Complementary data-intelligence model for river flow simulation

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher; Awadh, Salih Muhammad; Sharafati, Ahmad; Shahid, Shamsuddin

doi:10.1016/j.jhydrol.2018.10.020

Cited by 87 publications

(31 citation statements)

References 32 publications

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“…e proposed model revealed good capability to mimic the monthly river flow trend. Later on, several studies approved the potential of the ELM in the field of hydrology processes [13,[24][25][26][27][28][29]. Based on the reported literature of the extreme learning machine modeling and its version improvement in hydrological problems over a very short period, the authors' attention is mainly to modify this robust predictive model and attempt to enhance its predictability skills.…”

Section: Introductionmentioning

confidence: 99%

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Faris

Al‐Ansari

2020

Complexity

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e capability of the extreme learning machine (ELM) model in modeling stochastic, nonlinear, and complex hydrological engineering problems has been proven remarkably. e classical ELM training algorithm is based on a nontuned and random procedure that might not be efficient in convergence of excellent performance or possible entrapment in the local minima problem. is current study investigates the integration of a newly explored metaheuristic algorithm (i.e., Salp Swarm Algorithm (SSA)) with the ELM model to forecast monthly river flow. Twenty years of river flow data time series of the Tigris river at the Baghdad station, Iraq, is used as a case study. Different input combinations are applied for constructing the predictive models based on antecedent values. e results are evaluated based on several statistical measures and graphical presentations. e river flow forecast accuracy of SSA-ELM outperformed the classical ELM and other artificial intelligence (AI) models. Over the testing phase, the proposed SSA-ELM model yielded a satisfactory enhancement in the level accuracies (8.4 and 13.1 percentage of augmentation for RMSE and MAE, respectively) against the classical ELM model. In summary, the study ascertains that the SSA-ELM model is a qualified data-intelligent model for monthly river flow prediction at the Tigris river, Iraq.

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Section: Introductionmentioning

confidence: 99%

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Faris

Al‐Ansari

2020

Complexity

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“…Reliable prediction of river flow in catchment is very important due to its complex nature, particularly highly non-linearity [29]. Based on the Digital Elevation Model (DEM), which represents terrain's surface produced by 3D graphic tools, the data extracted from the contour lines in each of the catchments in the study site was assessed using a hydrologic analysis methodology.…”

Section: Catchment Analysis Methodsmentioning

confidence: 99%

GIS Based Assessment and Design for Areas Vulnerable to Soil Disasters: Case Study of Namhyeun-dong, South Korea

Jeon

Kang

2020

Sustainability

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Due to climate change, heavy rainfall events that trigger landslips are becoming common. This study investigates patterns in the soil’s response to major rainfall events in mountainous areas and proposes a new approach for resilient disaster prevention technology and recovery based on the effect of soil runoff. Namhyeon-dong within Seoul was selected for the case study because of its vulnerable location between two mountains. A master plan was developed to cope with the predicted soil runoff based on the annual rainfall, local land use and a series of 10-year forecasts covering the period from 2021 to 2100. A total of 22 catchments in the study area were analyzed with Arc Hydro, an ArcGIS plug-in and appropriate technologies proposed to deal with the soil runoff likely to be experienced in each catchment in an extreme disaster. The resulting model was deemed adequate to deal with disasters during the period predicted to represent the highest risk, 2051–2060. The study’s findings will help to forecast disasters from flood that could impact residential areas in mountainous regions, to predict the magnitude of potential soil disasters in individual regions and develop design guidelines for disaster prevention technology based on the predicted amount of soil runoff.

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“…Therefore, the changes in CWA and CWD would provide valuable knowledge required for long-term management of water resources in Iraq. [33]. The hot and dry summer (June-September) and the cool and wet winter (November-March) dominate the climate of the country [34].…”

Section: Introductionmentioning

confidence: 99%

“…Three major river basins namely, the Tigris, the Euphrates, and the Shatt Al-Arab cover the most parts of the county [39]. 102 with a total land area of 438,320 km 2 [33]. The hot and dry summer (June-September) and the cool 103 and wet winter (November-March) dominate the climate of the country [34].…”

Section: Introductionmentioning

confidence: 99%

Changes in Climatic Water Availability and Crop Water Demand for Iraq Region

et al. 2020

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Decreases in climatic water availability (CWA) and increases in crop water demand (CWD) in the background of climate change are a major concern in arid regions because of less water availability and higher irrigation requirements for crop production. Assessment of the spatiotemporal changes in CWA and CWD is important for the adaptation of irrigated agriculture to climate change for such regions. The recent changes in CWA and CWD during growing seasons of major crops have been assessed for Iraq where rapid changes in climate have been noticed in recent decades. Gridded precipitation of the global precipitation climatology center (GPCC) and gridded temperature of the climate research unit (CRU) having a spatial resolution of 0.5°, were used for the estimation of CWA and CWD using simple water balance equations. The Mann–Kendall (MK) test and one of its modified versions which can consider long-term persistence in time series, were used to estimate trends in CWA for the period 1961–2013. In addition, the changes in CWD between early (1961–1990) and late (1984–2013) periods were evaluated using the Wilcoxon rank test. The results revealed a deficit in water in all the seasons in most of the country while a surplus in the northern highlands in all the seasons except summer was observed. A significant reduction in the annual amount of CWA at a rate of −1 to −13 mm/year was observed at 0.5 level of significance in most of Iraq except in the north. Decreasing trends in CWA in spring (−0.4 to −1.8 mm/year), summer (−5.0 to −11 mm/year) and autumn (0.3 to −0.6 mm/year), and almost no change in winter was observed. The CWA during the growing season of summer crop (millet and sorghum) was found to decrease significantly in most of Iraq except in the north. The comparison of CWD revealed an increase in agricultural water needs in the late period (1984–2013) compared to the early period (1961–1990) by 1.0–8.0, 1.0–14, 15–30, 14–27 and 0.0–10 mm for wheat, barley, millet, sorghum and potato, respectively. The highest increase in CWD was found in April, October, June, June and April for wheat, barley, millet, sorghum and potato, respectively.

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Complementary data-intelligence model for river flow simulation

Cited by 87 publications

References 32 publications

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

GIS Based Assessment and Design for Areas Vulnerable to Soil Disasters: Case Study of Namhyeun-dong, South Korea

Changes in Climatic Water Availability and Crop Water Demand for Iraq Region

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