{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T11:32:30Z","timestamp":1790422350189,"version":"4.1.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:00:00Z","timestamp":1774483200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:00:00Z","timestamp":1774483200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Universit\u00e0 degli Studi di Roma La Sapienza"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Environ Monit Assess"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This study presents a Geospatial Artificial Intelligence (GeoAI) framework for high-resolution Zoonotic Cutaneous Leishmaniasis (ZCL) risk mapping, correlation analysis, and scenario-based projection, integrating geographic information systems (GIS), remote sensing, and neural network architecture. Historical disease maps and multi-temporal satellite-derived environmental layers were jointly modeled using a multilayer perceptron (MLP), two-dimensional convolutional neural networks (2D-CNNs), and three-dimensional CNNs (3D-CNNs). The principal methodological contribution is the implementation of a 3D-CNN, which enables explicit learning of spatiotemporal transmission dynamics. Environmental\u2013disease relationship analyses, based on Pearson coefficients and regression models, identified temperature as the dominant positive environmental driver of ZCL risk. Model performance assessment using root mean square error (RMSE), mean absolute error (MAE), and the area under the receiver operating characteristic curve (AUC) indicates that the 3D-CNN consistently outperforms alternative architectures in capturing complex spatial and temporal patterns. Elevated risk was concentrated in warmer western and southern regions, whereas cooler northern and eastern mountainous areas exhibited lower susceptibility. By 2030, ZCL risk is projected to undergo a spatial shift, with risk decreasing in western regions and intensifying in southern areas, which has direct implications for targeted surveillance and intervention efforts.\u00a0<\/jats:p>","DOI":"10.1007\/s10661-026-15085-8","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T08:59:19Z","timestamp":1774515559000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["GIS-based neural network framework for zoonotic cutaneous leishmaniasis risk mapping in Western Iran"],"prefix":"10.1007","volume":"198","author":[{"given":"Fatemeh","family":"Parto Dezfooli","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Javad","family":"Valadan Zoej","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fahimeh","family":"Youssefi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sudabeh","family":"Alatab","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ebrahim","family":"Ghaderpour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,26]]},"reference":[{"issue":"109","key":"15085_CR1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.22131\/sepehr.2019.35635","volume":"28","author":"M AhangarCani","year":"2019","unstructured":"AhangarCani, M., & Farnaghi, M. (2019). Providing a model for cutaneous leishmaniasis risk mapping using GIS and neural network algorithm. Scientific-Research Quarterly of Geographical Data (SEPEHR), 28(109), 7\u201324. https:\/\/doi.org\/10.22131\/sepehr.2019.35635","journal-title":"Scientific-Research Quarterly of Geographical Data (SEPEHR)"},{"issue":"6","key":"15085_CR2","doi-asserted-by":"publisher","DOI":"10.1088\/2515-7620\/ade6d0","volume":"7","author":"P Ahmadi","year":"2025","unstructured":"Ahmadi, P., Valadan Zoej, M. J., Mokhtarzade, M., Kardan Halvaie, N., & Ghaderpour, E. (2025). A capsule network framework for flood mapping integrating remote sensing fusion techniques. Environmental Research Communications, 7(6), Article 065031. https:\/\/doi.org\/10.1088\/2515-7620\/ade6d0","journal-title":"Environmental Research