Modeling spatially non-stationary land use/cover change in the lower Connecticut River Basin by combining geographically weighted logistic regression and the CA-Markov model

2019 ◽  
Vol 33 (7) ◽  
pp. 1313-1334 ◽  
Author(s):  
Hui Wang ◽  
Scott R. Stephenson ◽  
Shijin Qu
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Markov Model ◽  
River Basin ◽  
Connecticut River ◽  
Weighted Logistic Regression ◽  
Geographically Weighted Logistic Regression

scholarly journals Historic and Simulated Desert-Oasis Ecotone Changes in the Arid Tarim River Basin, China

2021 ◽  
Vol 13 (4) ◽  
pp. 647
Author(s):  
Fan Sun ◽  
Yi Wang ◽  
Yaning Chen ◽  
Yupeng Li ◽  
Qifei Zhang ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Markov Model ◽  
River Basin ◽  
Arable Land ◽  
Current Trend ◽  
Ecological Crisis ◽  
Tarim River ◽  
Tarim River Basin ◽  
Desert Oasis

The desert-oasis ecotone, as a crucial natural barrier, maintains the stability of oasis agricultural production and protects oasis habitat security. This paper investigates the dynamic evolution of the desert-oasis ecotone in the Tarim River Basin and predicts the near-future land-use change in the desert-oasis ecotone using the cellular automata–Markov (CA-Markov) model. Results indicate that the overall area of the desert-oasis ecotone shows a shrinking trend (from 67,642 km2 in 1990 to 46,613 km2 in 2015) and the land-use change within the desert-oasis ecotone is mainly manifested by the conversion of a large amount of forest and grass area into arable land. The increasing demand for arable land for groundwater has led to a decline in the groundwater level, which is an important reason for the habitat deterioration in the desert-oasis ecotone. The rising temperature and drought have further exacerbated this trend. Assuming the current trend in development without intervention, the CA-Markov model predicts that by 2030, there will be an additional 1566 km2 of arable land and a reduction of 1151 km2 in forested area and grassland within the desert-oasis ecotone, which will inevitably further weaken the ecological barrier role of the desert-oasis ecotone and trigger a growing ecological crisis.

scholarly journals Model Regresi Logistik Terboboti Georafis pada Status Kemiskinan Kabupaten/Kota di Provinsi Sulawesi Selatan tahun 2016

2019 ◽  
Vol 1 (3) ◽  
pp. 87
Author(s):  
Sadriana Rustan ◽  
Muhammad Arif Tiro ◽  
Muhammad Nadjib Bustan
Keyword(s):  
Logistic Regression ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Geographical Location ◽  
Predictor Variable ◽  
Likelihood Estimation ◽  
Local Form ◽  
South Sulawesi ◽  
Weighted Logistic Regression ◽  
Geographically Weighted Logistic Regression

Abstrak. Analisis regresi logistik digunakan untuk menentukan hubungan antara peubah respon bersifat kategori dengan satu atau lebih peubah penjelas dengan asumsi bahwa respon tidak dipengaruhi oleh lokasi geografis (data spasial). Salah satu metode analisis spasial adalah Model Regresi Logistik Terboboti Geografis (RLTG). Model RLTG adalah bentuk regresi logistik lokal di mana lokasi geografis diperhatikan dan diasumsikan memiliki distribusi Bernoulli. Pendugaan parameter model RLTG menggunakan metode Maximum Likelihood Estimation (MLE) dengan memberikan bobot yang berbeda pada lokasi yang berbeda. Data dalam penelitian ini diperoleh dari publikasi Badan Pusat Statistik, yaitu data dan Informasi Kemiskinan di Provinsi Sulawesi Selatan. Penelitian ini bertujuan untuk mengetahui faktor-faktor yang mempengaruhi status kemiskinan di Provinsi Sulawesi Selatan dengan menggunakan model regresi logistik terboboti geografis dengan fungsi pembobot Kernel bisquare. Hasil penelitian menunjukkan bahwa peubah penjelas yang mempengaruhi status kemiskinan di Provinsi Sulawesi Selatan adalah persentase penduduk tidak bekerja dan persentase rumah tangga pengguna jamban bersama.Abstract. Logistic regression a analysis is used to determine the relationship between categorical response variables with one or more predictor variable assuming that the response is not influenced by geographical location (spatial data). One method of spatial analysis is Geographically Weighted Logistic Regression (GWLR). The GWLR model is a local form of logistic regression where the geographical location is considered and assumed to have a Bernoulli distribution. Estimating parameters of the RLTG model uses the Maximum Likelihood Estimation (MLE) method by giving different weights to different locations. The data were obtained from BPS publications, namely Data and Information on Poverty in South Sulawesi Province. This study aims to determine the factors that influence poverty status in South Sulawesi Province using a geographically weighted logistic regression model with kernel bisquare weighting function. The results showed that the explanatory variables that influence the status of poverty in the province of South Sulawesi were the percentage of the population not working and the percentage of common household toilet users.Keywords: logistic regression, kernel bisquare, GWLR and poverty.

