Estimation of monthly evaporation values using gradient boosting machines and mode decomposition techniques in the Southeast Anatolia Project (GAP) area in Turkey

Sarıgöl, Metin; Katipoğlu, Okan Mert

doi:10.1007/s11600-023-01067-8

Cited by 10 publications

(4 citation statements)

References 33 publications

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“…As a result of the analysis, it was concluded that the data preprocessing based GBM model produced the best output in evaporation prediction Kisi & Zounemat-Kermani (2014);. Gumus et al (2016);Sarıgöl & Katipoğlu (2023) studies confirm the study in terms of the input combinations used in evaporation prediction and the successful prediction ability of AI mathematicians.…”

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confidence: 67%

“…Sarıgöl & Katipoğlu (2023) estimated the evaporation values of the Southeast Anatolia Project (GAP) area in Turkey using gradient boosting machines (GBM) and Mode Decomposition techniques. Precipitation, average, minimum and maximum air temperature, wind speed, actual air pressure, relative humidity, and solar time variables were first divided into subcomponents and then presented as input to the GMB model.…”

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confidence: 99%

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The Impact of Preprocessing Approaches on Neural Network Performance: A Case Study on Evaporation in Adana, a Mediterranean Climate

Katipoglu,

Pekin,

Akil

2023

Idn. J. Earth Sci.

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The application of artificial intelligence (AI) technologies is quickly expanding in water management. Additionally, the artificial neural network methodology has an advantage over traditional statistical approaches in that it does not need assumptions about the distribution of data and variables. These methods can be used if there is a large enough data collection and criteria relevant to the nature of the problem. Preprocessing data before utilizing it improves the performance of the AI model. Evaporation matters in water management, agriculture processes and soil science. It is critical to ensure proper estimation of evaporation losses for effective water resource planning and management particularly in drought-prone areas such as Adana. Artificial intelligence approaches can be applied successfully in evaporation calculation. In this research, we used the Standard scaler, power transformer, robust scaler quantile transformer (Uniform) and quantile transformer (Normal), and min-max scaler preprocessing techniques to preprocess the multilayer perceptron neural network (MLPNN). We also trained the MLPNN using unprocessed data and compared it to the results of the preprocessed model. In the setup of the model, daily temperature, pressure, wind, sunny hours, and humidity parameters covering the years 2018-2021 were presented as input to the MLPNN model. Consequently, we pinpoint that all preprocessing approaches produce better outcomes than unscaled. Although all models produced statistically high accuracy predictions according to statistical criteria, the MLPNN model established without transformation (test phase: r2: 0.93, NSE : 0.927, SMAPE: 10.77, RMSE: 1.2, MAE: 0.9) exhibited the lowest accuracy. The evaporation prediction model that was developed using the MLPNN-based standard scalar optimization algorithm exhibited the highest level of accuracy (test phase: r2: 0.978, NSE: 0.977, SMAPE: 5.93, RMSE: 0.68, MAE: 0.48). Power Transformer (test phase: r2: 0.978, NSE: 0.977, SMAPE: 5.81, RMSE: 0.67, MAE: 0.49) showed second-degree promising results. It can be concluded from these results that the estimation of meteorological variables requires the scaling and presentation of data in a uniform structure. Therefore, improving efficiency and productivity in water management or agricultural processes can be enhanced by making more accurate evaporation estimates.

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confidence: 67%

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confidence: 99%

The Impact of Preprocessing Approaches on Neural Network Performance: A Case Study on Evaporation in Adana, a Mediterranean Climate

Katipoglu,

Pekin,

Akil

2023

Idn. J. Earth Sci.

