Abatzoglou JT, Dobrowski SZ, Parks SA, Hegewisch KC (2018) TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015 Scientific data 5:1-12
Adeboye N, Fagoyinbo I, Olatayo T (2014) Estimation of the effect of multicollinearity on the standard error for regression coefficients Journal of Mathematics 10:16-20
Al-Khalidi J, Bakr D, Abdullah AA (2021) Synoptic Analysis of Dust Storm in Iraq EnvironmentAsia 14
Almasi H et al. (2020) Spatial distribution, ecological and health risk assessment and source identification of atrazine in Shadegan international wetland, Iran Marine Pollution Bulletin 160:111569
Amare S, Langendoen E, Keesstra S, Ploeg Mvd, Gelagay H, Lemma H, van der Zee SE (2021) Susceptibility to Gully Erosion: Applying Random Forest (RF) and Frequency Ratio (FR) Approaches to a Small Catchment in Ethiopia Water 13:216
Amiri M, Pourghasemi HR, Ghanbarian GA, Afzali SF (2019) Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modeling and mapping using three machine learning algorithms Geoderma 340:55-69
Arcusa SH, McKay NP, Routson CC, Munoz SE (2020) Dust-drought interactions over the last 15,000 years: A network of lake sediment records from the San Juan Mountains, Colorado The Holocene 30:559-574
Arjmand M, Rashki A, Sargazi H (2018) Monitoring of spatial and temporal variability of desert dust over the Hamoun e Jazmurian, Southeast of Iran based on the Satellite Data Scientific-Research Quarterly of Geographical Data (SEPEHR) 27:153-168
Ashayeri NY, Keshavarzi B (2019) Geochemical characteristics, partitioning, quantitative source apportionment, and ecological and health risk of heavy metals in sediments and water: A case study in Shadegan Wetland, Iran Marine pollution bulletin 149:110495
Ayanlade A, Proske U (2016) Assessing wetland degradation and loss of ecosystem services in the Niger Delta, Nigeria Marine and Freshwater Research 67:828-836
Baltaci H (2021) Meteorological characteristics of dust storm events in Turkey Aeolian Research 50:100673
Bansal A, Kaur S Extreme gradient boosting based tuning for classification in intrusion detection systems. In: International Conference on Advances in Computing and Data Sciences, 2018. Springer, pp 372-380
Bayat R, Jafari S, Ghermezcheshmeh B, Charkhabi A (2016) Studying the effect of dust on vegetation changes (case study: Shadegan Wetland, Khuzestan)
Cao C et al. (2012) Wetland changes and droughts in southwestern China Geomatics, Natural Hazards and Risk 3:79-95
Chatterjee K, Bandyopadhyay A, Ghosh A, Kar S (2015) Assessment of environmental factors causing wetland degradation, using Fuzzy Analytic Network Process: A case study on Keoladeo National Park, India Ecological Modelling 316:1-13
Chen T, Guestrin C Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016. pp 785-794
Chen W, Lei X, Chakrabortty R, Pal SC, Sahana M, Janizadeh S (2021) Evaluation of different boosting ensemble machine learning models and novel deep learning and boosting framework for head-cut gully erosion susceptibility Journal of Environmental Management 284:112015
Chen Z-Y et al. (2019) Extreme gradient boosting model to estimate PM2. 5 concentrations with missing-filled satellite data in China Atmospheric Environment 202:180-189
Dahmardeh M (2016) Assessment of drought damage of Hamoun wetland on health condition of inhabitants of Sistan region World Review of Science, Technology and Sustainable Development 12:335-352
Davidson NC (2014) How much wetland has the world lost? Long-term and recent trends in global wetland area Marine and Freshwater Research 65:934-941
Ebrahimi-Khusfi Z, Ghazavi R, Zarei M (2020a) The Effect of Climate Changes on the Wetland Moisture Variations and Its Correlation with Sand-Dust Events in a Semiarid Environment, Northwestern Iran Journal of the Indian Society of Remote Sensing 48:1797-1808
Ebrahimi-Khusfi Z, Taghizadeh-Mehrjardi R, Mirakbari M (2020b) Evaluation of machine learning models for predicting the temporal variations of dust storm index in arid regions of Iran Atmospheric Pollution Research
Ebrahimi-Khusfi Z, Taghizadeh-Mehrjardi R, Nafarzadegan AR (2020c) Accuracy, uncertainty, and interpretability assessments of ANFIS models to predict dust concentration in semi-arid regions Environmental Science and Pollution Research:1-15
