So I am trying to use time series analysis using E-views to forecast my data about the number of occurrence of earthquakes with magnitude greater than or equal to zero in a certain country. Attached below is my data.

year;eq
1911;1 1921;1 1931;0 1941;1 1951;0 1961;0 1971;0 1981;0 1991;1 2001;1 2011;0
1912;0 1922;0 1932;0 1942;2 1952;1 1962;0 1972;2 1982;1 1992;2 2002;1 2012;1
1913;1 1923;1 1933;0 1943;2 1953;0 1963;0 1973;1 1983;0 1993;1 2003;0 2013;1
1914;0 1924;2 1934;2 1944;0 1954;0 1964;0 1974;0 1984;1 1994;1 2004;0 2014;0
1915;0 1925;1 1935;0 1945;0 1955;1 1965;0 1975;1 1985;1 1995;2 2005;0 2015;0
1916;0 1926;0 1936;0 1946;0 1956;0 1966;0 1976;1 1986;0 1996;1 2006;0
1917;0 1927;0 1937;1 1947;1 1957;1 1967;0 1977;1 1987;0 1997;0 2007;0
1918;1 1928;1 1938;2 1948;2 1958;0 1968;1 1978;0 1988;1 1998;0 2008;0
1919;0 1929;2 1939;0 1949;1 1959;0 1969;0 1979;1 1989;1 1999;2 2009;0
1920;0 1930;0 1940;0 1950;0 1960;0 1970;2 1980;0 1990;2 2000;0 2010;3

Here is the graph of my data:

According to ADF test, my data is stationary at first differencing.

Here is the correlogram of my series:

The plots suggest an MA(1) model, right?

So here is the estimation output of the MA(1) model:

My problem is on the assumptions that need to be satisfied by the residuals of the model.The residuals of the model should be normally distributed, independent, and has a constant variance (homoscedasticity), right? My MA(1) model does not satisfy the normality assumption.

What is the consequence if the assumption of normality is not met? or What are the consequences of not meeting the assumptions for residuals of the model?

Are my procedures okay? Or should there be other methods that can be used for my data?