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The Digital Footprint of Water Awareness: Google Trends and Dam Levels in Türkiye

Yıl 2026, Cilt: 38 Sayı: 1 , 105 - 120 , 29.03.2026
https://doi.org/10.35234/fumbd.1739372
https://izlik.org/JA47GF34MF

Öz

As pressures on water resources intensify globally, monitoring not only physical systems but also societal behaviors have become a critical necessity for sustainable management. This study aims to explore whether there is a temporal relationship between dam levels in Türkiye and public information-seeking behavior reflected in digital search trends. In this context, daily dam levels from ten major dams in Istanbul were aggregated into monthly averages to create a representative time series for the city. This series was then matched with monthly Google Trends data for the keyword “baraj doluluk” (dam fullness) and standardized using z-score normalization. Seasonal patterns, threshold behaviors, and lagged correlations were analyzed. The findings reveal that digital search interest exhibits statistically significant relationships with reservoir levels, particularly with a time lag of one to two months. The results suggest that user behavior on digital platforms may reflect rising public awareness in response to changes in environmental systems. Accordingly, the study offers an innovative perspective by demonstrating the potential integration of open digital data sources such as Google Trends into socio-hydraulic monitoring frameworks.

Kaynakça

  • Sivapalan M, Savenije HHG, and Blöschl G. Socio-hydrology: A new science of people and water. Hydrol. Process, 2012; 26(8): 1270-1276.
  • Ripberger JT. Capturing Curiosity: Using internet search trends to measure public attentiveness. Policy studies journal, 2011; 39(2): 239-259.
  • Preis T, Moat HS, and H. Eugene Stanley. Quantifying trading behavior in financial markets using google trends. Scientific reports, 2013; 3(1): 1684.
  • Lahkar R. Equilibrium selection in the stag hunt game under generalized reinforcement learning. Journal of Economic Behavior and Organization, 2017; 138: 63-68.
  • Thompson JJ, Wilby RL, Matthews T, and Murphy C. The utility of Google Trends as a tool for evaluating flooding in data-scarce places. Area, 2022; 54(2): 203-212.
  • Santín C, Moustakas A, and Doerr SH. Searching the flames: Trends in global and regional public interest in wildfires. Environmental Science and Policy, 2023; 146: 151-161.
  • Nghiem LT, Papworth SK, Lim FKS, and Carrasco LR. Analysis of the capacity of google trends to measure interest in conservation topics and the role of online news. PloS one, 2016; 11(3): e0152802.
  • Barros JM, Melia R, Francis K, Bogue J, O’Sullivan M, Young K, Duggan, J. The validity of google trends search volumes for behavioral forecasting of national suicide rates in Ireland.International journal of environmental research and public health, 2019; 16(17): 3201.
  • Dasandi N, Kalatzi Pantera D, Dasandi N, Jankin S, Kalatzi Pantera D, and Romanello M. Personal View Public engagement with health and climate change around the world: a Google Trends analysis. The Lancet Planetary Health, 2025; 9(3): e236-e244.
  • Brodeur A, Clark AE, Flèche S, and Powdthavee N. Assessing the impact of the coronavirus lockdown on unhappiness, loneliness, and boredom using Google Trends. arXiv preprint arXiv,220; 2004.12129.
  • Fu C and Miller C. Using Google Trends as a proxy for occupant behavior to predict building energy consumption. Applied Energy, 2020; 310: 118343.
  • Fang Q, Burger J, Meijers R, and Van Berkel K. The Role of Time, Weather and Google Trends in Understanding and Predicting Web Survey Response, . arXiv preprint arXiv,2020; 2011.02034.
  • Amangeldi D, Usmanova A, and Shamoi P. Understanding Environmental Posts: Sentiment and Emotion Analysis of Social Media Data. IEEE Access, 2025; 12: 33504-33523.
  • Phillips BB, Burgess K, Willis C, and Gaston KJ. Monitoring public engagement with nature using Google Trends. People and Nature, 2022; 4(5): 1216-1232.
  • Towler E, Lazrus H, and PaiMazumder D. Characterizing the potential for drought action from combined hydrological and societal perspectives. Hydrology and Earth System Sciences, 2019; 23(3): 1469-1482.
  • Van Loon AF, Stahl K, Di Baldassarre G, Clark J, Rangecroft S, Wanders N, ...Van Lanen HA. Drought in a human-modified world: Reframing drought definitions, understanding, and analysis approaches. Hydrology and Earth System Sciences, 2016; 20(9): 3631-3650.
  • Google, Google Trends, Google. https://trends.google.com
