Selection of extreme drought event for Langat Basin and its consequence on salinity intrusion through Langat River System

Hasan, Md Mobassarul; Begum, Momena; Mamun, Abdullah Al; Khan, Zahirul Haque

doi:10.9790/1684-11376269

Cited by 1 publication

(2 citation statements)

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“…The temperature of the area varies from 23.5 • C to 33.5 • C all year, and the comparative humidity ranges from 63% to 95%, with an average of 81%. Heavier rainfall happens in the month of November, with a monthly average rainfall of 270 mm; the average annual rainfall of the study area is about 2400 mm [38]. The area also sometimes experiences rainstorms, and these usually occur in the early evening through the year, and are usually of a short duration with a high intensity.…”

Section: Langat River Basinmentioning

confidence: 96%

“…This river basin is located near Kuala Lumpur, the capital city of Malaysia. Therefore, the study area has been rapidly developed, which makes it dependent on the Langat River for water supply [38]. The Langat River has an estimated total catchment area of 1817 km 2 and is located at latitude 2°40′152″ N to 3°16′15″ N and longitude 101°19′20″ E to 102°1′10″ E [30], and the main river is 141 km in length.…”

Section: Langat River Basinmentioning

confidence: 99%

See 1 more Smart Citation

Wavelet-ANN versus ANN-Based Model for Hydrometeorological Drought Forecasting

Khan

Muhammad

El-Shafie

2018

Water

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Malaysia is one of the countries that has been experiencing droughts caused by a warming climate. This study considered the Standard Index of Annual Precipitation (SIAP) and Standardized Water Storage Index (SWSI) to represent meteorological and hydrological drought, respectively. The study area is the Langat River Basin, located in the central part of peninsular Malaysia. The analysis was done using rainfall and water level data over 30 years, from 1986 to 2016. Both of the indices were calculated in monthly scale, and two neural network-based models and two wavelet-based artificial neural network (W-ANN) models were developed for monthly droughts. The performance of the SIAP and SWSI models, in terms of the correlation coefficient (R), was 0.899 and 0.968, respectively. The application of a wavelet for preprocessing the raw data in the developed W-ANN models achieved higher correlation coefficients for most of the scenarios. This proves that the created model can predict meteorological and hydrological droughts very close to the observed values. Overall, this study helps us to understand the history of drought conditions over the past 30 years in the Langat River Basin. It further helps us to forecast drought and to assist in water resource management.

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Section: Langat River Basinmentioning

confidence: 96%

Section: Langat River Basinmentioning

confidence: 99%