Communications"},{"issue":"17","key":"15085_CR3","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph18179274","volume":"18","author":"W Barhoumi","year":"2021","unstructured":"Barhoumi, W., Chelbi, I., Fares, W., Zhioua, S., Abbas, M., Derbali, M., & Zhioua, E. (2021). Risk assessment of the role of the ecotones in the transmission of zoonotic cutaneous leishmaniasis in Central Tunisia. International Journal of Environmental Research and Public Health, 18(17), Article 9274. https:\/\/doi.org\/10.3390\/ijerph18179274","journal-title":"International Journal of Environmental Research and Public Health"},{"key":"15085_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2020.111886","volume":"248","author":"AB Barrett","year":"2020","unstructured":"Barrett, A. B., Duivenvoorden, S., Salakpi, E. E., Muthoka, J. M., Mwangi, J., Oliver, S., & Rowhani, P. (2020). Forecasting vegetation condition for drought early warning systems in pastoral communities in Kenya. Remote Sensing of Environment, 248, Article 111886. https:\/\/doi.org\/10.1016\/j.rse.2020.111886","journal-title":"Remote Sensing of Environment"},{"key":"15085_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2024.110513","volume":"253","author":"L Basora","year":"2025","unstructured":"Basora, L., Viens, A., Chao, M. A., & Olive, X. (2025). A benchmark on uncertainty quantification for deep learning prognostics. Reliability Engineering & System Safety, 253, Article 110513. https:\/\/doi.org\/10.1016\/j.ress.2024.110513","journal-title":"Reliability Engineering & System Safety"},{"issue":"13","key":"15085_CR6","doi-asserted-by":"publisher","DOI":"10.3390\/su151310388","volume":"15","author":"H Bouguerra","year":"2023","unstructured":"Bouguerra, H., Tachi, S. E., Bouchehed, H., Gilja, G., Aloui, N., Hasnaoui, Y., & Navarro-Pedre\u00f1o, J. (2023). Integration of high-accuracy geospatial data and machine learning approaches for soil erosion susceptibility mapping in the mediterranean region: A case study of the Macta Basin, Algeria. Sustainability, 15(13), Article 10388. https:\/\/doi.org\/10.3390\/su151310388","journal-title":"Sustainability"},{"key":"15085_CR7","doi-asserted-by":"publisher","unstructured":"Bounoua, L., Kahime, K., Houti, L., Blakey, T., Ebi, K. L., Zhang, P., ... & Boumezzough, A. (2013). Linking climate to incidence of zoonotic cutaneous leishmaniasis (L. major) in pre-Saharan North Africa. International Journal of Environmental Research and Public Health,10(8), 3172\u20133191. https:\/\/doi.org\/10.3390\/ijerph10083172","DOI":"10.3390\/ijerph10083172"},{"issue":"1","key":"15085_CR8","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1080\/10824669.2018.1523734","volume":"24","author":"AJ Bowers","year":"2019","unstructured":"Bowers, A. J., & Zhou, X. (2019). Receiver operating characteristic (ROC) area under the curve (AUC): A diagnostic measure for evaluating the accuracy of predictors of education outcomes. Journal of Education for Students Placed at Risk (JESPAR), 24(1), 20\u201346. https:\/\/doi.org\/10.1080\/10824669.2018.1523734","journal-title":"Journal of Education for Students Placed at Risk (JESPAR)"},{"key":"15085_CR9","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.623","volume":"7","author":"D Chicco","year":"2021","unstructured":"Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peerj Computer Science, 7, Article e623. https:\/\/doi.org\/10.7717\/peerj-cs.623","journal-title":"Peerj Computer Science"},{"key":"15085_CR10","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1007\/s00704-018-2370-3","volume":"135","author":"M Delbari","year":"2019","unstructured":"Delbari, M., Sharifazari, S., & Mohammadi, E. (2019). Modeling daily soil temperature over diverse climate conditions in Iran\u2014A comparison of multiple linear regression and support vector regression techniques. Theoretical and Applied Climatology, 135, 991\u20131001. https:\/\/doi.org\/10.1007\/s00704-018-2370-3","journal-title":"Theoretical