  • Google Trends Help, Understand and use Trends data. https://support.google.com/trends/answer/436 5533.
  • Jain AK, Duin RP, Mao J, and Member S. Statistical Pattern Recognition: A Review. IEEE Transactions on pattern analysis and machine intelligence, 2000; 22(1): 4-37.
  • Gopal S, Patro K, and Sahu KK. Normalization: A preprocessing stage. arXiv preprint arXiv:2015;1503.06462.
  • Paparrizos J and Gravano L. K-shape: Efficient and accurate clustering of time series. In Proceedings of the 2015 ACM SIGMOD international conference on management of data, 2015;1855-1870.
  • Huang J, Chen J, Huang H, and Cai X. Deep Learning-Based Daily Streamflow Prediction Model for the Hanjiang River Basin. Hydrology, 2025;12(7): 168.
  • Abu Arra A, Alashan S, and Şişman E. A new framework for innovative trend analysis: integrating extreme precipitation indices, standardization, enhanced visualization, and novel classification approaches (ITA-NF). Natural Hazards, 2025;1-33.
  • Pearson K. VII. Note on regression and inheritance in the case of two parents. proceedings of the royal society of London, 1895; 58(347-352): 240-242.
  • Lee Rodgers J and Alan Nicewander W. Thirteen Ways to Look at the Correlation Coefficient.The American Statistician, 1988; 42(1): 59-66.
  • Olguin Muñoz M, Klatzky R, Wang J, Pillai P, Satyanarayanan M, and Gross J. Impact of delayed response on wearable cognitive assistance. Plos one,2021; 16(3): e0248690.
  • Ye Y, Zhou T, Zhu Q, Vann W, and Du J. Brain functional connectivity under teleoperation latency: a fNIRS study. Frontiers in Neuroscience, 2024; 18: 1416719.
  • Wirzberger M, Schmidt R, Georgi M, Hardt W, Brunnett G, and Rey GD. Effects of system response delays on elderly humans’ cognitive performance in a virtual training scenario. Scientific Reports,2019; 9(1): 8291.
  • Altunkaynak A and Küllahcı K. Transfer precipitation learning via patterns of dependency matrix-based machine learning approaches. Neural Computing and Applications, 2022; 34(24): 22177-22196.
  • Lehmann EL. Introduction to Student (1908) The probable error of a mean. In Breakthroughs in statistics: methodology and distribution. New York, NY: Springer New York.1992; 29-32
  • Fisher RA. Statistical Methods for Research Workers.1936
  • Effenberger M, Kronbichler A, Il Shin J, Mayer G, Tilg H, and Perco P. Association of the COVID-19 pandemic with internet search volumes: a Google TrendsTM analysis. International Journal of Infectious Diseases,2020; 95:192-197.
  • Mangono T, Smittenaar P, Caplan Y, Huang VS, Sutermaster S, Kemp H, Sgaier SK. Information-seeking patterns during the COVID-19 pandemic across the United States: Longitudinal analysis of Google trends data. Journal of Medical Internet Research,2021; 23(5): e22933.
  • Slovic P. The perception of risk, Earthscan Publications, 2000.
  • Wilby RL, Murphy, P. O’Connor, J. J. Thompson, and T. Matthews. Google trends indicators to inform water planning and drought management. The Geographical Journal, 2024; 190(3): e12567.
  • Kam J, Stowers K, and Kim S. Monitoring of drought awareness from Google trends: a case study of the 2011-17 California drought. Weather, Climate, and Society, 2019;11(2): 419-429.
  • Pretorius A, Kruger E, and Bezuidenhout S. Google trends and water conservation awareness: the internet’s contribution in South Africa. South African Geographical Journal, 2022; 104(1): 53-69.
  • Knoble C, Fabolude G, Vu A, and Yu D. From crisis to prevention: mining big data for public health insights during the flint water crisis. Discover Sustainability,2024; 5(1): 289.
  • Slovic P. Perception of Risk. In The perception of risk, 2016: 220-231.
  • Granovetter M. Threshold models of collective behavior. American journal of sociology,1978; 83(6): 1420-1443.
  • Neuman WR. The Threshold of Public Attention.1990.
  • Gladwell M. The tipping point : how little things can make a big difference. Little, Brown and Co., 2006.
  • Otto IM, Donges JF, Cremades R, Bhowmik A, Hewitt RJ, Lucht W, ... Schellnhuber HJ. “Social tipping dynamics for stabilizing Earth’s climate by 2050.Public Opinion Quarterly, 1990; 54(2): 159-176.
  • Wiedermann M, Smith EK, Heitzig J, Donges JF. A network-based microfoundation of Granovetter’s threshold model for social tipping. Scientific reports,2020; 10(1): 11202.
  • Kam J, Ahmad DM. Disparity between global drought hazard and awareness. NPJ Clean Water, 2024;7(1)
  • Hâkimi O, Liu H, Abudayyeh O, Houshyar A, Almatared M, Alhawiti A. Data Fusion for Smart Civil Infrastructure Management: A Conceptual Digital Twin Framework. Buildings, 2023;13(11): 2725.

