Optimization of Monitoring Network to the Rainfall Distribution by Using Stochastic Search Algorithms: Lesson from Pakistan

Agricultural production is greatly influenced by environmental parameters such as temperature, rainfall, humidity, and wind speed. The accurate information about environmental parameters plays a vital and useful role when making policies for the agriculture sector as well as for other sectors. Pakis...

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Main Authors: Talha Omer, Mahmood Ul Hassan, Ijaz Hussain, Maryam Ilyas, Syed Ghulam Mohayud Din Hashmi, Yousaf Ali Khan
Format: Article
Language:English
Published: Stockholm University Press 2022-08-01
Series:Tellus: Series A, Dynamic Meteorology and Oceanography
Subjects:
Online Access:https://a.tellusjournals.se/articles/247
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author Talha Omer
Mahmood Ul Hassan
Ijaz Hussain
Maryam Ilyas
Syed Ghulam Mohayud Din Hashmi
Yousaf Ali Khan
author_facet Talha Omer
Mahmood Ul Hassan
Ijaz Hussain
Maryam Ilyas
Syed Ghulam Mohayud Din Hashmi
Yousaf Ali Khan
author_sort Talha Omer
collection DOAJ
description Agricultural production is greatly influenced by environmental parameters such as temperature, rainfall, humidity, and wind speed. The accurate information about environmental parameters plays a vital and useful role when making policies for the agriculture sector as well as for other sectors. Pakistan meteorological department observed these environmental parameters at more than 90 stations. The allocation of these monitoring stations is not made systematically correct. This leads to inaccurate predictions for unobserved locations. The study aims to propose a monitoring network by which these prediction errors of the environmental parameters can be minimized. The well-known prediction techniques named, model-based ordinary kriging and model-based universal kriging (UK) with the known Matheron variogram model are used for prediction purposes. We investigate the monitoring network of Pakistan for rainfall and focus on both the optimal deletion/addition of monitoring stations from/to this network. The two stochastic search algorithms, spatial simulated annealing, and genetic algorithm are used for optimization purposes. Furthermore, the minimization of the Average Kriging Variance (AKV) is taken as the interpolation accuracy measure. The spatial simulated annealing exhibits a lower AKV as compared to the Genetic algorithm when adding/removing the optimal/redundant locations from the monitoring network.
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spelling doaj.art-a77e0865a13746edae4f824799f49a162022-12-22T03:31:09ZengStockholm University PressTellus: Series A, Dynamic Meteorology and Oceanography1600-08702022-08-0174110.16993/tellusa.247240Optimization of Monitoring Network to the Rainfall Distribution by Using Stochastic Search Algorithms: Lesson from PakistanTalha Omer0Mahmood Ul Hassan1Ijaz Hussain2Maryam Ilyas3Syed Ghulam Mohayud Din Hashmi4Yousaf Ali Khan5Department of Economics, Finance and Statistics, JIBS, Jönköping University, SE; Department of Statistics and Computer Science, University of Veterinary and Animal Sciences, UVAS, LahoreDepartment of Statistics, Stockholm University, StockholmDepartment of Statistics, Quaid-I-Azam University, IslamabadCollege of Statistical and Actuarial Sciences, University of the Punjab, LahoreDepartment of Wildlife and Ecology, University of Veterinary and Animal Sciences, LahoreDepartment of Mathematics and Statistics, Hazara University, MansehraAgricultural production is greatly influenced by environmental parameters such as temperature, rainfall, humidity, and wind speed. The accurate information about environmental parameters plays a vital and useful role when making policies for the agriculture sector as well as for other sectors. Pakistan meteorological department observed these environmental parameters at more than 90 stations. The allocation of these monitoring stations is not made systematically correct. This leads to inaccurate predictions for unobserved locations. The study aims to propose a monitoring network by which these prediction errors of the environmental parameters can be minimized. The well-known prediction techniques named, model-based ordinary kriging and model-based universal kriging (UK) with the known Matheron variogram model are used for prediction purposes. We investigate the monitoring network of Pakistan for rainfall and focus on both the optimal deletion/addition of monitoring stations from/to this network. The two stochastic search algorithms, spatial simulated annealing, and genetic algorithm are used for optimization purposes. Furthermore, the minimization of the Average Kriging Variance (AKV) is taken as the interpolation accuracy measure. The spatial simulated annealing exhibits a lower AKV as compared to the Genetic algorithm when adding/removing the optimal/redundant locations from the monitoring network.https://a.tellusjournals.se/articles/247environmental parametersvariogramgenetic algorithmsspatial simulated annealingaverage kriging variance
spellingShingle Talha Omer
Mahmood Ul Hassan
Ijaz Hussain
Maryam Ilyas
Syed Ghulam Mohayud Din Hashmi
Yousaf Ali Khan
Optimization of Monitoring Network to the Rainfall Distribution by Using Stochastic Search Algorithms: Lesson from Pakistan
Tellus: Series A, Dynamic Meteorology and Oceanography
environmental parameters
variogram
genetic algorithms
spatial simulated annealing
average kriging variance
title Optimization of Monitoring Network to the Rainfall Distribution by Using Stochastic Search Algorithms: Lesson from Pakistan
title_full Optimization of Monitoring Network to the Rainfall Distribution by Using Stochastic Search Algorithms: Lesson from Pakistan
title_fullStr Optimization of Monitoring Network to the Rainfall Distribution by Using Stochastic Search Algorithms: Lesson from Pakistan
title_full_unstemmed Optimization of Monitoring Network to the Rainfall Distribution by Using Stochastic Search Algorithms: Lesson from Pakistan
title_short Optimization of Monitoring Network to the Rainfall Distribution by Using Stochastic Search Algorithms: Lesson from Pakistan
title_sort optimization of monitoring network to the rainfall distribution by using stochastic search algorithms lesson from pakistan
topic environmental parameters
variogram
genetic algorithms
spatial simulated annealing
average kriging variance
url https://a.tellusjournals.se/articles/247
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