and Applied Climatology"},{"key":"15085_CR11","doi-asserted-by":"publisher","first-page":"100453","DOI":"10.1016\/j.orhc.2024.100453","volume":"44","author":"AC Donizette","year":"2025","unstructured":"Donizette, A. C., Rocco, C. D., & de Queiroz, T. A. (2025). Predicting leishmaniasis outbreaks in Brazil using machine learning models based on disease surveillance and meteorological data. Operations Research for Health Care, 44, 100453. https:\/\/doi.org\/10.1016\/j.orhc.2024.100453","journal-title":"Operations Research for Health Care"},{"issue":"3","key":"15085_CR12","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1007\/s00484-023-02605-1","volume":"68","author":"L Fang","year":"2024","unstructured":"Fang, L., Hu, W., & Pan, G. (2024). Meteorological factors cannot be ignored in machine learning-based methods for predicting dengue, A systematic review. International Journal of Biometeorology, 68(3), 401\u2013410. https:\/\/doi.org\/10.1007\/s00484-023-02605-1","journal-title":"International Journal of Biometeorology"},{"key":"15085_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2022.155324","volume":"834","author":"M Faraji","year":"2022","unstructured":"Faraji, M., Nadi, S., Ghaffarpasand, O., Homayoni, S., & Downey, K. (2022). An integrated 3D CNN-GRU deep learning method for short-term prediction of PM2. 5 concentration in urban environment. Science of the Total Environment, 834, Article 155324. https:\/\/doi.org\/10.1016\/j.scitotenv.2022.155324","journal-title":"Science of the Total Environment"},{"issue":"1","key":"15085_CR14","doi-asserted-by":"publisher","DOI":"10.1186\/s40249-023-01103-1","volume":"12","author":"N Firouraghi","year":"2023","unstructured":"Firouraghi, N., Bergquist, R., Fatima, M., Mohammadi, A., Hamer, D. H., Shirzadi, M. R., & Kiani, B. (2023). High-risk spatiotemporal patterns of cutaneous leishmaniasis: A nationwide study in Iran from 2011 to 2020. Infectious Diseases of Poverty, 12(1), Article 49. https:\/\/doi.org\/10.1186\/s40249-023-01103-1","journal-title":"Infectious Diseases of Poverty"},{"issue":"14","key":"15085_CR15","doi-asserted-by":"publisher","first-page":"1878","DOI":"10.1017\/S003118201100148X","volume":"138","author":"AO Franco","year":"2011","unstructured":"Franco, A. O., Davies, C. R., Mylne, A., Dedet, J. P., G\u00e1llego, M., Ballart, C., & Cox, J. (2011). Predicting the distribution of canine leishmaniasis in western Europe based on environmental variables. Parasitology, 138(14), 1878\u20131891. https:\/\/doi.org\/10.1017\/S003118201100148X","journal-title":"Parasitology"},{"issue":"2","key":"15085_CR16","doi-asserted-by":"publisher","first-page":"e3940","DOI":"10.1002\/ecs2.3940","volume":"13","author":"EA Geissinger","year":"2022","unstructured":"Geissinger, E. A., Khoo, C. L., Richmond, I. C., Faulkner, S. J., & Schneider, D. C. (2022). A case for beta regression in the natural sciences. Ecosphere, 13(2), e3940. https:\/\/doi.org\/10.1002\/ecs2.3940","journal-title":"Ecosphere"},{"issue":"7","key":"15085_CR17","doi-asserted-by":"publisher","DOI":"10.3390\/land14071443","volume":"14","author":"E Ghaderpour","year":"2025","unstructured":"Ghaderpour, E., Bozzano, F., Scarascia Mugnozza, G., & Mazzanti, P. (2025). Analyses of MODIS land cover\/use and wildfires in Italian regions since 2001. Land, 14(7), Article 1443. https:\/\/doi.org\/10.3390\/land14071443","journal-title":"Land"},{"issue":"6","key":"15085_CR18","doi-asserted-by":"publisher","DOI":"10.3390\/land13060796","volume":"13","author":"E Ghaderpour","year":"2024","unstructured":"Ghaderpour, E., Mazzanti, P., Bozzano, F., & Scarascia Mugnozza, G. (2024). Trend analysis of MODIS land surface temperature and land cover in Central Italy. Land, 13(6), Article 796. https:\/\/doi.org\/10.3390\/land13060796","journal-title":"Land"},{"key":"15085_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/s00484-024-02723-4","author":"JD