Su Farkındalığının Dijital İzi: Google Trends ve Baraj Doluluğu Türkiye Örneği

Yıl 2026, Cilt: 38 Sayı: 1 , 105 - 120 , 29.03.2026
https://doi.org/10.35234/fumbd.1739372
https://izlik.org/JA47GF34MF

Öz

Su kaynakları üzerindeki baskılar, küresel ölçekte etkisini artırırken, yalnızca fiziksel sistemlerin değil, toplumsal davranışların da izlenmesi sürdürülebilir yönetim açısından kritik bir gereklilik hâline gelmiştir. Bu çalışma, Türkiye’deki baraj doluluk oranları ile dijital bilgi arama davranışları arasında zamansal bir ilişki olup olmadığını ortaya koymayı amaçlamaktadır. Çalışmada İstanbul’daki 10 baraja ait günlük doluluk verileri aylık ortalamalar üzerinden birleştirilerek, kenti temsil eden bir baraj doluluk serisi elde edilmiştir. Bu veri seti, “baraj doluluk” anahtar kelimesine yönelik Google Trends verileriyle eşleştirilmiş ve z-normalizasyon yöntemiyle ölçeklendirilmiştir. Elde edilen veriler üzerinden mevsimsel eğilimler, eşik davranışları ve zaman gecikmeli korelasyonlar analiz edilmiştir. Bulgular, dijital arama eğilimlerinin baraj doluluk oranlarıyla özellikle 1–2 aylık gecikmeyle anlamlı ilişkiler taşıdığını ortaya koymuştur. Sonuçlar dijital platformlardaki kullanıcı davranışlarının, çevresel sistemlerdeki değişimlere karşı gelişen toplumsal farkındalığın bir yansıması olabileceğini göstermektedir. Bu bağlamda çalışma, açık dijital veri kaynaklarının sosyo-hidrolik izleme süreçlerine entegre edilebileceğini göstererek literatüre yenilikçi bir katkı sunmaktadır.

