Prediction of droughts over Pakistan using machine learning algorithms

Khan, Najeebullah; Sachindra, D. A.; Shahid, Shamsuddin; Ahmed, Kamal; Shiru, Mohammed Sanusi; Nawaz, Nadeem

doi:10.1016/j.advwatres.2020.103562

Cited by 192 publications

(76 citation statements)

References 94 publications

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“…The possible reason could be statistical operations that average/sum the seasonal droughts, and thus such indicators are lost due to a few extreme events in the months with increasing trends or vice versa. The second possible reason could be Asian summer monsoon and/ or WD dominancy during the Rabi season (Rehman et al 2018;Khan et al 2020;Adnan et al 2020;Feng et al 2020). The positive trend in Kharif season and annual timescale may positively affect agriculture production, water management and built environment in Pakistan.…”

Section: Discussionmentioning

confidence: 99%

“…Pakistan has two main seasons: summer and winter monsoon; the summer monsoon precipitation spans four months from June to September, and winter precipitation extents over December to March. The westerly disturbance in the Mediterranean Sea caused by winter monsoon precipitation, while the monsoon winds mostly cause the summer precipitation originated from the Bay of Bengal (Ahmed et al 2018;Khan et al 2020). The Rabi (winter) and Kharif (summer) are the two distinct cropping seasons in the region.…”

Section: Study Areamentioning

confidence: 99%

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Influence of large-scale circulation and interannual modes of climate variability on seasonal drought characteristics over Pakistan

Ullah

Yin

et al. 2021

Preprint

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The long-term drought monitoring and its assessment are of great importance for meteorological disaster risk management. The recurrent spells of heatwaves and droughts have severely affected the environmental conditions worldwide, including Pakistan. The present work sought to investigate the spatiotemporal changes in drought characteristics over Pakistan during Rabi and Kharif cropping seasons. The role of large-scale circulation and interannual mode of climate variability is further explored to identify the physical mechanisms associated with droughts in the region. Monthly precipitation and temperature data (1983–2019) from 53 meteorological stations were used to study drought characteristics, using the standardized precipitation evapotranspiration index (SPEI). The non-parametric Mann-Kendall (MK), Sen’s Slope (SS), and Sequential Mann-Kendall (SQMK) tests were applied on the drought index to determine the statistical significance and magnitude of the historical trend. The state-of-the-art Bayesian Dynamic Linear (BDL) model was further used to analyze large scale climate drivers of droughts, analysis revealed an increase in drought severity mostly over arid to semi-arid regions for both cropping seasons. Temperature played a significant role in defining droughts over dry and hot seasons, while rainfall is influential over the western disturbances (WD) influenced region. The analysis of atmospheric circulation patterns revealed that large-scale changes in wind speed, air temperature, relative humidity and geopotential height anomalies are the likely drivers of droughts in the region. We found that Niño4, Sea Surface Temperature (SST), and multivariate El Niño-Southern Oscillation (ENSO4.0) Index are the most influential factors for seasonal droughts across Pakistan. Overall, the findings provide a better understanding of drought-prone areas in the region, and this information is of potential use for mitigating and managing drought risks.

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Section: Discussionmentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Influence of large-scale circulation and interannual modes of climate variability on seasonal drought characteristics over Pakistan

Ullah

Yin

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Droughts have become more common, the frequency and the severity have increased a lot due to climate change making adverse effects and a early warning system extremely necessary for the world to prepare for the adverse impacts [20]. In NatDisP the machine learning algorithms are used to predict and warn for droughts as well.…”

Section: Droughtmentioning

confidence: 99%

NatDisP – An Intelligent Natural Disaster Predictor

Mittapalli

Mutha

Maheswari

2021

Preprint

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Natural disasters are adverse actions that happen due to the natural processes of the earth. In today’s world with so much pollution, global warming and because of so many reasons, natural disasters are happening far more than they used to happen before and many people in the world face problems, lose their houses, livelihoods and even their houses. It is really painful to get to know the effects of these natural disasters. So, this paper proposes a model which helps us predict natural disasters before they happen using wireless technologies. In this paper modern technologies like IOT, artificial intelligence and machine learning are used. Here for the prediction of each disaster, data/signals given by nature are used , for example for the earthquake module the seismic signals from the earth are used, systems like UNITE are used where the sensors placed in the earth to get the seismic data. For the other disasters also such data is taken from signals given by nature. A detailed explanation on how disasters are predicted based on these simple signals and data from nature is given. In this paper solutions based on wireless technologies to solve some after effects of these natural disasters are also suggested . So, basically our idea is to have all disasters predicted in one place using modern computer science technologies.

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“…To prevent this an early stopping method, namely generalization loss, was used. Similarly, SVR may suffer from over-fitting and under-fitting during calibration and validation periods, respectively [20]. In such cases, the inputs are mapped at a higher dimension space so that the initial non-linear relationship between the prediction and the variables becomes linear, achieved through a kernel function.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Temporal Hydrological Drought Index Forecasting for New South Wales, Australia Using Machine Learning Approaches

2020

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Droughts can cause significant damage to agriculture and water resources leading to severe economic losses. One of the most important aspects of drought management is to develop useful tools to forecast drought events, which could be helpful in mitigation strategies. The recent global trends in drought events reveal that climate change would be a dominant factor in influencing such events. The present study aims to understand this effect for the New South Wales (NSW) region of Australia, which has suffered from several droughts in recent decades. The understanding of the drought is usually carried out using a drought index, therefore the Standard Precipitation Evaporation Index (SPEI) was chosen as it uses both rainfall and temperature parameters in its calculation and has proven to better reflect drought. The drought index was calculated at various time scales (1, 3, 6, and 12 months) using a Climate Research Unit (CRU) dataset. The study focused on predicting the temporal aspect of the drought index using 13 different variables, of which eight were climatic drivers and sea surface temperature indices, and the remainder were various meteorological variables. The models used for forecasting were an artificial neural network (ANN) and support vector regression (SVR). The model was trained from 1901–2010 and tested for nine years (2011–2018), using three different performance metric scores (coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The results indicate that ANN was better than SVR in predicting temporal drought trends, with the highest R2 value of 0.86 for the former compared to 0.75 for the latter. The study also reveals that sea surface temperatures and the climatic index (Pacific Decadal Oscillation) do not have a significant effect on the temporal drought aspect. The present work can be considered as a first step, wherein we only study the temporal trends, towards the use of climatological variables and drought incidences for the NSW region.

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Prediction of droughts over Pakistan using machine learning algorithms

Cited by 192 publications

References 94 publications

Influence of large-scale circulation and interannual modes of climate variability on seasonal drought characteristics over Pakistan

Influence of large-scale circulation and interannual modes of climate variability on seasonal drought characteristics over Pakistan

NatDisP – An Intelligent Natural Disaster Predictor

Temporal Hydrological Drought Index Forecasting for New South Wales, Australia Using Machine Learning Approaches

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