Guti\u00e9rrez","year":"2024","unstructured":"Guti\u00e9rrez, J. D., \u00c1vila-Jim\u00e9nez, J., & Altamiranda-Saavedra, M. (2024). Causal association between environmental variables and the excess cases of cutaneous leishmaniasis in Colombia: Are we looking to the wrong side? International Journal of Biometeorology. https:\/\/doi.org\/10.1007\/s00484-024-02723-4","journal-title":"International Journal of Biometeorology"},{"issue":"3","key":"15085_CR20","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.1007\/s41207-024-00553-9","volume":"9","author":"Y Hasnaoui","year":"2024","unstructured":"Hasnaoui, Y., Tachi, S. E., Bouguerra, H., Benmamar, S., Gilja, G., Szczepanek, R., & Yaseen, Z. M. (2024). Enhanced machine learning models development for flash flood mapping using geospatial data. Euro-Mediterranean Journal for Environmental Integration, 9(3), 1087\u20131107. https:\/\/doi.org\/10.1007\/s41207-024-00553-9","journal-title":"Euro-Mediterranean Journal for Environmental Integration"},{"key":"15085_CR21","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.actatropica.2016.11.004","volume":"166","author":"K Holakouie-Naieni","year":"2017","unstructured":"Holakouie-Naieni, K., Mostafavi, E., Boloorani, A. D., Mohebali, M., & Pakzad, R. (2017). Spatial modeling of cutaneous leishmaniasis in Iran from 1983 to 2013. Acta Tropica, 166, 67\u201373. https:\/\/doi.org\/10.1016\/j.actatropica.2016.11.004","journal-title":"Acta Tropica"},{"issue":"2","key":"15085_CR22","doi-asserted-by":"publisher","first-page":"315","DOI":"10.5958\/0974-4576.2020.00054.7","volume":"44","author":"E Jahanifard","year":"2020","unstructured":"Jahanifard, E., Hanafi-Bojd, A. A., Akhavan, A. A., Sharififard, M., Khazeni, A., & Vazirianzadeh, B. (2020). Modeling of at-risk areas of Zoonotic Cutaneous Leishmaniasis (ZCL) using Hierarchical Analysis Process (AHP) and Geographic Information System (GIS) in Southwest of Iran. Journal of Entomological Research, 44(2), 315\u2013322. https:\/\/doi.org\/10.5958\/0974-4576.2020.00054.7","journal-title":"Journal of Entomological Research"},{"key":"15085_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121490","volume":"237","author":"S Kumar","year":"2024","unstructured":"Kumar, S., Srivastava, A., & Maity, R. (2024). Modeling climate change impacts on vector-borne disease using machine learning models: Case study of Visceral leishmaniasis (Kala-azar) from Indian state of Bihar. Expert Systems with Applications, 237, Article 121490. https:\/\/doi.org\/10.1016\/j.eswa.2023.121490","journal-title":"Expert Systems with Applications"},{"key":"15085_CR24","doi-asserted-by":"publisher","unstructured":"Li, Z. L., Wu, H., Duan, S. B., Zhao, W., Ren, H., Liu, X., \u2026 Zhou, C. (2023). Satellite remote sensing of global land surface temperature: Definition, methods, products, and applications. Reviews of Geophysics, 61(1). https:\/\/doi.org\/10.1029\/2022RG000777","DOI":"10.1029\/2022RG000777"},{"issue":"3","key":"15085_CR25","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1016\/j.ejor.2023.05.009","volume":"313","author":"Z Liao","year":"2024","unstructured":"Liao, Z., Dai, S., & Kuosmanen, T. (2024). Convex support vector regression. European Journal of Operational Research, 313(3), 858\u2013870. https:\/\/doi.org\/10.1016\/j.ejor.2023.05.009","journal-title":"European Journal of Operational Research"},{"issue":"1","key":"15085_CR26","doi-asserted-by":"publisher","DOI":"10.1186\/s12942-021-00282-0","volume":"20","author":"A McMahon","year":"2021","unstructured":"McMahon, A., Mihretie, A., Ahmed, A. A., Lake, M., Awoke, W., & Wimberly, M. C. (2021). Remote sensing of environmental risk factors for malaria in different geographic contexts. International Journal of Health Geographics, 20(1), Article 28. https:\/\/doi.org\/10.1186\/s12942-021-00282-0","journal-title":"International