Drought Prediction With Standardized Precipitation and Evapotranspiration Index and Support Vector Regression Models

Deo, Ravinesh C.; Salcedo‐Sanz, Sancho; Carro-Calvo, L.; Saavedra-Moreno, B.

doi:10.1016/b978-0-12-812056-9.00010-5

Cited by 34 publications

(14 citation statements)

References 76 publications

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“…Therefore, SPEI reflects the effects of temperature on droughts, and so is more suitable for drought monitoring in a changing climate. There have been various studies conducted that show the efficiency of SPEI as an index to monitor and characterize droughts [14][15][16][17][18][19][20][21][22]. SPEI has also been documented to be a suitable indicator for monitoring droughts in Pakistan [14,23].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Evolution of Drought Characteristics in Balochistan, Pakistan

et al. 2020

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In the wake of a rapidly changing climate, droughts have intensified, in both duration and severity, across the globe. The Germanwatch long-term Climate Risk Index ranks Pakistan among the top 10 countries most affected by the adverse effects of climate change. Within Pakistan, the province of Balochistan is among the most vulnerable regions due to recurring prolonged droughts, erratic precipitation patterns, and dependence on agriculture and livestock for survival. This study aims to explore how the characteristics of droughts have evolved in the region from 1902–2015 using 3-month and 12-month timescales of a popular drought index, the Standardized Precipitation Evapotranspiration Index (SPEI). The region was divided into six zones using Spatial “K”luster Analysis using Tree Edge Removal (SKATER) method, and run theory was applied to characterize droughts in terms of duration, severity, intensity, and peak. The results of the non-parametric Mann–Kendall trend test applied to SPEI indicate prevailing significant negative trends (dryer conditions) in all the zones. Balochistan experienced its most severe droughts in the 1960s and around 2000. The effects of climate change are also evident in the fact that all the long duration droughts occurred after 1960. Moreover, the number of droughts identified by 3-month SPEI showed a significant increase after 1960 for all six zones. The same trend was found in the 12-month SPEI but for only three zones.

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Section: Introductionmentioning

confidence: 99%

Exploring the Evolution of Drought Characteristics in Balochistan, Pakistan

et al. 2020

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“…In addition, These results are in agreement with those from Borji et al, (2016), who simulated the stream flow drought index (SDI)of different multi timescales and indicated that R 2 ranged from 0.65 to 0.99. Also, SVM has been used for predicting drought based on SPEI by Deo et al (2018) and found that R 2 ranged from 0.52 to 0.98. Moreover, these findings were extremely an acceptable and agree with those suggested by Fung et al, (2019), who applied an improved SVM for estimation of SPEI-1, SPEI-3 and SPEI-6, at various timescales and their findings resulted that R 2 ranged from 0.83 to 0.95 for training and from 0.79 to 0.90 for validation periods.…”

Section: Resultsmentioning

confidence: 99%

Applying GRACE satellite data and hydro-meteorological parameters for predicting the combined terrestrial evapotranspiration index (CTEI) based on an improved SVM and a greedy regression approach

Elbeltagi

Jamei²,

Srivastava

et al. 2021

Preprint

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Drought has become relatively one of the widespread extreme events that affect socio-economic sphere, ecological balance, environmental conditions, crop-yields, climate feedback mechanism, and water resources management. Monitoring of the drought is one of the challenging tasks, hence required to develop new techniques in prediction of this natural calamity accurately and timely. In this work, Combined Terrestrial Evapotranspiration Index (CTEI) that is forced through precipitation (P) and potential evapotranspiration (PET) is used for estimating drought. Also, this is a novel index developed for Indus-Ganga river basin, which also uses a Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage anomalies (TWSA) data. Thirteen input-variables comprised of GRACE-TWSA, Global Land Data Assimilation System GLDAS-TWSA, Groundwater Storage Anomaly (GWSA), P, Land Surface Temperature ( LST), Wind Speed (WS), Evapotranspiration (ET), Runoff, air temperature ( ), PET, net-shortwave (SWN) and long-wave (LWN) along with net radiation (NR) radiations were implemented for predicting CTEI. Further, thirteen different combinations were analyzed using an improved Sequential Minimal Optimization (SMO) algorithm for support vector machine (SVM) and a greedy linear regression method. Therefore, the objectives of this study are to develop several combinations based on machine learning models used for modeling CTEI, compare the accuracy and stability of these models in CTEI prediction, and select the best outcomes have been provided from these models. The performed error measures and statistical indicators prove that Combo 2 and 3 owing to SVM model are superior to the best combinations of Greedy model. Overall based on the results, the improved SVM is superior to Greedy model in estimating CTEI. SVM (Combo2: R=0.81) in spite of having a higher range of relative error, is superior to Greedy model (Combo3, R=0.77) for estimating CTEI. This method can be one of promising alternatives for estimating CTEI over major river basins across the world.

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“…Toyasaki et al [18] focused on horizontal cooperation in inventory management, which is currently implemented in the United Nations Humanitarian Response Depot (UNHRD) network, and proposed a policy priority for the first-best system of optimal inventory management. Deo [19] used integrated disaster management technology, a quantitative method, and big data analysis to create disaster and early warning models to reduce the impact of these disasters and provide a comprehensive method for disaster management systems. Nassereddine et al [20] presented a multi-criteria decision-making approach to evaluate emergency response systems by taking into account the interaction synergy.…”

Section: Emergency Relief For Natural Hazardsmentioning

confidence: 99%

Research on the Construction of a Natural Hazard Emergency Relief Alliance Based on the Public Participation Degree

et al. 2020

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At present, in light of new situations and the new task of natural hazard response, effective public participation in emergency relief has become an urgent task that can reduce economic losses and casualties. The purpose of this paper is to construct a natural hazard emergency relief alliance and analyze the mechanisms and dynamics of public participation. In this study, methods based on a multi-agent system were adopted, and we used different participants as heterogeneous agents with different attitudes and resources. Using four different processes, namely participation proposals, negotiation interval, negotiation decision-making function, and participation strategy, we comprehensively construct an emergency relief alliance for natural hazards. In addition, the dynamic public interaction process is analyzed and a construction algorithm is given. The experimental results show that the proposed method has better performance in alliance formation efficiency, negotiation efficiency, and agent utility. The research results illustrate that the public’s attitudes and resources influence the construction of emergency relief alliances; a greater degree of public participation contributes to a more efficient alliance formation. The findings of this study contribute to the promotion of public cooperation and improvement in the efficiency of natural hazard emergency relief.

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Drought Prediction With Standardized Precipitation and Evapotranspiration Index and Support Vector Regression Models

Cited by 34 publications

References 76 publications

Exploring the Evolution of Drought Characteristics in Balochistan, Pakistan

Exploring the Evolution of Drought Characteristics in Balochistan, Pakistan

Applying GRACE satellite data and hydro-meteorological parameters for predicting the combined terrestrial evapotranspiration index (CTEI) based on an improved SVM and a greedy regression approach

Research on the Construction of a Natural Hazard Emergency Relief Alliance Based on the Public Participation Degree

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