Optimizing biodiversity prediction from abiotic parameters

Tamvakis, Αndroniki; Trygonis, Vasilis; Miritzis, John; Tsirtsis, George; Spatharis, Sofie

doi:10.1016/j.envsoft.2013.12.001

Cited by 7 publications

(3 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The success of the model was probably due to the high accuracy obtained for each taxon (>0.7 with several >0.9), as a result, of the selection of the most accurate of the three techniques available for each taxon , unlike with the single modelling approach. Powerful bioassessment tools based on machine learning techniques have significantly advanced all over the world (e.g., Feio et al, 2014a, 2014b; Feio et al, 2020; Mayfield et al, 2017; Rose et al, 2016; Sarrazin‐Delay et al, 2014; Tamvakis et al, 2014). However, to our knowledge, the use of simultaneous multiple machine learning techniques for simulations of environmental improvement has not been attempted.…”

Section: Discussionmentioning

confidence: 99%

“…These approaches have the ability to model and predict species distribution in dimensional space with advantages over classical predictive modelling techniques of: not requiring a priori reference sites that can be viewed as artificial; capturing nonlinear relationships; and being less influenced by outliers (Gevrey et al, 2004; Rose, Kennard, Moffatt, Sheldon, & Butler, 2016). Thus, this may prove to be favourable for obtaining realistic predictions from complex ecological data (Mayfield, Smith, Gallagher, & Hockings, 2017; Tamvakis, Trygonis, Miritzis, Tsirtsis, & Spatharis, 2014).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rehabilitation scenarios for reservoirs: Predicting their effect on invertebrate communities through machine learning

Azevêdo

Bezerra‐Neto

Molozzi

et al. 2020

River Research & Apps

View full text Add to dashboard Cite

Reservoirs are ecosystems that provide important services and reserves of aquatic biodiversity, especially in arid and semiarid regions. However, they are also highly vulnerable to pollution, water abstraction and land use change. Here, we aimed to develop a “dirty‐water” predictive model that could be used to create recovery scenarios and analyze the effect of measures on the biological quality of reservoirs. Accordingly, we constructed a machine learning predictive model that was trained with environmental and macroinvertebrate community data from 129 sites, sampled in six reservoirs in Northeast Brazil. Two different rehabilitation scenarios were simulated (D1 = lower improvement, 25% change; and D2 = higher improvement, 75% change), and three initial levels of disturbance were considered based on PCA analyses: least disturbed, moderately disturbed and severely disturbed. The effects were analyzed in terms of changes in expected taxa (E), observed/expected taxon ratio (OE), biotic and trophic status indices and the spatial distribution of sensitive taxa. The simulations resulted in significant improvements (p < .001) for all disturbance levels and indicators analyzed. Furthermore, rehabilitation measures in artificial systems, such as reservoirs, could result in a higher biodiversity, biological quality and water quality, also in the least disturbed sites. Thus, this approach has the potential to provide information on management and conservation measures, since the simultaneous predictions of many taxa can be used, including: mapping the distribution of species; monitor the population dynamics of species at risk of extinction; and determining the best rehabilitation measures to improve disturbed ecosystems, allowing the determination of the most cost‐effective restoration measures.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rehabilitation scenarios for reservoirs: Predicting their effect on invertebrate communities through machine learning

Azevêdo

Bezerra‐Neto

Molozzi

et al. 2020

River Research & Apps

View full text Add to dashboard Cite

show abstract

“…The k -Nearest-Neighbor classifier offers a good classification accuracy rate for activity classification [ 17 ]. The kNN algorithm is based on the notion that similar instances have similar behavior and thus the new input instances are predicted according to the stored most similar neighboring instances [ 18 ].…”

Section: Related Workmentioning

confidence: 99%

A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

Yang

Cheng

Chan

2017

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir's water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

show abstract

Integrated modeling framework to evaluate the impacts of multi-source water replenishment on lacustrine phytoplankton communities

Sun

Wang

Chen

et al. 2022

Journal of Hydrology

View full text Add to dashboard Cite

Optimizing biodiversity prediction from abiotic parameters

Cited by 7 publications

References 87 publications

Rehabilitation scenarios for reservoirs: Predicting their effect on invertebrate communities through machine learning

Rehabilitation scenarios for reservoirs: Predicting their effect on invertebrate communities through machine learning

A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

Integrated modeling framework to evaluate the impacts of multi-source water replenishment on lacustrine phytoplankton communities

Contact Info

Product

Resources

About