Journal of Health Geographics"},{"issue":"1","key":"15085_CR27","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1515\/reveh-2020-0066","volume":"36","author":"A Mohammadbeigi","year":"2021","unstructured":"Mohammadbeigi, A., Khazaei, S., Heidari, H., Asgarian, A., Arsangjang, S., Saghafipour, A., & Ansari, H. (2021). An investigation of the effects of environmental and ecologic factors on cutaneous leishmaniasis in the old world: A systematic review study. Reviews on Environmental Health, 36(1), 117\u2013128. https:\/\/doi.org\/10.1515\/reveh-2020-0066","journal-title":"Reviews on Environmental Health"},{"issue":"1","key":"15085_CR28","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1111\/zph.12109","volume":"62","author":"A Mollalo","year":"2015","unstructured":"Mollalo, A., Alimohammadi, A., Shirzadi, M. R., & Malek, M. R. (2015). Geographic information system-based analysis of the spatial and spatio-temporal distribution of zoonotic cutaneous leishmaniasis in Golestan Province, north-east of Iran. Zoonoses and Public Health, 62(1), 18\u201328. https:\/\/doi.org\/10.1111\/zph.12109","journal-title":"Zoonoses and Public Health"},{"issue":"6","key":"15085_CR29","doi-asserted-by":"publisher","DOI":"10.3390\/pathogens13060462","volume":"13","author":"D Mu\u00f1oz Morales","year":"2024","unstructured":"Mu\u00f1oz Morales, D., Suarez Daza, F., Franco Betancur, O., Martinez Guevara, D., & Liscano, Y. (2024). The impact of climatological factors on the incidence of cutaneous leishmaniasis (CL) in Colombian Municipalities from 2017 to 2019. Pathogens, 13(6), Article 462. https:\/\/doi.org\/10.3390\/pathogens13060462","journal-title":"Pathogens"},{"key":"15085_CR30","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1016\/j.mfglet.2023.08.056","volume":"35","author":"M Ogunsanya","year":"2023","unstructured":"Ogunsanya, M., Isichei, J., & Desai, S. (2023). Grid search hyperparameter tuning in additive manufacturing processes. Manufacturing Letters, 35, 1031\u20131042. https:\/\/doi.org\/10.1016\/j.mfglet.2023.08.056","journal-title":"Manufacturing Letters"},{"issue":"1","key":"15085_CR31","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0006141","volume":"12","author":"P Olliaro","year":"2018","unstructured":"Olliaro, P., Grogl, M., Boni, M., Carvalho, E. M., Chebli, H., Cisse, M., & Arana, B. (2018). Harmonized clinical trial methodologies for localized cutaneous leishmaniasis and potential for extensive network with capacities for clinical evaluation. PLoS Neglected Tropical Diseases, 12(1), Article e0006141. https:\/\/doi.org\/10.1371\/journal.pntd.0006141","journal-title":"PLoS Neglected Tropical Diseases"},{"issue":"1","key":"15085_CR32","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/5672401","volume":"2023","author":"YO Ouma","year":"2023","unstructured":"Ouma, Y. O., & Omai, L. (2023). Flood susceptibility mapping using image\u2010based 2D\u2010CNN deep learning: Overview and case study application using multiparametric spatial data in data\u2010scarce urban environments. International Journal of Intelligent Systems, 2023(1), Article 5672401. https:\/\/doi.org\/10.1155\/2023\/5672401","journal-title":"International Journal of Intelligent Systems"},{"key":"15085_CR33","doi-asserted-by":"publisher","unstructured":"Parto Dezfooli, F., Valadan Zoej, M. J., Mansourian, A., Youssefi, F., & Pirasteh, S. (2024). GEE-Based Environmental Monitoring and Phenology Correlation Investigation Using Support Vector Regression. Remote Sensing Applications: Society and Environment, 101445.