Muhammet Ali Peki̇n

İklim herhangi bir yerde ya da bölgede uzun zaman periyodunda hava koşullarında gözlemlenen ortalama durum olarak tanımlanmaktadır. Bir yerin matematik ve özel konumu ile ilişkili faktörlere bağlı olarak farklı iklim tipleri ve farklı iklim bölgeleri oluşabilmektedir. İklim bölgelerinin alansal yayılımının, sınırlarının ve birbirinden ayıran özelliklerinin bilinmesi, beraberinde bazı sınıflandırma yöntemlerinin de geliştirilmesini zorunlu kılmıştır. Modern iklim sınıflandırması yaklaşımlarında genetik ve ampirik yöntemler ön plana çıkmaktadır. Genetik yöntem iklim bölgesini oluşturan faktörleri (hava kütleleri) dikkate alırken, ampirik sınıflandırma ise sıcaklık ve yağış gibi meteorolojik değişkenlerin uzun süreli gözlemlerini esas alır. Türkiye’de ilk iklim sınıflandırma çalışmalarında Erinç indisi, Aydeniz indisi, De Martonne ve Thornthwaite indisi esas alınırken, daha sonraki dönemlerde Köppen ve Köppen’in farklı versiyonları olan Köppen-Geiger ve Köppen Threwartha sınıflandırma yöntemleri ön plana çıkmıştır. Karasal İklim, Akdeniz iklimi, Karadeniz İklimi ve Marmara Geçiş İklimi Türkiye’de görülen başlıca ana iklim tipleridir. Küresel ölçekte ise enlem, okyanuslara yakınlık, atmosferik sirkülasyonlar, okyanus akıntıları ve deniz seviyesinden yükseklik gibi faktörlere bağlı olarak Ekvatoral, Tropikal, Ilıman ve Arktik ana iklim bölgeleri ortaya çıkmıştır.

Klimatolojiye Giriş Serkan SABANCI İklim Sistemi İlker EROĞLU Atmosfer ve Bileşimi Baştürk KAYA Güneş Radyasyonu ve Sıcaklık Barış DURMUŞ Atmosferde Nem ve Yağış Fatma ESEN Hava Basıncı, Atmosferik Sirkülasyon ve Rüzgârlar Cemil İRDEM Hava Kütleleri, Cepheler ve Siklonlar Halil GÜNEK İklim Sınıflandırması ve İklim Tipleri Ali Ümran KÖMÜŞCÜ Muhammet Ali PEKİN İklim Değişikliği ve İklim Modelleme Barış DURMUŞ Klimatolojik Analiz için Temel İstatistik Yöntemler Ali Ümran KÖMÜŞCÜ

Air pollution has become an important problem due to its threats. Air pollutants are in complex interaction with atmosphere and environment. For this reason, it is important to study air pollution issues. In recent years, studies on prediction of air pollutants with machine learning methods have gained momentum. In this study, some air pollutants are predicted with various machine learning algorithms considering meteorological factors. In machine learning phase, a separate study is conducted with various machine learning algorithms (multilayer perceptron neural network, stochastic gradient descent, ridge regression, cross decomposition) considering temperature, relative humidity, wind, pressure and air pollutant measurements of previous hour. Consistencies of these algorithms in estimating pollutant concentrations are compared. Various statistical metrics are used to analyze the consistencies. As a result, the coefficient of determination of all algorithms are found above 0.67, considering the test section. It is found that the coefficient of determination of the multilayer perceptron neural network algorithm provides better results than other algorithms.

