Data-Driven Fuzzy Habitat Models: Impact of Performance Criteria and Opportunities for Ecohydraulics

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Abroon Qazi

Keyword(s):

Hybrid Approach ◽

Bayesian Belief Networks ◽

Project Performance ◽

Performance Criteria ◽

Data Driven ◽

Ann Model ◽

Content Type ◽

Performance Space ◽

Project Complexity ◽

And Performance

PurposeThe purpose of this paper is to propose a data-driven scheme for identifying critical project complexity dimensions and establishing the trade-off across multiple project performance criteria.Design/methodology/approachThis paper adopts a hybrid approach using Bayesian Belief Networks (BBNs) and Artificial Neural Networks (ANNs). The output of the ANN model is used as input to the BBN model for prioritizing project complexity dimensions relative to multiple project performance criteria. The proposed process is demonstrated through a real application in the construction industry.FindingsWith a number of nonlinear interactions involved within and across project complexity and performance, it is not feasible to model and assess the strength of these interactions using conventional techniques. The proposed process helps in effectively mapping a “multidimensional complexity” space to a “multidimensional performance” space and makes use of data from past projects for operationalizing this mapping scheme by means of ANNs. This obviates the need for developing a parametric model that is both challenging and computationally cumbersome. The mapping function can be used for generating all possible scenarios required for the development of a data-driven BBN model.Originality/valueThis paper introduces a data-driven process for operationalizing the mapping of project complexity to project performance within a network setting of interacting complexity dimensions and performance criteria. The results of the application study manifest the importance of capturing the interdependency across project complexity and performance. Ignoring the underlying interdependencies and relying exclusively on conventional correlation-based techniques may lead to making suboptimal decisions.

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Areal Precipitation Coverage Ratio for Enhanced AI Modelling of Monthly Runoff: A New Satellite Data-Driven Scheme for Semi-Arid Mountainous Climate

Remote Sensing ◽

10.3390/rs14020270 ◽

2022 ◽

Vol 14 (2) ◽

pp. 270

Author(s):

Seyyed Hasan Hosseini ◽

Hossein Hashemi ◽

Ahmad Fakheri Fard ◽

Ronny Berndtsson

Keyword(s):

Satellite Data ◽

Precipitation Variability ◽

Data Partitioning ◽

Performance Criteria ◽

Data Driven ◽

Coverage Ratio ◽

Areal Precipitation ◽

Input Variables ◽

Monthly Runoff ◽

New Variables

Satellite remote sensing provides useful gridded data for the conceptual modelling of hydrological processes such as precipitation–runoff relationship. Structurally flexible and computationally advanced AI-assisted data-driven (DD) models foster these applications. However, without linking concepts between variables from many grids, the DD models can be too large to be calibrated efficiently. Therefore, effectively formulized, collective input variables and robust verification of the calibrated models are desired to leverage satellite data for the strategic DD modelling of catchment runoff. This study formulates new satellite-based input variables, namely, catchment- and event-specific areal precipitation coverage ratios (CCOVs and ECOVs, respectively) from the Global Precipitation Mission (GPM) and evaluates their usefulness for monthly runoff modelling from five mountainous Karkheh sub-catchments of 5000–43,000 km2 size in west Iran. Accordingly, 12 different input combinations from GPM and MODIS products were introduced to a generalized deep learning scheme using artificial neural networks (ANNs). Using an adjusted five-fold cross-validation process, 420 different ANN configurations per fold choice and 10 different random initial parameterizations per configuration were tested. Runoff estimates from five hybrid models, each an average of six top-ranked ANNs based on six statistical criteria in calibration, indicated obvious improvements for all sub-catchments using the new variables. Particularly, ECOVs were most efficient for the most challenging sub-catchment, Kashkan, having the highest spacetime precipitation variability. However, better performance criteria were found for sub-catchments with lower precipitation variability. The modelling performance for Kashkan indicated a higher dependency on data partitioning, suggesting that long-term data representativity is important for modelling reliability.