\u200f https:\/\/doi.org\/10.1016\/j.rsase.2024.101445","DOI":"10.1016\/j.rsase.2024.101445"},{"key":"15085_CR34","doi-asserted-by":"publisher","DOI":"10.1088\/2515-7620\/adfbbe","volume":"7","author":"F Parto Dezfooli","year":"2025","unstructured":"Parto Dezfooli, F., Valadan Zoej, M. J., Youssefi, F., & Ghaderpour, E. (2025). Integrating remote sensing, GIS, and machine learning for zoonotic cutaneous leishmaniasis modelling. Environmental Research Communications, 7, Article 085017. https:\/\/doi.org\/10.1088\/2515-7620\/adfbbe","journal-title":"Environmental Research Communications"},{"key":"15085_CR35","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1017\/S0962492900002919","volume":"8","author":"A Pinkus","year":"1999","unstructured":"Pinkus, A. (1999). Approximation theory of the MLP model in neural networks. Acta Numerica, 8, 143\u2013195. https:\/\/doi.org\/10.1017\/S0962492900002919","journal-title":"Acta Numerica"},{"issue":"10","key":"15085_CR36","doi-asserted-by":"publisher","first-page":"2001","DOI":"10.1080\/13658816.2017.1346255","volume":"31","author":"J Pohjankukka","year":"2017","unstructured":"Pohjankukka, J., Pahikkala, T., Nevalainen, P., & Heikkonen, J. (2017). Estimating the prediction performance of spatial models via spatial k-fold cross validation. International Journal of Geographical Information Science, 31(10), 2001\u20132019. https:\/\/doi.org\/10.1080\/13658816.2017.1346255","journal-title":"International Journal of Geographical Information Science"},{"key":"15085_CR37","doi-asserted-by":"publisher","first-page":"11516","DOI":"10.1007\/s11356-018-1340-8","volume":"25","author":"R Ramezankhani","year":"2018","unstructured":"Ramezankhani, R., Sajjadi, N., Nezakati esmaeilzadeh, R., Jozi, S. A., & Shirzadi, M. R. (2018). Climate and environmental factors affecting the incidence of cutaneous leishmaniasis in Isfahan, Iran. Environmental Science and Pollution Research, 25, 11516\u201311526. https:\/\/doi.org\/10.1007\/s11356-018-1340-8","journal-title":"Environmental Science and Pollution Research"},{"issue":"2","key":"15085_CR38","doi-asserted-by":"publisher","DOI":"10.1007\/s12145-025-01718-y","volume":"18","author":"S Remidi","year":"2025","unstructured":"Remidi, S., Boutaleb, A., Tachi, S. E., Hasnaoui, Y., Szczepanek, R., & Seffari, A. (2025). Ensemble machine learning model for exploration and targeting of Pb-Zn deposits in Algeria. Earth Science Informatics, 18(2), Article 226. https:\/\/doi.org\/10.1007\/s12145-025-01718-y","journal-title":"Earth Science Informatics"},{"issue":"23","key":"15085_CR39","doi-asserted-by":"publisher","first-page":"17095","DOI":"10.1007\/s00521-023-08568-z","volume":"35","author":"M Reyad","year":"2023","unstructured":"Reyad, M., Sarhan, A. M., & Arafa, M. (2023). A modified Adam algorithm for deep neural network optimization. Neural Computing and Applications, 35(23), 17095\u201317112. https:\/\/doi.org\/10.1007\/s00521-023-08568-z","journal-title":"Neural Computing and Applications"},{"issue":"3","key":"15085_CR40","doi-asserted-by":"publisher","DOI":"10.3390\/app12031679","volume":"12","author":"I Riyanto","year":"2022","unstructured":"Riyanto, I., Rizkinia, M., Arief, R., & Sudiana, D. (2022). Three-dimensional convolutional neural network on multi-temporal synthetic aperture radar images for urban flood potential mapping in Jakarta. Applied Sciences, 12(3), Article 1679. https:\/\/doi.org\/10.3390\/app12031679","journal-title":"Applied Sciences"},{"issue":"12","key":"15085_CR41","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture12122083","volume":"12","author":"M Saadat","year":"2022","unstructured":"Saadat, M., Seydi, S. T., Hasanlou, M., & Homayouni, S. (2022). A convolutional neural network method for rice mapping using time-series of Sentinel-1 and Sentinel-2 imagery. Agriculture, 12(12), Article 2083. https:\/\/doi.org\/10.3390\/agriculture12122083","journal-title":"Agriculture"},{"key":"15085_CR42","doi-asserted-by":"publisher","unstructured":"Saadene, Y., & Salhi, A. (2025). Spatio-temporal modeling of cutaneous leishmaniasis under climate change scenarios in the Maghreb Region (2021-2100). Acta Tropica. https:\/\/doi.org\/10.1016\/j.actatropica.2025.107548","DOI":"10.1016\/j.actatropica.2025.107548"},{"key":"15085_CR43","doi-asserted-by":"publisher","unstructured":"Sedgwick, P. (2012). Pearson\u2019s correlation coefficient. Bmj, 345.