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Surface runoff estimation and prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA and distributed-GIS-based SCS-CN models at tropical region

The integration of precipitation intensity and LULC forecasting have played a significant role in prospect surface runoff, allowing for an extension of the lead time that enables a more timely implementation of the control measures. The current study proposes a full-package model to monitor the chan...

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Main Authors: Rizeei, Hossein Mojaddadi, Pradhan, Biswajeet, Saharkhiz, Maryam Adel
Format: Conference or Workshop Item
Language:English
Published: Springer Nature Singapore 2017
Online Access:http://psasir.upm.edu.my/id/eprint/64625/1/Surface%20runoff%20estimation%20and%20prediction%20regarding%20LULC%20and%20climate%20dynamics%20using%20coupled%20LTM%2C%20optimized%20ARIMA%20and%20distributed-GIS-based%20SCS-CN%20models%20at%20tropical%20region.pdf
http://psasir.upm.edu.my/id/eprint/64625/
https://link.springer.com/chapter/10.1007/978-981-10-8016-6_78
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spelling my.upm.eprints.646252018-08-13T03:13:34Z http://psasir.upm.edu.my/id/eprint/64625/ Surface runoff estimation and prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA and distributed-GIS-based SCS-CN models at tropical region Rizeei, Hossein Mojaddadi Pradhan, Biswajeet Saharkhiz, Maryam Adel The integration of precipitation intensity and LULC forecasting have played a significant role in prospect surface runoff, allowing for an extension of the lead time that enables a more timely implementation of the control measures. The current study proposes a full-package model to monitor the changes in surface runoff in addition to forecasting the future surface runoff based on LULC and precipitation factors. On one hand, six different LULC classes from Spot-5 satellite image were extracted by object-based Support Vector Machine (SVM) classifier. Conjointly, Land Transformation Model (LTM) was used to detect the LULC pixel changes from 2000 to 2010 as well as predict the 2020. On the other hand, ARIMA model was applied to the analysis and forecasting the rainfall trends. The parameters of ARIMA time series model were calibrated and fitted statistically to minimize the prediction uncertainty by latest Taguchi method. Rainfall and streamflow data recorded in eight nearby gauging stations were engaged to train, forecast, and calibrate the climate hydrological models. Then, distributed-GIS-based SCS-CN model was applied to simulate the maximum probable surface runoff for 2000, 2010, and 2020. The comparison results showed that first, deforestation and urbanization have occurred upon the given time and it is anticipated to increase as well. Second, the amount of rainfall has been nonstationary declined till 2015 and this trend is estimated to continue till 2020. Third, due to the damaging changes in LULC and climate, the surface runoff has also increased till 2010 and it is forecasted to gradually exceed. Springer Nature Singapore 2017 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/64625/1/Surface%20runoff%20estimation%20and%20prediction%20regarding%20LULC%20and%20climate%20dynamics%20using%20coupled%20LTM%2C%20optimized%20ARIMA%20and%20distributed-GIS-based%20SCS-CN%20models%20at%20tropical%20region.pdf Rizeei, Hossein Mojaddadi and Pradhan, Biswajeet and Saharkhiz, Maryam Adel (2017) Surface runoff estimation and prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA and distributed-GIS-based SCS-CN models at tropical region. In: Global Civil Engineering Conference (GCEC 2017), 25-28 July 2017, Kuala Lumpur, Malaysia. (pp. 1103-1126). https://link.springer.com/chapter/10.1007/978-981-10-8016-6_78 10.1007/978-981-10-8016-6_78
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description The integration of precipitation intensity and LULC forecasting have played a significant role in prospect surface runoff, allowing for an extension of the lead time that enables a more timely implementation of the control measures. The current study proposes a full-package model to monitor the changes in surface runoff in addition to forecasting the future surface runoff based on LULC and precipitation factors. On one hand, six different LULC classes from Spot-5 satellite image were extracted by object-based Support Vector Machine (SVM) classifier. Conjointly, Land Transformation Model (LTM) was used to detect the LULC pixel changes from 2000 to 2010 as well as predict the 2020. On the other hand, ARIMA model was applied to the analysis and forecasting the rainfall trends. The parameters of ARIMA time series model were calibrated and fitted statistically to minimize the prediction uncertainty by latest Taguchi method. Rainfall and streamflow data recorded in eight nearby gauging stations were engaged to train, forecast, and calibrate the climate hydrological models. Then, distributed-GIS-based SCS-CN model was applied to simulate the maximum probable surface runoff for 2000, 2010, and 2020. The comparison results showed that first, deforestation and urbanization have occurred upon the given time and it is anticipated to increase as well. Second, the amount of rainfall has been nonstationary declined till 2015 and this trend is estimated to continue till 2020. Third, due to the damaging changes in LULC and climate, the surface runoff has also increased till 2010 and it is forecasted to gradually exceed.
format Conference or Workshop Item
author Rizeei, Hossein Mojaddadi
Pradhan, Biswajeet
Saharkhiz, Maryam Adel
spellingShingle Rizeei, Hossein Mojaddadi
Pradhan, Biswajeet
Saharkhiz, Maryam Adel
Surface runoff estimation and prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA and distributed-GIS-based SCS-CN models at tropical region
author_facet Rizeei, Hossein Mojaddadi
Pradhan, Biswajeet
Saharkhiz, Maryam Adel
author_sort Rizeei, Hossein Mojaddadi
title Surface runoff estimation and prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA and distributed-GIS-based SCS-CN models at tropical region
title_short Surface runoff estimation and prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA and distributed-GIS-based SCS-CN models at tropical region
title_full Surface runoff estimation and prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA and distributed-GIS-based SCS-CN models at tropical region
title_fullStr Surface runoff estimation and prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA and distributed-GIS-based SCS-CN models at tropical region
title_full_unstemmed Surface runoff estimation and prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA and distributed-GIS-based SCS-CN models at tropical region
title_sort surface runoff estimation and prediction regarding lulc and climate dynamics using coupled ltm, optimized arima and distributed-gis-based scs-cn models at tropical region
publisher Springer Nature Singapore
publishDate 2017
url http://psasir.upm.edu.my/id/eprint/64625/1/Surface%20runoff%20estimation%20and%20prediction%20regarding%20LULC%20and%20climate%20dynamics%20using%20coupled%20LTM%2C%20optimized%20ARIMA%20and%20distributed-GIS-based%20SCS-CN%20models%20at%20tropical%20region.pdf
http://psasir.upm.edu.my/id/eprint/64625/
https://link.springer.com/chapter/10.1007/978-981-10-8016-6_78
_version_ 1643838077287792640
score 13.211869

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