Application of Signal Processing in Tracking Meteorological Drought in a Mountainous Region

Vaheddoost, Babak; Safari, Mir Jafar Sadegh

doi:10.1007/s00024-021-02737-8

Cited by 13 publications

(6 citation statements)

References 43 publications

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“…Given the well-established nature of the methodology and equations pertaining to SPI determination, we abstain from reiterating the same details in this section. Interested readers seeking a comprehensive understanding of SPI calculation may refer to the studies conducted by McKee et al [28], Aksoy [29], and Vaheddoost and Safari [30] for in-depth insights and further applications of the SPI.…”

Section: Standard Precipitation Index (Spi)mentioning

confidence: 99%

“…Diverging from prior approaches presented in earlier studies such as those by Bloomfield and Marchant [14] and Liu et al [15], which introduced the groundwater drought index by employing probability distributions like SPI, this study proposes a simpler standardization method similar to the Z-Score Index (ZSI) for the determination of SGL, as elucidated in Vaheddoost and Safari [30]. The ZSI primarily centers on identifying authentic anomalies within the dataset.…”

Section: Standard Groundwater Level (Sgl)mentioning

confidence: 99%

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The Association between Meteorological Drought and the State of the Groundwater Level in Bursa, Turkey

Vaheddoost,

Mohammadi,

Safari

2023

Sustainability

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This study addressed the intricate interplay between meteorological droughts and groundwater level fluctuations in the vicinity of Mount Uludag in Bursa, Turkey. To achieve this, an exhaustive analysis encompassing monthly precipitation records and groundwater level data sourced from three meteorological stations and eight groundwater observation points spanning the period from 2007 to 2018 was performed. Subsequently, this study employed the Standard Precipitation Index (SPI) and Standard Groundwater Level (SGL) metrics, meticulously calculating the temporal extents of drought events for each respective time series. Following this, a judicious application of both the Thiessen and Support Vector Machine (SVM) methodologies was undertaken to ascertain the optimal groundwater observation wells and their corresponding SGL durations, aligning them with SPI durations tied to the selected meteorological stations. The SVM technique, in particular, excelled in the identification of the most pertinent observation wells. Additionally, the Elman Neural Network (ENN) and its optimized version through the Firefly Algorithm (ENN-FA), demonstrated their prowess in accurately predicting SPI durations based on SGL durations. The results were favorable, as evidenced by the commendable performance metrics of the Normalized Root Mean Square Error (NRMSE), the Nash–Sutcliffe Efficiency (NSE), the product of the coefficient of determination and the slope of the regression line (bR2), and the Kling–Gupta Efficiency (KGE). Consequently, the favorable simulation results were construed as evidence supporting the presence of a discernible association between SGL and the duration of the SPI. As we substantiate the concordance between the temporal extent of meteorological droughts and the perturbations in groundwater levels, this unmistakably underscores the fact that the historical fluctuations in groundwater levels within the region were predominantly attributable to climatic influences, rather than being instigated by anthropogenic activities. Nevertheless, it is imperative to underscore that this revelation should not be misconstrued as an endorsement of future heedless exploitation of groundwater resources.

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Section: Standard Precipitation Index (Spi)mentioning

confidence: 99%

Section: Standard Groundwater Level (Sgl)mentioning

confidence: 99%

The Association between Meteorological Drought and the State of the Groundwater Level in Bursa, Turkey

Vaheddoost,

Mohammadi,

Safari

2023

Sustainability

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“…Drought is a naturally occurring disaster that takes place mostly due to insufficient rainfall for a prolonged duration [1,2]. It is widely regarded as a catastrophic and relatively less comprehended hazard.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Meteorological Drought Modeling Accuracy Using Hybrid Boost Regression Models: A Case Study from the Aegean Region, Türkiye

et al. 2023

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The impact of climate change has led to significant changes in hydroclimatic patterns and continuous stress on water resources through frequent wet and dry spells. Hence, understanding and effectively addressing the escalating impact of climate change on hydroclimatic patterns, especially in the context of meteorological drought, necessitates precise modeling of these phenomena. This study focuses on assessing the accuracy of drought modeling using the well-established Standard Precipitation Index (SPI) in the Aegean region of Türkiye. The study utilizes monthly precipitation data from six stations in Cesme, Kusadasi, Manisa, Seferihisar, Selcuk and Izmir at Kucuk Menderes Basin covering the period from 1973 to 2020. The dataset is divided into three sets, training (60%), validation (20%), and testing (20%) sets. The study aims to determine the SPI-3, SPI-6 and SPI-12 using a multi-station prediction technique. Three boosting regression models (BRMs), namely Extreme Gradient Boosting (XgBoost), Adaptive Boosting (AdaBoost), and Gradient Boosting (GradBoost), were employed and optimized with the help of the Weighted Mean of Vectors (INFO) technique. Model performances were then evaluated with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient of Determination (R2) and the Willmott Index (WI). Results demonstrated a distinct superiority of the XgBoost model over AdaBoost and GradBoost in terms of accuracy. During the test phase, the XgBoost model achieved RMSEs of 0.496, 0.429 and 0.389 for SPI-3, SPI-6 and SPI-12, respectively. The WIs were 0.899, 0.901 and 0.825 for SPI-3, SPI-6 and SPI-12, respectively. These are considerably lower than the corresponding values obtained by the other models. Yet, the comparative statistical analysis further underscores the effectiveness of XgBoost in modeling extended periods of drought in the Aegean region of Türkiye.

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“…These classifications represent a decrease in precipitation, a lack of moisture in the soil, a decrease in streamflows, a scarcity of water necessary to fulfill human requirements, and a lack of water in biota, respectively. (Yihdego et al 2017 [18], 2019 [19]; Vaheddoost and Safari, 2021 [14]). For the efficient water resources management, medium and long-term forecasts are critical.…”

Section: Introductionmentioning

confidence: 99%

Drought Modelling Using Artificial Intelligence Algorithms in Izmir District

Özen

Tengerlek

Yüksel

et al. 2021

Lecture Notes in Mechanical Engineering

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The world's water resources are decreasing day by day due to factors such as climate change, drought, inefficient pricing policies implemented by the government, population growth, uncontrolled water consumption, technological developments, and industrialization. A decrease in water resources causes water scarcity in the long-term period. This study is conducted to analysis the meteorological drought, in Izmir district, Turkey. Inspired by the real-life problem, drought estimation models are developed through artificial neural network-based artificial intelligence techniques incorporating a decision support system. The Zscore index (ZSI) values are computed using precipitation data collected from five meteorological station in Küçük Menderes basin, and several developed models are compared according to the variety of statistical performance metrics.

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Application of Signal Processing in Tracking Meteorological Drought in a Mountainous Region

Cited by 13 publications

References 43 publications

The Association between Meteorological Drought and the State of the Groundwater Level in Bursa, Turkey

The Association between Meteorological Drought and the State of the Groundwater Level in Bursa, Turkey

Enhancing Meteorological Drought Modeling Accuracy Using Hybrid Boost Regression Models: A Case Study from the Aegean Region, Türkiye

Drought Modelling Using Artificial Intelligence Algorithms in Izmir District

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