Modeling multi-decadal mangrove leaf area index in response to drought along the semi-arid southern coasts of Iran

Mafi-Gholami, Davood; Zenner, Eric K.; Jaafari, Abolfazl; Ward, Raymond D.

doi:10.1016/j.scitotenv.2018.11.462

Cited by 65 publications

(26 citation statements)

References 64 publications

(91 reference statements)

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“…The main step was the development of the models that was conducted in several phases. We first randomly divided the well data (72 locations) into two sets such that one set with 70% of locations (~50) was used for training the models and the remaining locations (~30% = 22 locations) were used for the validation [34,74,[88][89][90]. Regarding the set of influencing factors, we used correlation-based feature selection method [91] to measure the average merit of each factor for mapping the groundwater potential.…”

Section: Modeling Methodologymentioning

confidence: 99%

“…Opposite to rainfall, river density has a reverse relationship with groundwater potential [32] because when the drainage density is lower, the infiltration and recharge are greater [29]. Land use presents the influence of human activities on the landscape evolutions [33][34][35]. Soil type indicates the filtration rate and, therefore, is another important factor for groundwater potential [36,37].…”

Section: Data Usedmentioning

confidence: 99%

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Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Nguyen¹,

Jaafari

et al. 2020

IJERPH

Self Cite

113

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The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.

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Section: Modeling Methodologymentioning

confidence: 99%

Section: Data Usedmentioning

confidence: 99%

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Nguyen¹,

Jaafari

et al. 2020

IJERPH

Self Cite

113

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“…Increasing annual average temperature and decreasing annual precipitation have directly influenced the mangrove ecosystems in the study area. In the last century, the SPI value and annual rainfall have decreased and led to frequent droughts in Qeshm Island as reported in the study by Mafi-Gholami et al (2017) [36]. They investigated the relationship between drought events and mangrove in the study area and found that mangrove areas are significantly related to the reduction in the annual rainfall and occurrence of more drought events.…”

Section: Driving Forces Of Luccmentioning

confidence: 71%

“…Analysis of intensity of changes in the three periods indicated high level of change in dense-vegetation class, attributing to high dependence and direct relation of this class to extent of precipitation and rainfall instability in the arid and ultra-arid climates. This has been especially evident from several droughts in the region as shown in the study by Mafi-Gholami (2017), who showed that the SPI index has been significantly less compared to previous years [36]. Also, the mangrove class showed significant changes compared to other classes.…”

Section: Patterns To Processes In Luccmentioning

confidence: 87%

“…The reduction of the agriculture class in the first period is another remarkable point in the table, which by the way has happened in a small amount (250 pixels in total). Also, the increment in the mangroves level due to protective actions and artificial implantation of mangroves in the first period and also its reduction in the second period, probably due to periods of drought in this region during this period [36] is another important point in the table. In this research, the total accuracy and also the standard Kappa were used to verify the accuracy of classifications.…”

Section: Land Use/cover Mapsmentioning

confidence: 99%

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Use of Intensity Analysis to Characterize Land Use/Cover Change in the Biggest Island of Persian Gulf, Qeshm Island, Iran

et al. 2019

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In this study, land use/cover change was systematically investigated in the Qeshm Island to understand how human and nature interact in the largest island of Persian Gulf. Land-use maps were prepared for 1996, 2002, 2008, and 2014 using Landsat satellite imagery in six classes including agriculture, bare-land, built-up, dense-vegetation, mangrove, and water-body, and then dynamic of changes in the classes was evaluated using intensity analysis at three levels: interval, category, and transition. Results illustrated that, while the land changes were fast over the first and third time intervals (1996-2002 and 2008-2014), the trend of changes was slow in the second period (2002)(2003)(2004)(2005)(2006)(2007)(2008). Driven by high demand for construction and population growth, the built-up class was identified as an active gainer in all the three time intervals. The class of bare-land was the main supplier of the land for other classes especially for built-up area, while built-up did not act as the active supplier of the land for other classes. The dense-vegetation class was active in all three time intervals. As for the mangrove class, drought and cutting by residents had negative effects, while setting up protected areas can effectively maintain this valuable ecosystem. High demands were observed for land change in relation to built-up and agriculture classes among other classes. The findings of this study can advance our understanding of the relationship and behavior of land use/cover classes among each other over 18 years in a coastal island with arid climate.

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Blue Carbon Dynamics in the Indian Ocean Mangrove Ecosystems

Akhand

Chanda

Das

2022

Blue Carbon Dynamics of the Indian Ocean

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Modeling multi-decadal mangrove leaf area index in response to drought along the semi-arid southern coasts of Iran

Cited by 65 publications

References 64 publications

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Use of Intensity Analysis to Characterize Land Use/Cover Change in the Biggest Island of Persian Gulf, Qeshm Island, Iran

Blue Carbon Dynamics in the Indian Ocean Mangrove Ecosystems

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