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Modeling Reference Evapotranspiration in a Semiarid Area Using Data-driven Techniques: A Case Study in Lower Cheliff plain, Algeria

10.21203/rs.3.rs-773420/v1 ◽

2021 ◽

Author(s):

Mohammed ACHITE ◽

Muhammad Taghi Sattari ◽

Abderrezak Kamel Toubal ◽

Andrzej Wałęga ◽

Nir Krakauer ◽

...

Keyword(s):

Neural Network ◽

Reference Evapotranspiration ◽

Meteorological Data ◽

Gene Expression Programming ◽

Global Radiation ◽

Performance Criteria ◽

Data Driven ◽

Coefficient Of Determination ◽

Irrigated Agriculture ◽

Efficiency Coefficient

Abstract Evapotranspiration (ET) is an important part of the hydrologic cycle, especially when it comes to irrigated agriculture. For the estimation of reference evapotranspiration (ET0), direct methods either pose difficulties or call for many inputs that may not always be available from weather stations. This study compares Feed Forward Neural Network (FFNN), Radial Basis Function Neural Network (RBFNN). and Gene Expression Programming (GEP) approachs for the estimation of daily ET0 in a weather station in Lower Cheliff plain (northwest Algeria), over a 6-year period (2006–2011). Firstly, measured air temperature, relative humidity, wind speed, solar radiation and global radiation was used to calculate ET0 using FAO-56 Penman-Monteith equation as the reference. Then, the calculated ET0 using FAO-56 Penman-Monteith was considered as output for data driven models, while the measured meteorological data were considered as input of the models. The coefficient of determination (R2), root mean square error (RMSE) and Nash Sutcliffe efficiency coefficient (EF) were used to evaluate the developed models. The results of the developed models were compared with the Penman-Monteith evapotranspiration using these performance criteria. The FFNN model proved to yield the best performance compared to all the developed data-driven models, while the RBF-NN and GEP models also demonstrated potential for good performance.

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Impact of sampling efficiency on the performance of data-driven fish habitat models

Ecological Modelling ◽

10.1016/j.ecolmodel.2012.03.003 ◽

2012 ◽

Vol 245 ◽

pp. 94-102 ◽

Cited By ~ 6

Author(s):

A.M. Mouton ◽

A. Dillen ◽

T. Van den Neucker ◽

D. Buysse ◽

M. Stevens ◽

...

Keyword(s):

Fish Habitat ◽

Data Driven ◽

Sampling Efficiency ◽

Habitat Models

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Predictive Validity of a Selection Procedure for Air Traffic Controller Trainees

Aviation Psychology and Applied Human Factors ◽

10.1027/2192-0923/a000039 ◽

2013 ◽

Vol 3 (1) ◽

pp. 19-27 ◽

Cited By ~ 6

Author(s):

Yvonne Pecena ◽

Doris Keye ◽

Kristin Conzelmann ◽

Dietrich Grasshoff ◽

Peter Maschke ◽

...

Keyword(s):

Predictive Validity ◽

Selection Procedure ◽

Air Traffic ◽

Performance Criteria ◽

Training Phase ◽

Air Traffic Controller ◽

Training Performance ◽

Navigation Service ◽

German Aerospace ◽

Training Success

The job of an air traffic controller (ATCO) is very specific and demanding. The assessment of potential suitable candidates requires a customized and efficient selection procedure. The German Aerospace Center DLR conducts a highly selective, multiple-stage selection procedure for ab initio ATCO applicants for the German Air Navigation Service Provider DFS. Successful applicants start their training with a training phase at the DFS Academy and then continue with a unit training phase in live traffic. ATCO validity studies are scarcely reported in the international scientific literature and have mainly been conducted in a military context with only small and male samples. This validation study encompasses the data from 430 DFS ATCO trainees, starting with candidate selection and extending to the completion of their training. Validity analyses involved the prediction of training success and several training performance criteria derived from initial training. The final training success rate of about 79% was highly satisfactory and higher than that of other countries. The findings demonstrated that all stages of the selection procedure showed predictive validity toward training performance. Among the best predictors were scores measuring attention and multitasking ability, and ratings on general motivation from the interview.

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