Kaynakça

  • Sivapalan M, Savenije HHG, and Blöschl G. Socio-hydrology: A new science of people and water. Hydrol. Process, 2012; 26(8): 1270-1276.
  • Ripberger JT. Capturing Curiosity: Using internet search trends to measure public attentiveness. Policy studies journal, 2011; 39(2): 239-259.
  • Preis T, Moat HS, and H. Eugene Stanley. Quantifying trading behavior in financial markets using google trends. Scientific reports, 2013; 3(1): 1684.
  • Lahkar R. Equilibrium selection in the stag hunt game under generalized reinforcement learning. Journal of Economic Behavior and Organization, 2017; 138: 63-68.
  • Thompson JJ, Wilby RL, Matthews T, and Murphy C. The utility of Google Trends as a tool for evaluating flooding in data-scarce places. Area, 2022; 54(2): 203-212.
  • Santín C, Moustakas A, and Doerr SH. Searching the flames: Trends in global and regional public interest in wildfires. Environmental Science and Policy, 2023; 146: 151-161.
  • Nghiem LT, Papworth SK, Lim FKS, and Carrasco LR. Analysis of the capacity of google trends to measure interest in conservation topics and the role of online news. PloS one, 2016; 11(3): e0152802.
  • Barros JM, Melia R, Francis K, Bogue J, O’Sullivan M, Young K, Duggan, J. The validity of google trends search volumes for behavioral forecasting of national suicide rates in Ireland.International journal of environmental research and public health, 2019; 16(17): 3201.
  • Dasandi N, Kalatzi Pantera D, Dasandi N, Jankin S, Kalatzi Pantera D, and Romanello M. Personal View Public engagement with health and climate change around the world: a Google Trends analysis. The Lancet Planetary Health, 2025; 9(3): e236-e244.
  • Brodeur A, Clark AE, Flèche S, and Powdthavee N. Assessing the impact of the coronavirus lockdown on unhappiness, loneliness, and boredom using Google Trends. arXiv preprint arXiv,220; 2004.12129.
  • Fu C and Miller C. Using Google Trends as a proxy for occupant behavior to predict building energy consumption. Applied Energy, 2020; 310: 118343.
  • Fang Q, Burger J, Meijers R, and Van Berkel K. The Role of Time, Weather and Google Trends in Understanding and Predicting Web Survey Response, . arXiv preprint arXiv,2020; 2011.02034.
  • Amangeldi D, Usmanova A, and Shamoi P. Understanding Environmental Posts: Sentiment and Emotion Analysis of Social Media Data. IEEE Access, 2025; 12: 33504-33523.
  • Phillips BB, Burgess K, Willis C, and Gaston KJ. Monitoring public engagement with nature using Google Trends. People and Nature, 2022; 4(5): 1216-1232.
  • Towler E, Lazrus H, and PaiMazumder D. Characterizing the potential for drought action from combined hydrological and societal perspectives. Hydrology and Earth System Sciences, 2019; 23(3): 1469-1482.
  • Van Loon AF, Stahl K, Di Baldassarre G, Clark J, Rangecroft S, Wanders N, ...Van Lanen HA. Drought in a human-modified world: Reframing drought definitions, understanding, and analysis approaches. Hydrology and Earth System Sciences, 2016; 20(9): 3631-3650.
  • Google, Google Trends, Google. https://trends.google.com
  • Google Trends Help, Understand and use Trends data. https://support.google.com/trends/answer/436 5533.
  • Jain AK, Duin RP, Mao J, and Member S. Statistical Pattern Recognition: A Review. IEEE Transactions on pattern analysis and machine intelligence, 2000; 22(1): 4-37.
  • Gopal S, Patro K, and Sahu KK. Normalization: A preprocessing stage. arXiv preprint arXiv:2015;1503.06462.
  • Paparrizos J and Gravano L. K-shape: Efficient and accurate clustering of time series. In Proceedings of the 2015 ACM SIGMOD international conference on management of data, 2015;1855-1870.
  • Huang J, Chen J, Huang H, and Cai X. Deep Learning-Based Daily Streamflow Prediction Model for the Hanjiang River Basin. Hydrology, 2025;12(7): 168.
  • Abu Arra A, Alashan S, and Şişman E. A new framework for innovative trend analysis: integrating extreme precipitation indices, standardization, enhanced visualization, and novel classification approaches (ITA-NF). Natural Hazards, 2025;1-33.
  • Pearson K. VII. Note on regression and inheritance in the case of two parents. proceedings of the royal society of London, 1895; 58(347-352): 240-242.
  • Lee Rodgers J and Alan Nicewander W. Thirteen Ways to Look at the Correlation Coefficient.The American Statistician, 1988; 42(1): 59-66.
  • Olguin Muñoz M, Klatzky R, Wang J, Pillai P, Satyanarayanan M, and Gross J. Impact of delayed response on wearable cognitive assistance. Plos one,2021; 16(3): e0248690.
  • Ye Y, Zhou T, Zhu Q, Vann W, and Du J. Brain functional connectivity under teleoperation latency: a fNIRS study. Frontiers in Neuroscience, 2024; 18: 1416719.
  • Wirzberger M, Schmidt R, Georgi M, Hardt W, Brunnett G, and Rey GD. Effects of system response delays on elderly humans’ cognitive performance in a virtual training scenario. Scientific Reports,2019; 9(1): 8291.
  • Altunkaynak A and Küllahcı K. Transfer precipitation learning via patterns of dependency matrix-based machine learning approaches. Neural Computing and Applications, 2022; 34(24): 22177-22196.
  • Lehmann EL. Introduction to Student (1908) The probable error of a mean. In Breakthroughs in statistics: methodology and distribution. New York, NY: Springer New York.1992; 29-32
  • Fisher RA. Statistical Methods for Research Workers.1936
  • Effenberger M, Kronbichler A, Il Shin J, Mayer G, Tilg H, and Perco P. Association of the COVID-19 pandemic with internet search volumes: a Google TrendsTM analysis. International Journal of Infectious Diseases,2020; 95:192-197.
  • Mangono T, Smittenaar P, Caplan Y, Huang VS, Sutermaster S, Kemp H, Sgaier SK. Information-seeking patterns during the COVID-19 pandemic across the United States: Longitudinal analysis of Google trends data. Journal of Medical Internet Research,2021; 23(5): e22933.
  • Slovic P. The perception of risk, Earthscan Publications, 2000.
  • Wilby RL, Murphy, P. O’Connor, J. J. Thompson, and T. Matthews. Google trends indicators to inform water planning and drought management. The Geographical Journal, 2024; 190(3): e12567.
  • Kam J, Stowers K, and Kim S. Monitoring of drought awareness from Google trends: a case study of the 2011-17 California drought. Weather, Climate, and Society, 2019;11(2): 419-429.
  • Pretorius A, Kruger E, and Bezuidenhout S. Google trends and water conservation awareness: the internet’s contribution in South Africa. South African Geographical Journal, 2022; 104(1): 53-69.
  • Knoble C, Fabolude G, Vu A, and Yu D. From crisis to prevention: mining big data for public health insights during the flint water crisis. Discover Sustainability,2024; 5(1): 289.
  • Slovic P. Perception of Risk. In The perception of risk, 2016: 220-231.
  • Granovetter M. Threshold models of collective behavior. American journal of sociology,1978; 83(6): 1420-1443.
  • Neuman WR. The Threshold of Public Attention.1990.
  • Gladwell M. The tipping point : how little things can make a big difference. Little, Brown and Co., 2006.
  • Otto IM, Donges JF, Cremades R, Bhowmik A, Hewitt RJ, Lucht W, ... Schellnhuber HJ. “Social tipping dynamics for stabilizing Earth’s climate by 2050.Public Opinion Quarterly, 1990; 54(2): 159-176.
  • Wiedermann M, Smith EK, Heitzig J, Donges JF. A network-based microfoundation of Granovetter’s threshold model for social tipping. Scientific reports,2020; 10(1): 11202.
  • Kam J, Ahmad DM. Disparity between global drought hazard and awareness. NPJ Clean Water, 2024;7(1)
  • Hâkimi O, Liu H, Abudayyeh O, Houshyar A, Almatared M, Alhawiti A. Data Fusion for Smart Civil Infrastructure Management: A Conceptual Digital Twin Framework. Buildings, 2023;13(11): 2725.
Toplam 46 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Su Kaynakları Mühendisliği, Su Kaynakları ve Su Yapıları
Bölüm Araştırma Makalesi
Yazarlar