scholarly journals Assessing the impacts of hydrologic and land use alterations on water temperature in the Farmington River basin in Connecticut

2019 ◽  
Vol 23 (11) ◽  
pp. 4491-4508 ◽  
Author(s):  
John R. Yearsley ◽  
Ning Sun ◽  
Marisa Baptiste ◽  
Bart Nijssen
Keyword(s):  
Land Use ◽  
Water Temperature ◽  
River Basin ◽  
Land Surface ◽  
Canopy Cover ◽  
River System ◽  
Stream Temperature ◽  
Hydrologic Model ◽  
Riparian Buffers ◽  
Connecticut River

Abstract. Aquatic ecosystems can be significantly altered by the construction of dams and modification of riparian buffers, and the effects are often reflected in spatial and temporal changes to water temperature. To investigate the implications for water temperature of spatially and temporally varying riparian buffers and dam-induced hydrologic alterations, we have implemented a modeling system (DHSVM-RBM) within the framework of the state-space paradigm that couples a spatially distributed land surface hydrologic model, DHSVM, with the distributed stream temperature model, RBM. The basic modeling system has been applied previously to several similar-sized watersheds. However, we have made enhancements to DHSVM-RBM that simulate spatial heterogeneity and temporal variation (i.e., seasonal changes in canopy cover) in riparian vegetation, and we included additional features in DHSVM-RBM that provide the capability for simulating the impacts of reservoirs that may develop thermal stratification. We have tested the modeling system in the Farmington River basin in the Connecticut River system, which includes varying types of watershed development (e.g., deforestation and reservoirs) that can alter the streams' hydrologic regime and thermal energy budget. We evaluated streamflow and stream temperature simulations against all available observations distributed along the Farmington River basin. Results based on metrics recommended for model evaluation compare well to those obtained in similar studies. We demonstrate the way in which the model system can provide decision support for watershed planning by simulating a limited number of scenarios associated with hydrologic and land use alterations.

scholarly journals Assessing the Impacts of Hydrologic and Land Use Alterations on Water Temperature in the Farmington River Basin in Connecticut

2019 ◽  
Author(s):  
John R. Yearsley ◽  
Ning Sun ◽  
Marisa Baptiste ◽  
Bart Nijssen
Keyword(s):  
Land Use ◽  
Water Temperature ◽  
River Basin ◽  
Land Surface ◽  
Canopy Cover ◽  
River System ◽  
Stream Temperature ◽  
Hydrologic Model ◽  
Riparian Buffers ◽  
Connecticut River

Abstract. Aquatic ecosystems can be significantly altered by the construction of dams and modification of riparian buffers and the effects are often reflected in spatial and temporal changes to water temperature. To investigate the implications for water temperature of spatially and temporally varying riparian buffers and dam-induced hydrologic alterations, we have implemented a modeling system (DHSVM-RBM) that couples a spatially distributed land surface hydrologic model, DHSVM, with the distributed stream temperature model, RBM. The basic modeling system has been applied previously to several similar-sized watersheds. However, we have made enhancements to DHSVM-RBM that simulate spatial heterogeneity and temporal variation (i.e. seasonal changes in canopy cover) in riparian vegetation, and we included additional features in DHSVM-RBM that provide the capability for simulating the impacts of reservoirs that may develop thermal stratification. We have tested the modeling system in the Farmington River basin in the Connecticut River system that includes varying types of watershed development (e.g. deforestation and reservoirs) that can alter the streams’ hydrologic regime and thermal energy budget. We evaluated streamflow and stream temperature simulations against all available observations distributed along the Farmington River basin. Results based on metrics recommended for model evaluation compare well to those obtained in similar studies. We demonstrate the way in which the model system can provide decision support for watershed planning by simulating a limited number of scenarios associated with hydrologic and land use alterations.

Comparing the application of logistic and geographically weighted logistic regression models for Fujian forest fire forecasting

2017 ◽  
Vol 37 (12) ◽  
Author(s):  
梁慧玲 LIANG Huiling ◽  
王文辉 WANG Wenhui ◽  
郭福涛 GUO Futao ◽  
林芳芳 LIN Fangfang ◽  
林玉蕊 LIN Yurui
Keyword(s):  
Logistic Regression ◽  
Forest Fire ◽  
Regression Models ◽  
Logistic Regression Models ◽  
Weighted Logistic Regression ◽  
Geographically Weighted Logistic Regression
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