The application of artificial intelligence (AI) technologies is quickly expanding in water management. Additionally, the artificial neural network methodology has an advantage over traditional statistical approaches in that it does not need assumptions about the distribution of data and variables. These methods can be used if there is a large enough data collection and criteria relevant to the nature of the problem. Preprocessing data before utilizing it improves the performance of the AI model. Evaporation matters in water management, agriculture processes and soil science. It is critical to ensure proper estimation of evaporation losses for effective water resource planning and management particularly in drought-prone areas such as Adana. Artificial intelligence approaches can be applied successfully in evaporation calculation. In this research, we used the Standard scaler, power transformer, robust scaler quantile transformer (Uniform) and quantile transformer (Normal), and min-max scaler preprocessing techniques to preprocess the multilayer perceptron neural network (MLPNN). We also trained the MLPNN using unprocessed data and compared it to the results of the preprocessed model. In the setup of the model, daily temperature, pressure, wind, sunny hours, and humidity parameters covering the years 2018-2021 were presented as input to the MLPNN model. Consequently, we pinpoint that all preprocessing approaches produce better outcomes than unscaled. Although all models produced statistically high accuracy predictions according to statistical criteria, the MLPNN model established without transformation (test phase: r²: 0.93, NSE : 0.927, SMAPE: 10.77, RMSE: 1.2, MAE: 0.9) exhibited the lowest accuracy. The evaporation prediction model that was developed using the MLPNN-based standard scalar optimization algorithm exhibited the highest level of accuracy (test phase: r²: 0.978, NSE: 0.977, SMAPE: 5.93, RMSE: 0.68, MAE: 0.48). Power Transformer (test phase: r²: 0.978, NSE: 0.977, SMAPE: 5.81, RMSE: 0.67, MAE: 0.49) showed second-degree promising results. It can be concluded from these results that the estimation of meteorological variables requires the scaling and presentation of data in a uniform structure. Therefore, improving efficiency and productivity in water management or agricultural processes can be enhanced by making more accurate evaporation estimates.

Alfalfa is one of the most widely cultivated forage crops in the world. Alfalfa farming is carried out on approximately 35 million ha of land worldwide with an annual production amounting to 255 million tons. The average alfalfa cultivated area is about 637 000 ha with production of 13 million tons and yield of 2 200 kg da^-1 in Turkey. It is expected that climate change will have significantly different effects on its production and yield in future. Therefore, the aim of the study was to predict the effect of climate change on the yield of alfalfa via selected Artificial Neural Network (ANN) according to RCP4.5 and RCP8.5 climate change scenarios. In line with this, first of all the best ANN structure among 176 different ANN alternatives consisting of various input parameters, learning rates, decay and neuron numbers to predicts alfalfa yield was selected. The ANN training/test dataset used in the study were composed of the alfalfa cultivation statistics, the soil parameters and the climatological data. Alfalfa yield for years 2020-2060 and 2060-2100 in 79 provinces of Turkey are predicted by using best ANN model, according to climate change projections (HadGEM2-ES RCP4.5 and RCP8.5). The ANN was able to calculate alfalfa yield with 0.827 coefficient of determination and 0.813 Nash-Sutcliff coefficient. It is understood that the alfalfa plant can resist climate change and its yield tend to increase or decrease in regions, where there will be an increase or decrease in precipitation in the same order as result of climatic change. It is predicted that the highest yield increase will be in Artvin (6%) (a province of the Eastern Anatolia region) and the maximum yield decrease will be noted in Siirt (9%) (a province of the South eastern Anatolia region). This research may be considered as a creative prediction approach for the alfalfa yield estimation.

Forest fires are one of the natural disasters that concern all countries in the globalizing world in terms of their effects and consequences. Fires are a vital threat that causes the burning of millions of hectares of forest areas worldwide every year, causing loss of life and property. An early warning system helps people respond to dangers promptly and appropriately. In the scope of this study, the forest fires in Antalya-Manavgat (starting on 28 July 2021 and ending on 6 August 2021) analyzed using the meteorological early warning system (MEUS), which is developed by the employees of the Turkish State Meteorology Service, and the performance of the model products was assessed. Concordantly, the association between the weather conditions in the region and the forest fire was analyzed. Besides, the analysis of the model output products is also considered. By examining the synoptic models, hourly meteorological data and MEUS warnings taken two days before the forest fire selected in the Antalya-Manavgat region, the probabilities created by the meteorological variables that may be effective in the preparation of fire conditions in the region were evaluated.