\u200f https:\/\/doi.org\/10.1136\/bmj.e4483","DOI":"10.1136\/bmj.e4483"},{"issue":"1","key":"15085_CR44","doi-asserted-by":"publisher","DOI":"10.3390\/rs13010078","volume":"13","author":"O Sefrin","year":"2020","unstructured":"Sefrin, O., Riese, F. M., & Keller, S. (2020). Deep learning for land cover change detection. Remote Sensing, 13(1), Article 78. https:\/\/doi.org\/10.3390\/rs13010078","journal-title":"Remote Sensing"},{"key":"15085_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2022.102854","volume":"112","author":"N Shabanpour","year":"2022","unstructured":"Shabanpour, N., Razavi-Termeh, S. V., Sadeghi-Niaraki, A., Choi, S. M., & Abuhmed, T. (2022). Integration of machine learning algorithms and GIS-based approaches to cutaneous leishmaniasis prevalence risk mapping. International Journal of Applied Earth Observation and Geoinformation, 112, Article 102854. https:\/\/doi.org\/10.1016\/j.jag.2022.102854","journal-title":"International Journal of Applied Earth Observation and Geoinformation"},{"key":"15085_CR46","doi-asserted-by":"publisher","unstructured":"Shan, S. (2023). Machine Learning Analysis of Socioeconomic Factors Affecting Global Food Prices and Security. Available at SSRN 4505965.\u200f https:\/\/doi.org\/10.2139\/ssrn.4505965","DOI":"10.2139\/ssrn.4505965"},{"key":"15085_CR47","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2023.1091709","volume":"11","author":"I Sharifi","year":"2023","unstructured":"Sharifi, I., Khosravi, A., Aflatoonian, M. R., Salarkia, E., Bamorovat, M., Karamoozian, A., & Gouya, M. M. (2023). Cutaneous leishmaniasis situation analysis in the Islamic Republic of Iran in preparation for an elimination plan. Frontiers in Public Health, 11, Article 1091709. https:\/\/doi.org\/10.3389\/fpubh.2023.1091709","journal-title":"Frontiers in Public Health"},{"issue":"11","key":"15085_CR48","doi-asserted-by":"publisher","first-page":"2877","DOI":"10.1175\/JHM-D-21-0077.1","volume":"22","author":"P Shi","year":"2021","unstructured":"Shi, P., Zeng, J., Chen, K. S., Ma, H., Bi, H., & Cui, C. (2021). The 20-year spatiotemporal trends of remotely sensed soil moisture and vegetation and their response to climate change over the third pole. Journal of Hydrometeorology, 22(11), 2877\u20132896. https:\/\/doi.org\/10.1175\/JHM-D-21-0077.1","journal-title":"Journal of Hydrometeorology"},{"key":"15085_CR49","doi-asserted-by":"publisher","unstructured":"Sinsomboonthong, J., Phaeobang, A., & Sinsomboonthong, S. (2025). Adjusted optimal trimmed theil-sen method for multiple regression model, with outliers detection and management. Contemporary Mathematics, 2939\u20132967.\u200f https:\/\/doi.org\/10.37256\/cm.6320255546","DOI":"10.37256\/cm.6320255546"},{"issue":"3","key":"15085_CR50","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1002\/wics.1198","volume":"4","author":"X Su","year":"2012","unstructured":"Su, X., Yan, X., & Tsai, C. L. (2012). Linear regression. Wiley Interdisciplinary Reviews: Computational Statistics, 4(3), 275\u2013294. https:\/\/doi.org\/10.1002\/wics.1198","journal-title":"Wiley Interdisciplinary Reviews: Computational Statistics"},{"key":"15085_CR51","doi-asserted-by":"publisher","unstructured":"Tabasi, M., & Alesheikh, A. A. (2020). Spatiotemporal variability of Zoonotic Cutaneous Leishmaniasis based on sociodemographic heterogeneity. The case of Northeastern Iran, 2011\u20132016. Japanese Journal of Infectious Diseases, JJID-2020. https:\/\/doi.org\/10.7883\/yoken.jjid.2020.048","DOI":"10.7883\/yoken.jjid.2020.048"},{"issue":"2037","key":"15085_CR52","doi-asserted-by":"publisher","first-page":"20241712","DOI":"10.1098\/rspb.2024.1712","volume":"291","author":"CS Teitelbaum","year":"2024","unstructured":"Teitelbaum, C. S., Ferraz, A., De