Kübra Küllahcı 0000-0003-4699-5878

Gönderilme Tarihi 10 Temmuz 2025
Kabul Tarihi 29 Eylül 2025
Yayımlanma Tarihi 29 Mart 2026
DOI https://doi.org/10.35234/fumbd.1739372
IZ https://izlik.org/JA47GF34MF
Yayımlandığı Sayı Yıl 2026 Cilt: 38 Sayı: 1

Kaynak Göster

APA Küllahcı, K. (2026). Su Farkındalığının Dijital İzi: Google Trends ve Baraj Doluluğu Türkiye Örneği. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 38(1), 105-120. https://doi.org/10.35234/fumbd.1739372
AMA 1.Küllahcı K. Su Farkındalığının Dijital İzi: Google Trends ve Baraj Doluluğu Türkiye Örneği. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2026;38(1):105-120. doi:10.35234/fumbd.1739372
Chicago Küllahcı, Kübra. 2026. “Su Farkındalığının Dijital İzi: Google Trends ve Baraj Doluluğu Türkiye Örneği”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 38 (1): 105-20. https://doi.org/10.35234/fumbd.1739372.
EndNote Küllahcı K (01 Mart 2026) Su Farkındalığının Dijital İzi: Google Trends ve Baraj Doluluğu Türkiye Örneği. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 38 1 105–120.
IEEE [1]K. Küllahcı, “Su Farkındalığının Dijital İzi: Google Trends ve Baraj Doluluğu Türkiye Örneği”, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, c. 38, sy 1, ss. 105–120, Mar. 2026, doi: 10.35234/fumbd.1739372.
ISNAD Küllahcı, Kübra. “Su Farkındalığının Dijital İzi: Google Trends ve Baraj Doluluğu Türkiye Örneği”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 38/1 (01 Mart 2026): 105-120. https://doi.org/10.35234/fumbd.1739372.
JAMA 1.Küllahcı K. Su Farkındalığının Dijital İzi: Google Trends ve Baraj Doluluğu Türkiye Örneği. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2026;38:105–120.
MLA Küllahcı, Kübra. “Su Farkındalığının Dijital İzi: Google Trends ve Baraj Doluluğu Türkiye Örneği”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, c. 38, sy 1, Mart 2026, ss. 105-20, doi:10.35234/fumbd.1739372.
Vancouver 1.Kübra Küllahcı. Su Farkındalığının Dijital İzi: Google Trends ve Baraj Doluluğu Türkiye Örneği. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 01 Mart 2026;38(1):105-20. doi:10.35234/fumbd.1739372