Redesigning rain gauges network in Johor using geostatistics and simulated annealing

2015 ◽  
Author(s):  
Mohd Khairul Bazli Mohd Aziz ◽  
Fadhilah Yusof ◽  
Zalina Mohd Daud ◽  
Zulkifli Yusop ◽  
Mohammad Afif Kasno
Keyword(s):  
Simulated Annealing ◽  
Rain Gauges ◽  
Rain Gauges Network

A dense network of rain gauges within an urban area: Rainfall uncertainty and variability

2021 ◽  
Author(s):  
Luc Neppel ◽  
Pierre Marchand ◽  
Pascal Finaud-Guyot ◽  
Vincent Guinot ◽  
Christian Salles
Keyword(s):  
Spatial Variability ◽  
Urban Area ◽  
Urban Areas ◽  
Rainfall Variability ◽  
Temporal Structure ◽  
Drainage System ◽  
Spatiotemporal Variability ◽  
Small Scale ◽  
Rain Gauges ◽  
Rain Gauges Network

<p>This study presents a new high density rain gauges network installed in urban area to study spatio-temporal structure and variability of precipitation at small scales. The preliminary results concerning gauges calibration and characterization of the rainfall spatial variability at fine scale are discussed.</p><p>In urban areas, the impervious surfaces connected to the drainage system leads to highly dynamic flows. The flood and runoff risk characterization requires  fine spatiotemporal scale to describe hydrological model input data :rainfall within spatial scale of less than 1km and temporal scale close to 1minis necessary for urban hydrological applications and risk assessment. In order to characterize small-scale rainfall spatiotemporal variability, a dense rain gauges network is deployed at Montpellier (France) with inter-gauges distances from 100m to 1km. Currently, 9 tipping bucket rain gauges  associated with 9 anemometers are acquiring rainfall and wind norm intensity every minutes. The network density and extension will be increased soon.</p><p>The first year measurements highlight a spatial variability of the 1-minute rainfall at the subkilometer scale. This observed variability is analyzed in view of the measurement uncertainty (gauge calibration, gauge error, bias due to the gauge location) to identify the natural rainfall variability.</p><p>This contribution presents the new densely extensive rainfall  network measurement, the typing bucket raingauge calibration and highlights that the observed 1-minute rainfall intensity variability  is significant and cannot be only explained by the measurement uncertainties.</p>

Bias introduced in rainfall risk assessment by a rain gauges network

1998 ◽  
Vol 37 (11) ◽  
pp. 97-104
Author(s):  
L. Neppel ◽  
M. Desbordes ◽  
J. M. Masson
Keyword(s):  
Risk Assessment ◽  
Frequency Analysis ◽  
Return Period ◽  
Return Periods ◽  
Rainfall Events ◽  
Rain Gauges ◽  
Spatial Approach ◽  
Rainfall Depth ◽  
Rain Gauges Network

When large periods of observation are considered, the densest information are often a collection of the daily rain gauges network. As this information is scattered in space, the stochastic results and specially the rainfall risk assessment, are biased because of the rainfall events that are not ‘observed’ by the network. Rainfall risk can be assessed using a punctual approach with the estimation of regional return period of a punctual rainfall depth exceeding a given value, or using a spatial approach with the frequency analysis of the areas of isohyets defined at a given rain threshold τ. This last approach consists, for a given τ, in estimating the return period of isohyet areas. Using simulation, a method of unbiased rainfall risk assessment is proposed for the Languedoc-Roussillon region (France). It has been shown that the bias influence is negligible for the regional return periods of isohyet areas, for 24-hour and 48-hour duration, when compared to their confident limits. On the contrary the return periods of punctual rainfall depths above a given value are more sensitive: for values above 170 mm/24h and 270 mm/48h, the biased return periods could be up to 3 times overestimated.