Eppink FV, Brander LM, Wagtendonk AJ (2014) An initial assessment of the economic value of coastal and freshwater wetlands in West Asia Land 3:557-573
Gebresllassie H, Gashaw T, Mehari A (2014) Wetland degradation in Ethiopia: causes, consequences and remedies Journal of Environment and Earth Science 4:40-48
Ghanian M, Bakhshi A, YUSEFI HR, Hasheminejad A (2015) Neural network analysis to predict factors affecting conservation behavior of rural operators of Shadegan Wetland
Gholami H, Mohamadifar A, Sorooshian A, Jansen JD (2020) Machine-learning algorithms for predicting land susceptibility to dust emissions: The case of the Jazmurian Basin, Iran Atmospheric Pollution Research 11:1303-1315
Ghorbani R, Hosseini S, Hedayati S, Hashemi S, Abolhasani M (2016) Evaluation of effects of physico-chemical factors on chlorophyll-a in Shadegan International Wetland-Khouzestan Province-Iran Iranian Journal of Fisheries Sciences 15:360-368
Gilpin LH, Bau D, Yuan BZ, Bajwa A, Specter M, Kagal L Explaining explanations: An overview of interpretability of machine learning. In: 2018 IEEE 5th International Conference on data science and advanced analytics (DSAA), 2018. IEEE, pp 80-89
Gokce D (2018) Introductory Chapter: Wetland Importance and Management. In: Wetlands Management-Assessing Risk and Sustainable Solutions. IntechOpen,
Gu J, Yang B, Brauer M, Zhang KM (2021) Enhancing the Evaluation and Interpretability of Data-Driven Air Quality Models Atmospheric Environment 246:118125
Han T, Pan X, Wang X (2021) Evaluating and improving the sand storm numerical simulation performance in Northwestern China using WRF-Chem and remote sensing soil moisture data Atmospheric Research 251:105411
Hassanien AE, Salem A-BM, Ramadan R, Kim T-h (2012) Advanced Machine Learning Technologies and Applications: First International Conference, AMLTA 2012, Cairo, Egypt, December 8-10, 2012, Proceedings vol 322. Springer,
Jia M, Mao D, Wang Z, Ren C, Zhu Q, Li X, Zhang Y (2020) Tracking long-term floodplain wetland changes: A case study in the China side of the Amur River Basin International Journal of Applied Earth Observation and Geoinformation 92:102185
Jiang W, Lv J, Wang C, Chen Z, Liu Y (2017) Marsh wetland degradation risk assessment and change analysis: A case study in the Zoige Plateau, China Ecological Indicators 82:316-326
Kandakji T, Gill TE, Lee JA (2021) Drought and land use/land cover impact on dust sources in Southern Great Plains and Chihuahuan Desert of the US: Inferring anthropogenic effect Science of The Total Environment 755:142461
Kaur H, Nori H, Jenkins S, Caruana R, Wallach H, Wortman Vaughan J Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 2020. pp 1-14
Khusfi Z, Vali A, Khosroshahi M, Ghazavi R (2017) The role of dried bed of Gavkhooni wetland on the production of the internal dust using remote sensing and storm roses (case study: Isfahan province) Iranian Journal of Range and Desert Research 24
Knapp AK et al. (2020) Resolving the Dust Bowl paradox of grassland responses to extreme drought Proceedings of the National Academy of Sciences 117:22249-22255
Kong G, Lin K, Hu Y (2020) Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU BMC Medical Informatics and Decision Making 20:1-10
Li J, Garshick E, Al-Hemoud A, Huang S, Koutrakis P (2020) Impacts of meteorology and vegetation on surface dust concentrations in Middle Eastern countries Science of The Total Environment 712:136597
Lundberg S, Lee S-I (2017) A unified approach to interpreting model predictions arXiv preprint arXiv:170507874
Ma J, Yu Z, Qu Y, Xu J, Cao Y (2020) Application of the XGBoost machine learning method in PM2. 5 prediction: A case study of Shanghai Aerosol and Air Quality Research 20:128-138
Martins VS, Kaleita AL, Gelder BK, Nagel GW, Maciel DA (2020) Deep neural network for complex open-water wetland mapping using high-resolution WorldView-3 and airborne LiDAR data International Journal of Applied Earth Observation and Geoinformation 93:102215
Meng Z, Dang X, Gao Y, Ren X, Ding Y, Wang M (2018) Interactive effects of wind speed, vegetation coverage and soil moisture in controlling wind erosion in a temperate desert steppe, Inner Mongolia of China Journal of Arid Land 10:534-547