La Cruz, S. E., Gilmour, M. E., & Brosnan, I. G. (2024). The potential of remote sensing for improved infectious disease ecology research and practice. Proceedings of the Royal Society B: Biological Sciences, 291(2037), 20241712. https:\/\/doi.org\/10.1098\/rspb.2024.1712","journal-title":"Proceedings of the Royal Society B: Biological Sciences"},{"issue":"3","key":"15085_CR53","doi-asserted-by":"publisher","first-page":"733","DOI":"10.3390\/rs14030733","volume":"14","author":"S Varela","year":"2022","unstructured":"Varela, S., Pederson, T. L., & Leakey, A. D. (2022). Implementing spatio-temporal 3D-convolution neural networks and UAV time series imagery to better predict lodging damage in sorghum. Remote Sensing, 14(3), 733. https:\/\/doi.org\/10.3390\/rs14030733","journal-title":"Remote Sensing"},{"issue":"1","key":"15085_CR54","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-024-17929-9","volume":"24","author":"S Yang","year":"2024","unstructured":"Yang, S., Li, R. Y., Yan, S. N., Yang, H. Y., Cao, Z. Y., Zhang, L., & Zheng, B. (2024). Risk assessment of imported malaria in China: A machine learning perspective. BMC Public Health, 24(1), Article 865. https:\/\/doi.org\/10.1186\/s12889-024-17929-9","journal-title":"BMC Public Health"},{"issue":"5","key":"15085_CR55","doi-asserted-by":"publisher","DOI":"10.3390\/s22051942","volume":"22","author":"F Youssefi","year":"2022","unstructured":"Youssefi, F., Zoej, M. J. V., Hanafi-Bojd, A. A., Dariane, A. B., Khaki, M., Safdarinezhad, A., & Ghaderpour, E. (2022). Temporal monitoring and predicting of the abundance of malaria vectors using time series analysis of remote sensing data through Google Earth Engine. Sensors, 22(5), Article 1942. https:\/\/doi.org\/10.3390\/s22051942","journal-title":"Sensors"},{"key":"15085_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.actatropica.2021.105861","volume":"217","author":"I Zeb","year":"2021","unstructured":"Zeb, I., Qureshi, N. A., Shaheen, N., Zafar, M. I., Ali, A., Hamid, A., & Ashraf, A. (2021). Spatiotemporal patterns of cutaneous leishmaniasis in the district upper and lower Dir, Khyber Pakhtunkhwa, Pakistan: A GIS-based spatial approaches. Acta Tropica, 217, Article 105861. https:\/\/doi.org\/10.1016\/j.actatropica.2021.105861","journal-title":"Acta Tropica"}],"container-title":["Environmental Monitoring and Assessment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10661-026-15085-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10661-026-15085-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10661-026-15085-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T08:05:04Z","timestamp":1776067504000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10661-026-15085-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,26]]},"references-count":56,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["15085"],"URL":"https:\/\/doi.org\/10.1007\/s10661-026-15085-8","relation":{},"ISSN":["1573-2959"],"issn-type":[{"value":"1573-2959","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,26]]},"assertion":[{"value":"15 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors have read, understood, and complied as applicable with the statement on \u201cEthical responsibilities of Authors\u201d as found in the Instructions for Authors. This study utilized a fully anonymized dataset of ZCL cases, excluding any personal identifiers such as names, addresses, or national identification numbers. All analyses were conducted exclusively on aggregated data, including maps and charts, without access to individual patient records. All relevant ethical standards, including the protection of data confidentiality and participant privacy, were rigorously upheld throughout the study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical trial number"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"357"}}