The aim of this study is to examine the fluctuation in atmospheric pressure caused by the volcanic eruption of Hunga Tonga - Hunga Ha’apai volcano on January 15, 2022, at 04:14 UTC. In the study, it was predicted that the wave created by the explosion would move at a constant speed of sound and it was calculated that it would arrive in Türkiye in 13 hours and 22 minutes. According to this situation, it was calculated that the first lamb wave could come to Türkiye as of 15 January 2022 17:36 UTC. In the second stage, 1 minute pressure data of 12 Meteorological Station (Automated Weather Observation System, AWOS) in Türkiye was analyzed. In the analyzes and evaluations made, a sharp upward movement was observed in the pressure measured at 18:38 on January 15, 2022, at the Van Meteorology Station, which is the easternmost station. It was calculated that the lamb wave arrived at this station at an average speed of 303.2 m/s in 15.1 hours.

Turkey is one of the most sensitive regions to climate change. Drought events triggered by climate change occur frequently in the region and cause important environmental problems. The agricultural sector and crop yield are among the areas that are adversely affected. In this study, meteorological and hydrological drought situation and precipitation trend is studied with SPI and SPEI methods by using Nevşehir Center and Ürgüp stations. Besides, the severity and duration of long-lasting strong droughts in the region are studied. The most important negative effects of drought events are seen in the agricultural sector on crop production. In order to analyze this effect, the yield values of agricultural products grown in the region were obtained from Turkish Statistical Institute (TUIK) and standardized with Z-Score method. Afterwards, the impact of drought events on the yield of agricultural products grown in the region are examined

Partikül madde (PM) kirliliği önemli çevresel sorunlara sebep olmaktadır. PM kirliliğinin olumsuz etkileri, canlı sağlığına yönelik riskleri nedeniyle yaygın bir sorun haline gelmiştir. PM kirliliğinin tüm bu olumsuz etkileri ve atmosferdeki karmaşık etkileşimi sebebiyle, daha fazla çalışmaya konu olması önemlidir. Özellikle, PM kirliliğinin izlenmesi ve tahmin edilmesi konusunda yapılacak çalışmalar önemlidir. Son yıllarda meteorolojik faktörler göz önüne alınarak PM kirliliğinin tahmin edilmesi çalışmaları artmıştır. Özellikle makine öğrenme yöntemleri ile PM kirliliği tahmini çalışmaları hız kazanmıştır. Bu çalışmada, meteorolojik faktörler göz önüne alınarak çeşitli makine öğrenme algoritmaları ile PM10 kirliliği tahmin edilmiştir. Çalışmada kullanılan meteoroloji verileri Meteoroloji Genel Müdürlüğü Ankara Bölge istasyonundan (enlem:39,9727, boylam:32,8637, rakım:891 m.) elde edilmiştir. PM10 kirlilik verileri ise Çevre, Şehircilik ve İklim Değişikliği Bakanlığı Ankara Keçiören-Sanatoryum hava kalitesi istasyonundan (enlem: 39,999, boylam: 32,856, rakım: 1009 m.) elde edilmiştir. Makine öğrenme çalışması aşamasında, sıcaklık, çiğ noktası sıcaklığı, yağış, bağıl nem, rüzgar hızı, basınç, bulut kapalılığı ve bir önceki güne ait PM10 ölçümleri göz önüne alınarak, farklı makine öğrenme (karar ağacı regresyonu, destek vektör regresyonu, lasso regresyonu ve yapay sinir ağı) algoritmalarıyla ayrı ayrı çalışma yapılmış ve bu algoritmaların tutarlılıkları karşılaştırılmıştır. Tutarlılıklarının incelenmesi aşamasında çeşitli istatistiksel metrikler kullanılmıştır. Sonuçta, test bölümü göz önüne alındığında, yapay sinir ağı algoritmasının belirleme katsayısı ̴0,6, kök ortalama kare hatası ̴18 ve ortalama mutlak hata ̴12 olarak bulunmuş ve yapay sinir ağı algoritmasının diğer algoritmalara göre daha iyi sonuç verdiği görülmüştür.