Moghanlo S, Alavinejad M, Oskoei V, Saleh HN, Mohammadi AA, Mohammadi H, DerakhshanNejad Z (2021) Using artificial neural networks to model the impacts of climate change on dust phenomenon in the Zanjan region, north-west Iran Urban Climate 35:100750
Naghibi SA, Hashemi H, Berndtsson R, Lee S (2020) Application of extreme gradient boosting and parallel random forest algorithms for assessing groundwater spring potential using DEM-derived factors Journal of Hydrology 589:125197
Natekin A, Knoll A (2013) Gradient boosting machines, a tutorial Frontiers in neurorobotics 7:21
Poornazari N, Khalilimoghadam B, Hazbavi Z, Bagheri Bodaghabadi M (2020) Land degradation assessment in the dust hotspot of southeastern Ahvaz, Iran Land Degradation & Development
Pourghasemi HR, Kariminejad N, Amiri M, Edalat M, Zarafshar M, Blaschke T, Cerda A (2020) Assessing and mapping multi-hazard risk susceptibility using a machine learning technique Scientific reports 10:1-11
Rashki A, Arjmand M, Kaskaoutis D (2017) Assessment of dust activity and dust-plume pathways over Jazmurian Basin, southeast Iran Aeolian Research 24:145-160
Rashki A, Middleton N, Goudie A (2021) Dust storms in Iran–Distribution, causes, frequencies and impacts Aeolian Research 48:100655
Rice JS, Saia SM, Emanuel RE (2020) Improved Accuracy of Watershed-Scale General Circulation Model Runoff Using Deep Neural Networks
Sahin EK (2020) Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest SN Applied Sciences 2:1-17
Salmabadi H, Khalidy R, Saeedi M (2020) Transport routes and potential source regions of the Middle Eastern dust over Ahvaz during 2005–2017 Atmospheric Research 241:104947
Salmabai H, Saeedi M Areal fluctuations monitoring of Al-Azim/Al-Havizeh wetland during the 1986–2017 period, using time-series Landsat data. In: The 2 nd international conference on strategic ideas for architecture urbanism, geography, and the environment, Mashhad, Iran, 2018.
Shabani E, Hayati B, Pishbahar E, Ghorbani MA, Ghahremanzadeh M (2021) A novel approach to predict CO2 emission in the agriculture sector of Iran based on Inclusive Multiple Model Journal of Cleaner Production 279:123708
Shahraki AS, Shahraki J, Monfared SAH (2021) An integrated model for economic assessment of environmental scenarios for dust stabilization and sustainable flora–fauna ecosystem in international Hamoun wetland Environment, Development and Sustainability 23:947-967
Shamsudin MN, Radam A, Rahim KA, Yacob MR, Muda A, Yazid M (2011) Economic valuation of Shadegan International Wetland, Iran: notes for conservation Regional Environmental Change 11:925-934
Sima S, Tajrishy M Water allocation for wetland environmental water requirements: the case of Shadegan Wetland, Jarrahi Catchment, Iran. In: World Environmental and Water Resource Congress 2006: Examining the Confluence of Environmental and Water Concerns, 2006. pp 1-10
Song K, Wang Z, Du J, Liu L, Zeng L, Ren C (2014) Wetland degradation: its driving forces and environmental impacts in the Sanjiang Plain, China Environmental Management 54:255-271
Teng Y, Zhan J, Liu W, Sun Y, Agyemang FB, Liang L, Li Z (2021) Spatiotemporal dynamics and drivers of wind erosion on the Qinghai-Tibet Plateau, China Ecological Indicators 123:107340
Thornthwaite CW, Mather JR (1957) Instructions and tables for computing potential evapotranspiration and the water balance. Centerton,
Vali A, Ebrahimi Z, Khosroshahi M, Ghazavi R (2016) Determination of the importance of hydro-climate parameters on drying in Gavkhooni wetland using artificial neural network and remote sensing data Desert Ecosystem Engineering Journal 5:79-94
Vicente-Serrano SM, Beguería S, López-Moreno JI (2010) A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index Journal of climate 23:1696-1718
Wang L, Shi Z, Wu G, Fang N (2014) Freeze/thaw and soil moisture effects on wind erosion Geomorphology 207:141-148
Wu Q, Ren H, Gao W, Ren J (2017) Benefit allocation for distributed energy network participants applying game theory based solutions Energy 119:384-391
Xu L, Rossel RAV, Lee J, Wang Z, Ma H (2020) A simple approach to estimate coastal soil salinity using digital camera images Soil Research 58:737-747
Youssef AM, Pourghasemi HR (2021) Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia Geoscience Frontiers 12:639-655