are frequently sparse which makes it difficult to analyse the species suitable habitat distribution using various modelling approaches. The current paper used maximum entropy (Maxent) and educational global climate model (EdGCM) modelling to predict and conserve the suitable habitat distributions for Tossa species in Bangladesh to the year 2100. Nine environmental variables, 239 occurrence data and two Representative Concentration Pathway scenarios (RCP4.5 and RCP8.5) were used for the Maxent modelling to project the impact of climate change on the Tossa distributions. Furthermore, the EdGCM was used to study the climatic space suitability for the Tossa species in the context of Bangladesh. Both of the climatic scenarios were used for the prediction to the year 2100. The Maxent model performed better than random for the Tossa species with a high AUC value of 0.86. Under the RCP scenarios, the Maxent model predicted habitat reduction for RCP4.5 is 2%, RCP8.5 is 9% and EdGCM is 10.2% from the current localities. The predictive modelling approach presented here is promising and can be applied to other important species for conservation planning, monitoring and management, especially those under the threat of extinction due to climate change.

scholarly journals Spatial-temporal habitat suitability for lemuru fish (Sardinella lemuru) using the Second-generation Global Imager (SGLI) and Maximum Entropy model in the Bali Strait, Indonesia

2021 ◽  
Vol 944 (1) ◽  
pp. 012066
Author(s):  
N Gustantia ◽  
T Osawa ◽  
I W S Adnyana ◽  
D Novianto ◽  
Chonnaniyah
Keyword(s):  
Maximum Entropy ◽  
Habitat Suitability ◽  
Second Generation ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Essential Information ◽  
Fishery Resource ◽  
Chl A ◽  
Maxent Model ◽  
Temporal Prediction

Abstract Lemuru fish (Sardinella lemuru), the most dominant fishery resource, has economic values for the fisherman fishing activities in the Bali Strait (between Jawa and Bali islands), Indonesia. Spatial and temporal prediction for the fishing location is essential information for effective fisheries management. The high spatial resolution of sea surface temperature (SST) and Chlorophyll-a (Chl-a) by the second-generation global imager (SGLI) on the global change observation mission (GCOM-C) satellite was employed for the input of the Maximum Entropy Model (MaxEnt) to predict the potential fishing area of lemuru fish in 2020. This study analyzed SST and Chl-a using the SGLI data and shows the variability of SST and Chl-a for lemuru fish-catching data. The MaxEnt model performance to predict the habitat suitability for lemuru fish in the Bali Strait has been shown in this study. As a result, the maximum average Chl-a estimated in August 2020 was around 1.62 mg m−3 and maximum SST in March 2020 around 28.12°C. The correlation between SST and Chl-a with total lemuru fish-catching were -0.209 and 0.375 for SST and Chl-a, respectively. The prediction of lemuru fishing areas using the MaxEnt model showed excellent model evaluations with a correlation value higher than 0.80.

scholarly journals Modelling Climate Suitability for Rainfed Maize Cultivation in Kenya Using a Maximum Entropy (MaxENT) Approach

Agronomy ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 727 ◽  
Author(s):  
Benjamin Kipkemboi Kogo ◽  
Lalit Kumar ◽  
Richard Koech ◽  
Champika S. Kariyawasam
Keyword(s):  
Maximum Entropy ◽  
General Circulation ◽  
General Circulation Models ◽  
Mitigation Strategies ◽  
Maize Production ◽  
Maxent Model ◽  
Mean Temperature ◽  
Bioclimatic Variables ◽  
Climatic Scenarios ◽  
The Mean

Climate change and variability are projected to alter the geographic suitability of lands for crop cultivation. In many developing countries, such as Kenya, information on the mean changes in climate is limited. Therefore, in this study, we model the current and future changes in areas suitable for rainfed maize production in the country using a maximum entropy (MaxENT) model. Maize is by far a major staple food crop in Kenya. We used maize occurrence location data and bioclimatic variables for two climatic scenarios-Representative Concentration Pathways (RCP) 4.5 and 8.5 from two general circulation models (HadGEM2-ES and CCSM4) for 2070. The study identified the annual mean temperature, annual precipitation and the mean temperature of the wettest quarter as the major variables that affect the distribution of maize. Simulation results indicate an average increase of unsuitable areas of between 1.9–3.9% and a decrease of moderately suitable areas of 14.6–17.5%. The change in the suitable areas is an increase of between 17–20% and in highly suitable areas of 9.6% under the climatic scenarios. The findings of this study are of utmost importance to the country as they present an opportunity for policy makers to develop appropriate adaptation and mitigation strategies required to sustain maize production under future climates.

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