An improved adaptive neuro-fuzzy inference system for hydrological drought prediction in Algeria

Achite, Mohammed; Gül, Enes; Elshaboury, Nehal; Jehanzaib, Muhammad; Mohammadi, Babak; Mehr, Ali Danandeh

doi:10.1016/j.pce.2023.103451

Cited by 4 publications

(2 citation statements)

References 49 publications

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“…When demand is higher than supply, water shortages occur; in this direction, the beginning of storage is a barometer of water shortages (Tareke & Awoke, 2022). Achite et al (2023) defined this kind of drought as a lack of groundwater supplies or streamflow shortages. Other researchers have defined hydrological drought as a significant reduction in available water (surface and underground water).…”

Section: Hydrological Droughtmentioning

confidence: 99%

See 1 more Smart Citation

Drought characterization: A systematic literature review

Choukri,

Naimi,

Chikhaoui

2023

Sains Tanah J. Soil Sci. Agroclimatology

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<p>This study examined the worsening severity of global droughts caused by climate change. However, the multiple definitions and varied range of drought indices pose challenges in effectively monitoring and assessing the prevalence and severity of droughts. This study aims to give a comprehensive overview of the various drought definitions found in the literature and how they have evolved based on their applications. Specifically, the focus was to shed light on the dynamic nature of drought characterization and offer insights into the factors that shaped its conceptualization over time. Within this context, this study explored three primary categories of drought indices: climatic, remote sensing, and composite. Each category was discussed in relation to its utility in specific fields, such as meteorological, agricultural, and hydrological drought assessments, along with an analysis of their strengths and limitations. Furthermore, this study presents modified meteorological drought indices that have been adapted to better monitor agricultural droughts. Additionally, the authors used geographic information systems to create a map showing the distribution of drought-related publications globally over the past decade. The findings showed that countries with arid and semi-arid climates are more actively involved in drought research, highlighting their particular interest and concern regarding the subject matter. The implications of this study emphasize the urgent need for immediate and coordinated efforts to address the escalating issue of droughts caused by climate change. By improving monitoring and assessment methods and focusing on tailored strategies in vulnerable regions, it is possible to mitigate the far-reaching consequences of drought and to build more resilient communities and ecosystems.</p>

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Section: Hydrological Droughtmentioning

confidence: 99%

“…Similar to SPI, the RDI can be computed for any time scale. The estimation method for this index is detailed by Moorhead et al (2015) and Achite et al (2023).…”

Section: Simple Indices 3311 Climatic Indicesmentioning

confidence: 99%

Drought characterization: A systematic literature review

Choukri,

Naimi,

Chikhaoui

2023

Sains Tanah J. Soil Sci. Agroclimatology

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A novel metaheuristic optimization and soft computing techniques for improved hydrological drought forecasting

Katipoğlu,

Ertugay,

Elshaboury

et al. 2024

Physics and Chemistry of the Earth, Parts A/B/C

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Artificial Neural Networks for Drought Forecasting in the Central Region of the State of Zacatecas, Mexico

Esquivel-Saenz,

Ortiz-Gómez,

Zavala

et al. 2024

Climate

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Drought is, among natural hazards, one of the most harmful to humanity. The forecasting of droughts is essential to reduce their impact on the economy, agriculture, tourism and water resource systems. In this study, drought forecast in the central region of the state of Zacatecas, a semi-arid region of Mexico, is explored by means of artificial neural networks (ANNs), forecasting numerical values of three drought indices—the standardized precipitation index (SPI), the standardized precipitation and evapotranspiration index (SPEI) and the reconnaissance drought index (RDI)—in an effort to establish the most suitable index for drought forecasting with ANNs in semi-arid regions. Records of 52 years of monthly precipitation and temperature were used. The indices were calculated in three different time scales: 3, 6 and 12 months. The analyzed models showed great capacity to forecast the values of the three drought indices, and it was found that for the trial set, the RDI was the drought index that was best fitted by the models, with the evaluation metrics R2 (determination coefficient), RMSE (root mean square error), MAE (mean absolute error) and MBE (Mean Bias Error) showing ranges of 0.834–0.988, 0.099–0.402, 0.072–0.343 and 0.017–0.095, respectively. For the validation set, the evaluation metrics were slightly better.

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An improved adaptive neuro-fuzzy inference system for hydrological drought prediction in Algeria

Cited by 4 publications

References 49 publications

Drought characterization: A systematic literature review

Drought characterization: A systematic literature review

A novel metaheuristic optimization and soft computing techniques for improved hydrological drought forecasting

Artificial Neural Networks for Drought Forecasting in the Central Region of the State of Zacatecas, Mexico

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