A gradient boosting tree approach for SPEI classification and prediction in Turkey

Mehr, Ali Danandeh; Attar, Nasrin Fathollahzadeh

doi:10.1080/02626667.2021.1962884

Cited by 11 publications

(4 citation statements)

References 30 publications

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“…Danandeh Mehr and Attar [99] presented a gradient boosting regression tree (GBT) model for 1-and 3-month ahead SPEI class prediction in Antalya and Ankara. To this end, SPEI data were gathered from the global SPEI database for four grid points in Antalya and four grid points in Ankara.…”

Section: Review Of Drought Forecasting Studiesmentioning

confidence: 99%

Drought Monitoring and Forecasting across Turkey: A Contemporary Review

et al. 2023

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One of the critical consequences of climate change at both local and regional scales is a change in the patterns of extreme climate events such as droughts. Focusing on the different types of droughts, their quantifying indices, associated indicators, and sources of data (remote sensing (RS)/in situ measurements), this article reviewed the recent studies (from 2010 to 2022) that have explored drought features in Turkey. To this end, a total of 71 articles were selected from the Web of Science (WoS) and Scopus databases. The selected papers were clustered into two categories: (i) drought monitoring studies and (ii) drought forecasting articles. Then, the representative papers were reviewed in detail regarding the implemented indices, models (techniques), case study area, and source of the indicators used to derive drought indices. The review results showed that most of the studies aimed at meteorological drought monitoring and forecasting. An increasing trend was also observed in the use of machine learning for short-term meteorological and hydrological drought prediction. On the other hand, the emerging RS technology and satellite-driven indicators were rarely used in the country. The review showed that there is room for more research on agricultural and hydrological drought monitoring, forecasting, and pattern detection in Turkey.

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Section: Review Of Drought Forecasting Studiesmentioning

confidence: 99%

Drought Monitoring and Forecasting across Turkey: A Contemporary Review

et al. 2023

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“…A powerful ensemble learning approach built on a gradient boosting system is called a gradient boosting regression tree (GBRT) [ 31 ]. To be more precise, GBRT is a robust data-mining technique that has been extensively tested and shown to be successful in various classification and regression problems [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. As a result, in this study the prediction of the Cs of eco-friendly concrete was chosen to demonstrate the potential of the GBRT technique.…”

Section: Research Significancementioning

confidence: 99%

Prediction of Ecofriendly Concrete Compressive Strength Using Gradient Boosting Regression Tree Combined with GridSearchCV Hyperparameter-Optimization Techniques

Alhakeem

Jebur

Henedy³

et al. 2022

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A crucial factor in the efficient design of concrete sustainable buildings is the compressive strength (Cs) of eco-friendly concrete. In this work, a hybrid model of Gradient Boosting Regression Tree (GBRT) with grid search cross-validation (GridSearchCV) optimization technique was used to predict the compressive strength, which allowed us to increase the precision of the prediction models. In addition, to build the proposed models, 164 experiments on eco-friendly concrete compressive strength were gathered for previous researches. The dataset included the water/binder ratio (W/B), curing time (age), the recycled aggregate percentage from the total aggregate in the mixture (RA%), ground granulated blast-furnace slag (GGBFS) material percentage from the total binder used in the mixture (GGBFS%), and superplasticizer (kg). The root mean square error (RMSE) and coefficient of determination (R2) between the observed and forecast strengths were used to evaluate the accuracy of the predictive models. The obtained results indicated that—when compared to the default GBRT model—the GridSearchCV approach can capture more hyperparameters for the GBRT prediction model. Furthermore, the robustness and generalization of the GSC-GBRT model produced notable results, with RMSE and R2 values (for the testing phase) of 2.3214 and 0.9612, respectively. The outcomes proved that the suggested GSC-GBRT model is advantageous. Additionally, the significance and contribution of the input factors that affect the compressive strength were explained using the Shapley additive explanation (SHAP) approach.

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“…When a natural ecosystem is injured by water deficit, this extreme condition is called ecological drought [3]. Each class has own detecting variables such as precipitation and temperature for meteorological drought [4], evaporation stress and soil moisture for agricultural drought [5], surface and subsurface water shortage for hydrological one [6], resilience of inflow-demand and water storage for socioeconomic drought [7], and difference between ecological water requirement and consumption for ecological drought [8]. As these parameters, particularly water cycle components, are highly nonlinear and non-stationary in nature, the associated drought indices have chaotic characteristics, and hence are challenging to model and forecast [9,10].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Extreme Learning Machine for Drought Forecasting: Water Cycle vs. Bacterial Foraging

et al. 2023

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Machine learning (ML) methods have shown noteworthy skill in recognizing environmental patterns. However, presence of weather noise associated with the chaotic characteristics of water cycle components restricts the capability of standalone ML models in the modeling of extreme climate events such as droughts. To tackle the problem, this article suggests two novel hybrid ML models based on combination of extreme learning machine (ELM) with water cycle algorithm (WCA) and bacterial foraging optimization (BFO). The new models, respectively called ELM-WCA and ELM-BFO, were applied to forecast standardized precipitation evapotranspiration index (SPEI) at Beypazari and Nallihan meteorological stations in Ankara province (Turkey). The performance of the proposed models was compared with those the standalone ELM considering root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and graphical plots. The forecasting results for three- and six-month accumulation periods showed that the ELM-WCA is superior to its counterparts. The NSE results of the SPEI-3 forecasting in the testing period proved that the ELM-WCA improved drought modeling accuracy of the standalone ELM up to 72% and 85% at Beypazari and Nallihan stations, respectively. Regarding the SPEI-6 forecasting results, the ELM-WCA achieved the highest RMSE reduction percentage about 63% and 56% at Beypazari and Nallihan stations, respectively.

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A gradient boosting tree approach for SPEI classification and prediction in Turkey

Cited by 11 publications

References 30 publications

Drought Monitoring and Forecasting across Turkey: A Contemporary Review

Drought Monitoring and Forecasting across Turkey: A Contemporary Review

Prediction of Ecofriendly Concrete Compressive Strength Using Gradient Boosting Regression Tree Combined with GridSearchCV Hyperparameter-Optimization Techniques

Optimizing Extreme Learning Machine for Drought Forecasting: Water Cycle vs. Bacterial Foraging

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