scholarly journals THE STORMS IN RIO DE JANEIRO CITY IN APRIL 2010

Engevista ◽  
2011 ◽  
Vol 13 (1) ◽  
Author(s):  
Fabio Gomes Coelho ◽  
Mônica De Aquino G. Massera da Hora ◽  
Antonio Ferreira da Hora
Keyword(s):  
Rio De Janeiro ◽  
High Tide ◽  
Daily Maximum ◽  
Historical Records ◽  
Maximum Rainfall ◽  
Rain Gauges ◽  
Rain Gauges Network ◽  
Official Information ◽  
Janeiro City

The Rio de Janeiro City suffered great floods due to the storms that took place between April5th and 6th 2010. According to the official information, the total accumulated in 24 hours was 278mm(considered the worst flood in 44 years, since 1966) and the high tide also contributed to the 60 floodingpoints verified in the whole city. The purpose of the present work is to compare the daily maximum rainfall(P1day) available in Agência Nacional de Águas (ANA) database with the data from the Fundação deGeotécnica da Cidade do Rio de Janeiro (GEO-RIO) rain gauges network and historical records. Theprecipitation gauges registered in ANA and selected for the study were SABÓIA LIMA; IRAJÁ; PRAÇAXV and JARDIM BOTÂNICO and the P1day values found were, respectively, 330.9mm (02/26/1971);339.6mm (02/26/1971); 237.0mm (01/11/1966); and, 242.6mm (01/11/1966). The selected stationsfrom GEO-RIO were TIJUCA; SAÚDE and JARDIM BOTÂNICO and the P1day values found were,respectively, 259.9mm (01/08/1998); 160.8mm (10/24/2007); and 239.0mm (04/06/2010). For the samedays, is also presented an analysis of the recorded tides, observed by Diretoria de Hidrografia e Navegação– DHN, which is a Brazilian navy organization, as a complement to understand the phenomenon. Inconclusion, it was possible to infer that April 2010 event was overcome twice in the last 40 years (February,1971 and January, 1998).

Penjadwalan Produksi Menggunakan Metode Simulated Annealing pada Unit Produksi Daun Pintu di PT. ML

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Rosnani Ginting ◽  
Chairul Rahmadsyah Manik
Keyword(s):  
Simulated Annealing ◽  
Problem Solving ◽  
Production Scheduling ◽  
Production Control ◽  
Delivery Time ◽  
Production Schedule ◽  
A Company ◽  
Predetermined Time ◽  
Factory Floor

Penjadwalan merupakan aspek yang sangat penting karena didalamnya terdapat elemen perencanaan dan pengendalian produksi bagi suatu perusahaan yang dapat mengirim barang sesuai dengan waktu yang telah ditentukan, untuk memperoleh waktu total penyelesaian yang minimum. Masalah utama yang dihadapi oleh PT. ML adalah keterlambatan penyelesaian order yang mempengaruhi delivery time ke tangan costumer karena pelaksanaan penjadwalan produksi dilantai pabrik belum menghasilkan makespan yang sesuai dengan order yang ada. Oleh kaena itu dituntut untuk mencari solusi pemecahan masalah optimal dalam penentuan jadwal produksi untuk meminimisasi total waktu penyelessaian (makespan) semua order. Dalam penelitian ini, penjadwalan menggunakan metode Simulated Annealing (SA) diharapkan dapat menghasilkan waktu total penyelesaian lebih cepat dari penjadwalan yang ada pada perusahaan.   Scheduling is a very important aspect because in it there are elements of planning and production control for a company that can send goods in accordance with a predetermined time, to obtain a minimum total time of completion. The main problem faced by PT. ML is the delay in completing orders that affect delivery time to customer because the implementation of production scheduling on the factory floor has not produced the makespan that matches the existing order. Therefore, it is required to find optimal problem solving solutions in determining the production schedule to minimize the total time of elimination (makespan) of all orders. In this study, scheduling using the Simulated Annealing (SA) method is expected to produce a total time of completion faster than the existing scheduling in the company.

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