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“…SVM and Random Forest are the two most widely utilized ML techniques for estimating solar radiation and ET. In the literature, there are also deep learning models [15] and hybrid ML models for predicting ET [16,17]. Table 5 provides a summary of ML-based methods for climate-smart agriculture approach.…”

Section: Estimation Of Gsr and Et Using ML Modelsmentioning

confidence: 99%

Crop water management using machine learning-based evapotranspiration estimation

Meenal,

Jala,

Samundeswari

et al. 2024

J App Biol Biotech

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variations, the pre-developed systems have not used all available weather data and so no robust models. As a consequence of cost and difficulties in direct measurement techniques with a pyranometer and lysimeter, solar radiation and ET 0 were predicted using suitable models [8]. Different empirical models have been developed for ET 0 estimation rendering to various climatic conditions [9,10]. Many models such as empirical, artificial neural network, machine learning (ML), and deep learning exist in the literature to compute the global solar radiation (GSR) and ET 0 [11,12]. However, the standard method recommended by the Food and Agriculture Organization (FAO), namely, the Penman-Monteith (PM-FAO56) equation requires an extensive range of data support for ET estimation. In this work, ML models with a limited number of input parameters are utilized to estimate the solar radiation and ET in chosen locations of Tamil Nādu. To estimate the ET 0 , the solar radiation and temperature values are used. Empirical correlations are also utilized to estimate the ET 0 for comparison with ML methods. Based on the performance metrics, ML-based ET 0 estimations are more accurate than empirical-based estimations. To develop the ML model, SVM and random forest algorithms are employed with a reduced number of meteorological parameters. STUDY AREA AND DATA SOURCESThe study site, Coimbatore has a semi-arid tropical climate. The tomato is one of the horticultural products produced in the study location. The water requirement for tomato plant in the study location is calculated by the relation of crop coefficient and ET. The geographical parameters of the study location are given in Table 1 and Figure 1 shows the monthly

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“…They determined that all methods present good results in subtropical China, but the CatBoost method significantly improves ET forecasting. The literature shows that numerous meteorological and hydrological prediction models use the ANN and ML methods (Kisi and Alizamir 2018 ; Chong et al 2020 ; Hameed et al 2021 ; Quilty and Adamowski 2021 ; Sarıgöl and Katipoğlu 2023 ). Katipoglu ( 2023 ) predicted ET values by combining discrete wavelet decomposition and soft computing techniques in the semi-arid Hakkâri province.…”

Section: Introductionmentioning

confidence: 99%

Prediction of monthly evapotranspiration by artificial neural network model development with Levenberg–Marquardt method in Elazig, Turkey

Kartal

2024

Environ Sci Pollut Res

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The phenomenon of evapotranspiration (ET) is closely linked to the issue of water scarcity, as it involves water loss through both evaporation and plant transpiration. Accurate prediction of evapotranspiration is of utmost importance in the strategic planning of agricultural irrigation, effective management of water resources, and precise hydrological modeling. The current investigation aims to predict the monthly ET values in the Elazig province by developing an artificial neural network (ANN) model utilizing the Levenberg–Marquardt method. Consequently, the values of temperature, precipitation, relative humidity, solar hour, and mean wind speed were utilized in forecasting evapotranspiration values by implementing ANN algorithms. This research makes a valuable contribution to the existing body of literature by utilizing an ANN model developed with the Levenberg–Marquardt method to estimate evapotranspiration. It has been discovered that evapotranspiration values are impacted by various factors such as temperature (minimum, average, maximum), relative humidity (minimum, average, maximum), wind speed, solar hour, and precipitation values, which are taken into consideration for prediction. The findings indicated that Elazig, Keban, Baskil, and Agin sites had R values of 0.9995, 0.9948, 0.9898, and 0.9994 in the proposed model. It was found that Elazig’s MAPE ranged from 0 to 0.2288, Keban’s was 0.0001 to 0.3703, Baskil’s was between 0 and 0.4453, and Agin’s was both 0 and 0.2784. The findings obtained from the proposed model are compatible with evapotranspiration values computed from the Hargreaves method (R2 = 0.996). The study’s findings provide significant insights for planners and decision-makers involved in the planning and managing water resources and agricultural irrigation.

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Estimation of monthly evaporation values using gradient boosting machines and mode decomposition techniques in the Southeast Anatolia Project (GAP) area in Turkey

Cited by 10 publications

References 33 publications

The Impact of Preprocessing Approaches on Neural Network Performance: A Case Study on Evaporation in Adana, a Mediterranean Climate

The Impact of Preprocessing Approaches on Neural Network Performance: A Case Study on Evaporation in Adana, a Mediterranean Climate

Crop water management using machine learning-based evapotranspiration estimation

Prediction of monthly evapotranspiration by artificial neural network model development with Levenberg–Marquardt method in Elazig, Turkey

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